Use of the AIC with the EM algorithm: A demonstration of a probability model selection technique
Energy Technology Data Exchange (ETDEWEB)
Glosup, J.G.; Axelrod M.C. [Lawrence Livermore National Lab., CA (United States)
1994-11-15
The problem of discriminating between two potential probability models, a Gaussian distribution and a mixture of Gaussian distributions, is considered. The focus of our interest is a case where the models are potentially non-nested and the parameters of the mixture model are estimated through the EM algorithm. The AIC, which is frequently used as a criterion for discriminating between non-nested models, is modified to work with the EM algorithm and is shown to provide a model selection tool for this situation. A particular problem involving an infinite mixture distribution known as Middleton`s Class A model is used to demonstrate the effectiveness and limitations of this method.
Automatic Algorithm Selection for Complex Simulation Problems
Ewald, Roland
2012-01-01
To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments without demanding expert knowledge on simulation. Roland Ewald analyzes and discusses existing approaches to solve the algorithm selection problem in the context of simulation. He introduces a framework for automatic simulation algorithm selection and
Portfolio selection using genetic algorithms | Yahaya | International ...
African Journals Online (AJOL)
In this paper, one of the nature-inspired evolutionary algorithms – a Genetic Algorithms (GA) was used in solving the portfolio selection problem (PSP). Based on a real dataset from a popular stock market, the performance of the algorithm in relation to those obtained from one of the popular quadratic programming (QP) ...
Selection of views to materialize using simulated annealing algorithms
Zhou, Lijuan; Liu, Chi; Wang, Hongfeng; Liu, Daixin
2002-03-01
A data warehouse contains lots of materialized views over the data provided by the distributed heterogeneous databases for the purpose of efficiently implementing decision-support or OLAP queries. It is important to select the right view to materialize that answer a given set of queries. The goal is the minimization of the combination of the query evaluation and view maintenance costs. In this paper, we have addressed and designed algorithms for selecting a set of views to be materialized so that the sum of processing a set of queries and maintaining the materialized views is minimized. We develop an approach using simulated annealing algorithms to solve it. First, we explore simulated annealing algorithms to optimize the selection of materialized views. Then we use experiments to demonstrate our approach. The results show that our algorithm works better. We implemented our algorithms and a performance study of the algorithms shows that the proposed algorithm gives an optimal solution.
Template Generation and Selection Algorithms
Guo, Y.; Smit, Gerardus Johannes Maria; Broersma, Haitze J.; Heysters, P.M.; Badaway, W.; Ismail, Y.
The availability of high-level design entry tooling is crucial for the viability of any reconfigurable SoC architecture. This paper presents a template generation method to extract functional equivalent structures, i.e. templates, from a control data flow graph. By inspecting the graph the algorithm
A Fast Algorithm of Cartographic Sounding Selection
Institute of Scientific and Technical Information of China (English)
SUI Haigang; HUA Li; ZHAO Haitao; ZHANG Yongli
2005-01-01
An effective strategy and framework that adequately integrate the automated and manual processes for fast cartographic sounding selection is presented. The important submarine topographic features are extracted for important soundings selection, and an improved "influence circle" algorithm is introduced for sounding selection. For automatic configuration of soundings distribution pattern, a special algorithm considering multi-factors is employed. A semi-automatic method for solving the ambiguous conflicts is described. On the basis of the algorithms and strategies a system named HGIS for fast cartographic sounding selection is developed and applied in Chinese Marine Safety Administration Bureau (CMSAB). The application experiments show that the system is effective and reliable. At last some conclusions and the future work are given.
Tag SNP selection via a genetic algorithm.
Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh
2010-10-01
Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.
Signal filtering algorithm for depth-selective diffuse optical topography
International Nuclear Information System (INIS)
Fujii, M; Nakayama, K
2009-01-01
A compact filtered backprojection algorithm that suppresses the undesirable effects of skin circulation for near-infrared diffuse optical topography is proposed. Our approach centers around a depth-selective filtering algorithm that uses an inverse problem technique and extracts target signals from observation data contaminated by noise from a shallow region. The filtering algorithm is reduced to a compact matrix and is therefore easily incorporated into a real-time system. To demonstrate the validity of this method, we developed a demonstration prototype for depth-selective diffuse optical topography and performed both computer simulations and phantom experiments. The results show that the proposed method significantly suppresses the noise from the shallow region with a minimal degradation of the target signal.
Ad Hoc Access Gateway Selection Algorithm
Jie, Liu
With the continuous development of mobile communication technology, Ad Hoc access network has become a hot research, Ad Hoc access network nodes can be used to expand capacity of multi-hop communication range of mobile communication system, even business adjacent to the community, improve edge data rates. For mobile nodes in Ad Hoc network to internet, internet communications in the peer nodes must be achieved through the gateway. Therefore, the key Ad Hoc Access Networks will focus on the discovery gateway, as well as gateway selection in the case of multi-gateway and handover problems between different gateways. This paper considers the mobile node and the gateway, based on the average number of hops from an average access time and the stability of routes, improved gateway selection algorithm were proposed. An improved gateway selection algorithm, which mainly considers the algorithm can improve the access time of Ad Hoc nodes and the continuity of communication between the gateways, were proposed. This can improve the quality of communication across the network.
An evolutionary algorithm for model selection
Energy Technology Data Exchange (ETDEWEB)
Bicker, Karl [CERN, Geneva (Switzerland); Chung, Suh-Urk; Friedrich, Jan; Grube, Boris; Haas, Florian; Ketzer, Bernhard; Neubert, Sebastian; Paul, Stephan; Ryabchikov, Dimitry [Technische Univ. Muenchen (Germany)
2013-07-01
When performing partial-wave analyses of multi-body final states, the choice of the fit model, i.e. the set of waves to be used in the fit, can significantly alter the results of the partial wave fit. Traditionally, the models were chosen based on physical arguments and by observing the changes in log-likelihood of the fits. To reduce possible bias in the model selection process, an evolutionary algorithm was developed based on a Bayesian goodness-of-fit criterion which takes into account the model complexity. Starting from systematically constructed pools of waves which contain significantly more waves than the typical fit model, the algorithm yields a model with an optimal log-likelihood and with a number of partial waves which is appropriate for the number of events in the data. Partial waves with small contributions to the total intensity are penalized and likely to be dropped during the selection process, as are models were excessive correlations between single waves occur. Due to the automated nature of the model selection, a much larger part of the model space can be explored than would be possible in a manual selection. In addition the method allows to assess the dependence of the fit result on the fit model which is an important contribution to the systematic uncertainty.
Gene selection heuristic algorithm for nutrigenomics studies.
Valour, D; Hue, I; Grimard, B; Valour, B
2013-07-15
Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in "R." Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.
A redundancy-removing feature selection algorithm for nominal data
Directory of Open Access Journals (Sweden)
Zhihua Li
2015-10-01
Full Text Available No order correlation or similarity metric exists in nominal data, and there will always be more redundancy in a nominal dataset, which means that an efficient mutual information-based nominal-data feature selection method is relatively difficult to find. In this paper, a nominal-data feature selection method based on mutual information without data transformation, called the redundancy-removing more relevance less redundancy algorithm, is proposed. By forming several new information-related definitions and the corresponding computational methods, the proposed method can compute the information-related amount of nominal data directly. Furthermore, by creating a new evaluation function that considers both the relevance and the redundancy globally, the new feature selection method can evaluate the importance of each nominal-data feature. Although the presented feature selection method takes commonly used MIFS-like forms, it is capable of handling high-dimensional datasets without expensive computations. We perform extensive experimental comparisons of the proposed algorithm and other methods using three benchmarking nominal datasets with two different classifiers. The experimental results demonstrate the average advantage of the presented algorithm over the well-known NMIFS algorithm in terms of the feature selection and classification accuracy, which indicates that the proposed method has a promising performance.
A Cancer Gene Selection Algorithm Based on the K-S Test and CFS
Directory of Open Access Journals (Sweden)
Qiang Su
2017-01-01
Full Text Available Background. To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S test and correlation-based feature selection (CFS principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. Results. We adopted support vector machines (SVM as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR, and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. Conclusions. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms.
Improving permafrost distribution modelling using feature selection algorithms
Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail
2016-04-01
overall operation. It operates by constructing a large collection of decorrelated classification trees, and then predicts the permafrost occurrence through a majority vote. With the so-called out-of-bag (OOB) error estimate, the classification of permafrost data can be validated as well as the contribution of each predictor can be assessed. The performances of compared permafrost distribution models (computed on independent testing sets) increased with the application of FS algorithms on the original dataset and irrelevant or redundant variables were removed. As a consequence, the process provided faster and more cost-effective predictors and a better understanding of the underlying structures residing in permafrost data. Our work demonstrates the usefulness of a feature selection step prior to applying a machine learning algorithm. In fact, permafrost predictors could be ranked not only based on their heuristic and subjective importance (expert knowledge), but also based on their statistical relevance in relation of the permafrost distribution.
Demonstration of essentiality of entanglement in a Deutsch-like quantum algorithm
Huang, He-Liang; Goswami, Ashutosh K.; Bao, Wan-Su; Panigrahi, Prasanta K.
2018-06-01
Quantum algorithms can be used to efficiently solve certain classically intractable problems by exploiting quantum parallelism. However, the effectiveness of quantum entanglement in quantum computing remains a question of debate. This study presents a new quantum algorithm that shows entanglement could provide advantages over both classical algorithms and quantum algo- rithms without entanglement. Experiments are implemented to demonstrate the proposed algorithm using superconducting qubits. Results show the viability of the algorithm and suggest that entanglement is essential in obtaining quantum speedup for certain problems in quantum computing. The study provides reliable and clear guidance for developing useful quantum algorithms.
ADAPTIVE SELECTION OF AUXILIARY OBJECTIVES IN MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS
Directory of Open Access Journals (Sweden)
I. A. Petrova
2016-05-01
Full Text Available Subject of Research.We propose to modify the EA+RL method, which increases efficiency of evolutionary algorithms by means of auxiliary objectives. The proposed modification is compared to the existing objective selection methods on the example of travelling salesman problem. Method. In the EA+RL method a reinforcement learning algorithm is used to select an objective – the target objective or one of the auxiliary objectives – at each iteration of the single-objective evolutionary algorithm.The proposed modification of the EA+RL method adopts this approach for the usage with a multiobjective evolutionary algorithm. As opposed to theEA+RL method, in this modification one of the auxiliary objectives is selected by reinforcement learning and optimized together with the target objective at each step of the multiobjective evolutionary algorithm. Main Results.The proposed modification of the EA+RL method was compared to the existing objective selection methods on the example of travelling salesman problem. In the EA+RL method and its proposed modification reinforcement learning algorithms for stationary and non-stationary environment were used. The proposed modification of the EA+RL method applied with reinforcement learning for non-stationary environment outperformed the considered objective selection algorithms on the most problem instances. Practical Significance. The proposed approach increases efficiency of evolutionary algorithms, which may be used for solving discrete NP-hard optimization problems. They are, in particular, combinatorial path search problems and scheduling problems.
Naive Bayes-Guided Bat Algorithm for Feature Selection
Directory of Open Access Journals (Sweden)
Ahmed Majid Taha
2013-01-01
Full Text Available When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.
Naive Bayes-Guided Bat Algorithm for Feature Selection
Taha, Ahmed Majid; Mustapha, Aida; Chen, Soong-Der
2013-01-01
When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets. PMID:24396295
An algorithm for preferential selection of spectroscopic targets in LEGUE
International Nuclear Information System (INIS)
Carlin, Jeffrey L.; Newberg, Heidi Jo; Lépine, Sébastien; Deng Licai; Chen Yuqin; Fu Xiaoting; Gao Shuang; Li Jing; Liu Chao; Beers, Timothy C.; Christlieb, Norbert; Grillmair, Carl J.; Guhathakurta, Puragra; Han Zhanwen; Hou Jinliang; Lee, Hsu-Tai; Liu Xiaowei; Pan Kaike; Sellwood, J. A.; Wang Hongchi
2012-01-01
We describe a general target selection algorithm that is applicable to any survey in which the number of available candidates is much larger than the number of objects to be observed. This routine aims to achieve a balance between a smoothly-varying, well-understood selection function and the desire to preferentially select certain types of targets. Some target-selection examples are shown that illustrate different possibilities of emphasis functions. Although it is generally applicable, the algorithm was developed specifically for the LAMOST Experiment for Galactic Understanding and Exploration (LEGUE) survey that will be carried out using the Chinese Guo Shou Jing Telescope. In particular, this algorithm was designed for the portion of LEGUE targeting the Galactic halo, in which we attempt to balance a variety of science goals that require stars at fainter magnitudes than can be completely sampled by LAMOST. This algorithm has been implemented for the halo portion of the LAMOST pilot survey, which began in October 2011.
Enhancement of Selection, Bubble and Insertion Sorting Algorithm
Muhammad Farooq Umar; Ehsan Ullah Munir; Shafqat Ali Shad; Muhammad Wasif Nisar
2014-01-01
In everyday life there is a large amount of data to arrange because sorting removes any ambiguities and make the data analysis and data processing very easy, efficient and provides with cost less effort. In this study a set of improved sorting algorithms are proposed which gives better performance and design idea. In this study five new sorting algorithms (Bi-directional Selection Sort, Bi-directional bubble sort, MIDBiDirectional Selection Sort, MIDBidirectional bubble sort and linear insert...
Selection method of terrain matching area for TERCOM algorithm
Zhang, Qieqie; Zhao, Long
2017-10-01
The performance of terrain aided navigation is closely related to the selection of terrain matching area. The different matching algorithms have different adaptability to terrain. This paper mainly studies the adaptability to terrain of TERCOM algorithm, analyze the relation between terrain feature and terrain characteristic parameters by qualitative and quantitative methods, and then research the relation between matching probability and terrain characteristic parameters by the Monte Carlo method. After that, we propose a selection method of terrain matching area for TERCOM algorithm, and verify the method correctness with real terrain data by simulation experiment. Experimental results show that the matching area obtained by the method in this paper has the good navigation performance and the matching probability of TERCOM algorithm is great than 90%
Selection of individual features of a speech signal using genetic algorithms
Directory of Open Access Journals (Sweden)
Kamil Kamiński
2016-03-01
Full Text Available The paper presents an automatic speaker’s recognition system, implemented in the Matlab environment, and demonstrates how to achieve and optimize various elements of the system. The main emphasis was put on features selection of a speech signal using a genetic algorithm which takes into account synergy of features. The results of optimization of selected elements of a classifier have been also shown, including the number of Gaussian distributions used to model each of the voices. In addition, for creating voice models, a universal voice model has been used.[b]Keywords[/b]: biometrics, automatic speaker recognition, genetic algorithms, feature selection
Selective epidemic vaccination under the performant routing algorithms
Bamaarouf, O.; Alweimine, A. Ould Baba; Rachadi, A.; EZ-Zahraouy, H.
2018-04-01
Despite the extensive research on traffic dynamics and epidemic spreading, the effect of the routing algorithms strategies on the traffic-driven epidemic spreading has not received an adequate attention. It is well known that more performant routing algorithm strategies are used to overcome the congestion problem. However, our main result shows unexpectedly that these algorithms favor the virus spreading more than the case where the shortest path based algorithm is used. In this work, we studied the virus spreading in a complex network using the efficient path and the global dynamic routing algorithms as compared to shortest path strategy. Some previous studies have tried to modify the routing rules to limit the virus spreading, but at the expense of reducing the traffic transport efficiency. This work proposed a solution to overcome this drawback by using a selective vaccination procedure instead of a random vaccination used often in the literature. We found that the selective vaccination succeeded in eradicating the virus better than a pure random intervention for the performant routing algorithm strategies.
Algorithms for selecting informative marker panels for population assignment.
Rosenberg, Noah A
2005-11-01
Given a set of potential source populations, genotypes of an individual of unknown origin at a collection of markers can be used to predict the correct source population of the individual. For improved efficiency, informative markers can be chosen from a larger set of markers to maximize the accuracy of this prediction. However, selecting the loci that are individually most informative does not necessarily produce the optimal panel. Here, using genotypes from eight species--carp, cat, chicken, dog, fly, grayling, human, and maize--this univariate accumulation procedure is compared to new multivariate "greedy" and "maximin" algorithms for choosing marker panels. The procedures generally suggest similar panels, although the greedy method often recommends inclusion of loci that are not chosen by the other algorithms. In seven of the eight species, when applied to five or more markers, all methods achieve at least 94% assignment accuracy on simulated individuals, with one species--dog--producing this level of accuracy with only three markers, and the eighth species--human--requiring approximately 13-16 markers. The new algorithms produce substantial improvements over use of randomly selected markers; where differences among the methods are noticeable, the greedy algorithm leads to slightly higher probabilities of correct assignment. Although none of the approaches necessarily chooses the panel with optimal performance, the algorithms all likely select panels with performance near enough to the maximum that they all are suitable for practical use.
Xu, Z N
2014-12-01
In this study, an error analysis is performed to study real water drop images and the corresponding numerically generated water drop profiles for three widely used static contact angle algorithms: the circle- and ellipse-fitting algorithms and the axisymmetric drop shape analysis-profile (ADSA-P) algorithm. The results demonstrate the accuracy of the numerically generated drop profiles based on the Laplace equation. A significant number of water drop profiles with different volumes, contact angles, and noise levels are generated, and the influences of the three factors on the accuracies of the three algorithms are systematically investigated. The results reveal that the above-mentioned three algorithms are complementary. In fact, the circle- and ellipse-fitting algorithms show low errors and are highly resistant to noise for water drops with small/medium volumes and contact angles, while for water drop with large volumes and contact angles just the ADSA-P algorithm can meet accuracy requirement. However, this algorithm introduces significant errors in the case of small volumes and contact angles because of its high sensitivity to noise. The critical water drop volumes of the circle- and ellipse-fitting algorithms corresponding to a certain contact angle error are obtained through a significant amount of computation. To improve the precision of the static contact angle measurement, a more accurate algorithm based on a combination of the three algorithms is proposed. Following a systematic investigation, the algorithm selection rule is described in detail, while maintaining the advantages of the three algorithms and overcoming their deficiencies. In general, static contact angles over the entire hydrophobicity range can be accurately evaluated using the proposed algorithm. The ease of erroneous judgment in static contact angle measurements is avoided. The proposed algorithm is validated by a static contact angle evaluation of real and numerically generated water drop
Feature Selection Criteria for Real Time EKF-SLAM Algorithm
Directory of Open Access Journals (Sweden)
Fernando Auat Cheein
2010-02-01
Full Text Available This paper presents a seletion procedure for environmet features for the correction stage of a SLAM (Simultaneous Localization and Mapping algorithm based on an Extended Kalman Filter (EKF. This approach decreases the computational time of the correction stage which allows for real and constant-time implementations of the SLAM. The selection procedure consists in chosing the features the SLAM system state covariance is more sensible to. The entire system is implemented on a mobile robot equipped with a range sensor laser. The features extracted from the environment correspond to lines and corners. Experimental results of the real time SLAM algorithm and an analysis of the processing-time consumed by the SLAM with the feature selection procedure proposed are shown. A comparison between the feature selection approach proposed and the classical sequential EKF-SLAM along with an entropy feature selection approach is also performed.
Development of Base Transceiver Station Selection Algorithm for ...
African Journals Online (AJOL)
TEMS) equipment was carried out on the existing BTSs, and a linear algorithm optimization program based on the spectral link efficiency of each BTS was developed, the output of this site optimization gives the selected number of base station sites ...
A numeric comparison of variable selection algorithms for supervised learning
International Nuclear Information System (INIS)
Palombo, G.; Narsky, I.
2009-01-01
Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.
A Genetic Algorithm-based Antenna Selection Approach for Large-but-Finite MIMO Networks
Makki, Behrooz
2016-12-29
We study the performance of antenna selectionbased multiple-input-multiple-output (MIMO) networks with large but finite number of transmit antennas and receivers. Considering the continuous and bursty communication scenarios with different users’ data request probabilities, we develop an efficient antenna selection scheme using genetic algorithms (GA). As demonstrated, the proposed algorithm is generic in the sense that it can be used in the cases with different objective functions, precoding methods, levels of available channel state information and channel models. Our results show that the proposed GAbased algorithm reaches (almost) the same throughput as the exhaustive search-based optimal approach, with substantially less implementation complexity.
A Genetic Algorithm-based Antenna Selection Approach for Large-but-Finite MIMO Networks
Makki, Behrooz; Ide, Anatole; Svensson, Tommy; Eriksson, Thomas; Alouini, Mohamed-Slim
2016-01-01
We study the performance of antenna selectionbased multiple-input-multiple-output (MIMO) networks with large but finite number of transmit antennas and receivers. Considering the continuous and bursty communication scenarios with different users’ data request probabilities, we develop an efficient antenna selection scheme using genetic algorithms (GA). As demonstrated, the proposed algorithm is generic in the sense that it can be used in the cases with different objective functions, precoding methods, levels of available channel state information and channel models. Our results show that the proposed GAbased algorithm reaches (almost) the same throughput as the exhaustive search-based optimal approach, with substantially less implementation complexity.
Effective traffic features selection algorithm for cyber-attacks samples
Li, Yihong; Liu, Fangzheng; Du, Zhenyu
2018-05-01
By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.
Core Business Selection Based on Ant Colony Clustering Algorithm
Directory of Open Access Journals (Sweden)
Yu Lan
2014-01-01
Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.
Naturally selecting solutions: the use of genetic algorithms in bioinformatics.
Manning, Timmy; Sleator, Roy D; Walsh, Paul
2013-01-01
For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.
Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration
Ganguly, Sangram; Nemani, Ramakrishna R.; Zhang, Gong; Hashimoto, Hirofumi; Milesi, Cristina; Michaelis, Andrew; Wang, Weile; Votava, Petr; Samanta, Arindam; Melton, Forrest;
2012-01-01
This paper summarizes the implementation of a physically based algorithm for the retrieval of vegetation green Leaf Area Index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsatobserved surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites. ©
A review of channel selection algorithms for EEG signal processing
Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq
2015-12-01
Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.
Toward optimal feature selection using ranking methods and classification algorithms
Directory of Open Access Journals (Sweden)
Novaković Jasmina
2011-01-01
Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.
Local anesthesia selection algorithm in patients with concomitant somatic diseases.
Anisimova, E N; Sokhov, S T; Letunova, N Y; Orekhova, I V; Gromovik, M V; Erilin, E A; Ryazantsev, N A
2016-01-01
The paper presents basic principles of local anesthesia selection in patients with concomitant somatic diseases. These principles are history taking; analysis of drugs interaction with local anesthetic and sedation agents; determination of the functional status of the patient; patient anxiety correction; dental care with monitoring of hemodynamics parameters. It was found that adhering to this algorithm promotes prevention of urgent conditions in patients in outpatient dentistry.
Optimal Parameter Selection of Power System Stabilizer using Genetic Algorithm
Energy Technology Data Exchange (ETDEWEB)
Chung, Hyeng Hwan; Chung, Dong Il; Chung, Mun Kyu [Dong-AUniversity (Korea); Wang, Yong Peel [Canterbury Univeristy (New Zealand)
1999-06-01
In this paper, it is suggested that the selection method of optimal parameter of power system stabilizer (PSS) with robustness in low frequency oscillation for power system using real variable elitism genetic algorithm (RVEGA). The optimal parameters were selected in the case of power system stabilizer with one lead compensator, and two lead compensator. Also, the frequency responses characteristics of PSS, the system eigenvalues criterion and the dynamic characteristics were considered in the normal load and the heavy load, which proved usefulness of RVEGA compare with Yu's compensator design theory. (author). 20 refs., 15 figs., 8 tabs.
The Research and Application of SURF Algorithm Based on Feature Point Selection Algorithm
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Zhang Fang Hu
2014-04-01
Full Text Available As the pixel information of depth image is derived from the distance information, when implementing SURF algorithm with KINECT sensor for static sign language recognition, there can be some mismatched pairs in palm area. This paper proposes a feature point selection algorithm, by filtering the SURF feature points step by step based on the number of feature points within adaptive radius r and the distance between the two points, it not only greatly improves the recognition rate, but also ensures the robustness under the environmental factors, such as skin color, illumination intensity, complex background, angle and scale changes. The experiment results show that the improved SURF algorithm can effectively improve the recognition rate, has a good robustness.
Combinatorial Optimization in Project Selection Using Genetic Algorithm
Dewi, Sari; Sawaluddin
2018-01-01
This paper discusses the problem of project selection in the presence of two objective functions that maximize profit and minimize cost and the existence of some limitations is limited resources availability and time available so that there is need allocation of resources in each project. These resources are human resources, machine resources, raw material resources. This is treated as a consideration to not exceed the budget that has been determined. So that can be formulated mathematics for objective function (multi-objective) with boundaries that fulfilled. To assist the project selection process, a multi-objective combinatorial optimization approach is used to obtain an optimal solution for the selection of the right project. It then described a multi-objective method of genetic algorithm as one method of multi-objective combinatorial optimization approach to simplify the project selection process in a large scope.
An Improved Nested Sampling Algorithm for Model Selection and Assessment
Zeng, X.; Ye, M.; Wu, J.; WANG, D.
2017-12-01
Multimodel strategy is a general approach for treating model structure uncertainty in recent researches. The unknown groundwater system is represented by several plausible conceptual models. Each alternative conceptual model is attached with a weight which represents the possibility of this model. In Bayesian framework, the posterior model weight is computed as the product of model prior weight and marginal likelihood (or termed as model evidence). As a result, estimating marginal likelihoods is crucial for reliable model selection and assessment in multimodel analysis. Nested sampling estimator (NSE) is a new proposed algorithm for marginal likelihood estimation. The implementation of NSE comprises searching the parameters' space from low likelihood area to high likelihood area gradually, and this evolution is finished iteratively via local sampling procedure. Thus, the efficiency of NSE is dominated by the strength of local sampling procedure. Currently, Metropolis-Hasting (M-H) algorithm and its variants are often used for local sampling in NSE. However, M-H is not an efficient sampling algorithm for high-dimensional or complex likelihood function. For improving the performance of NSE, it could be feasible to integrate more efficient and elaborated sampling algorithm - DREAMzs into the local sampling. In addition, in order to overcome the computation burden problem of large quantity of repeating model executions in marginal likelihood estimation, an adaptive sparse grid stochastic collocation method is used to build the surrogates for original groundwater model.
Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm
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Peng Li
2016-01-01
Full Text Available The optimal performance of the ant colony algorithm (ACA mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA, considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA and a particle swarm optimization (PSO, and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.
Variable selection in Logistic regression model with genetic algorithm.
Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi
2018-02-01
Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.
Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim
2014-01-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using
An artificial bee colony algorithm for uncertain portfolio selection.
Chen, Wei
2014-01-01
Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.
Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm
Abbas, Ahmed; Kong, Xin-Bing; Liu, Zhi; Jing, Bing-Yi; Gao, Xin
2013-01-01
A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into p-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013
Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm
Abbas, Ahmed
2013-01-07
A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into p-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013
Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm
Salameh Shreem, Salam; Abdullah, Salwani; Nazri, Mohd Zakree Ahmad
2016-04-01
Microarray technology can be used as an efficient diagnostic system to recognise diseases such as tumours or to discriminate between different types of cancers in normal tissues. This technology has received increasing attention from the bioinformatics community because of its potential in designing powerful decision-making tools for cancer diagnosis. However, the presence of thousands or tens of thousands of genes affects the predictive accuracy of this technology from the perspective of classification. Thus, a key issue in microarray data is identifying or selecting the smallest possible set of genes from the input data that can achieve good predictive accuracy for classification. In this work, we propose a two-stage selection algorithm for gene selection problems in microarray data-sets called the symmetrical uncertainty filter and harmony search algorithm wrapper (SU-HSA). Experimental results show that the SU-HSA is better than HSA in isolation for all data-sets in terms of the accuracy and achieves a lower number of genes on 6 out of 10 instances. Furthermore, the comparison with state-of-the-art methods shows that our proposed approach is able to obtain 5 (out of 10) new best results in terms of the number of selected genes and competitive results in terms of the classification accuracy.
ALGORITHM OF SELECTION EFFECTIVE SOLUTIONS FOR REPROFILING OF INDUSTRIAL BUILDINGS
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MENEJLJUK A. I.
2016-08-01
Full Text Available Raising of problem.Non-compliance requirements of today's industrial enterprises, which were built during the Soviet period, as well as significant technical progress, economic reform and transition to market principles of performance evaluation leading to necessity to change their target and functionality. The technical condition of many industrial buildings in Ukraine allows to exploit them for decades.Redesigning manufacturing enterprises allows not only to reduce the cost of construction, but also to obtain new facilities in the city. Despite the large number of industrial buildings that have lost their effectiveness and relevance, as well as a significant investor interest in these objects, the scope of redevelopment in the construction remains unexplored. Analysis researches on the topic. The problem of reconstruction of industrial buildings considered in Topchy D. [3], Travin V. [9], as well as in the work of other scientists. However, there are no rules in regulatory documents and system studies for improving the organization of the reconstruction of buildings at realigning. The purpose of this work is the development an algorithm of actions for selection of effective organizational decisions at the planning stage of a reprofiling project of industrial buildings. The proposed algorithm allows you to select an effective organizational and technological solution for the re-profiling of industrial buildings, taking into account features of the building, its location, its state of structures and existing restrictions. The most effective organizational solution allows realize the reprofiling project of an industrial building in the most possible short terms and with the lowest possible use of material resources, taking into account the available features and restrictions. Conclusion. Each object has a number of unique features that necessary for considering at choosing an effective reprofiling variant. The developed algorithm for selecting
Directory of Open Access Journals (Sweden)
Masoumeh Soflaei
2014-01-01
Full Text Available One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA and the family of selective partial update APA (SPU-APA which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE of SR-APA and SPU-APA decrease by 5.8 (dB and 5.5 (dB, respectively, in comparison with least mean square (LMS algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm.
The production route selection algorithm in virtual manufacturing networks
Krenczyk, D.; Skolud, B.; Olender, M.
2017-08-01
The increasing requirements and competition in the global market are challenges for the companies profitability in production and supply chain management. This situation became the basis for construction of virtual organizations, which are created in response to temporary needs. The problem of the production flow planning in virtual manufacturing networks is considered. In the paper the algorithm of the production route selection from the set of admissible routes, which meets the technology and resource requirements and in the context of the criterion of minimum cost is proposed.
A Demonstration of Regression False Positive Selection in Data Mining
Pinder, Jonathan P.
2014-01-01
Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…
Feature selection using genetic algorithms for fetal heart rate analysis
International Nuclear Information System (INIS)
Xu, Liang; Redman, Christopher W G; Georgieva, Antoniya; Payne, Stephen J
2014-01-01
The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. (paper)
Natural selection and algorithmic design of mRNA.
Cohen, Barry; Skiena, Steven
2003-01-01
Messenger RNA (mRNA) sequences serve as templates for proteins according to the triplet code, in which each of the 4(3) = 64 different codons (sequences of three consecutive nucleotide bases) in RNA either terminate transcription or map to one of the 20 different amino acids (or residues) which build up proteins. Because there are more codons than residues, there is inherent redundancy in the coding. Certain residues (e.g., tryptophan) have only a single corresponding codon, while other residues (e.g., arginine) have as many as six corresponding codons. This freedom implies that the number of possible RNA sequences coding for a given protein grows exponentially in the length of the protein. Thus nature has wide latitude to select among mRNA sequences which are informationally equivalent, but structurally and energetically divergent. In this paper, we explore how nature takes advantage of this freedom and how to algorithmically design structures more energetically favorable than have been built through natural selection. In particular: (1) Natural Selection--we perform the first large-scale computational experiment comparing the stability of mRNA sequences from a variety of organisms to random synonymous sequences which respect the codon preferences of the organism. This experiment was conducted on over 27,000 sequences from 34 microbial species with 36 genomic structures. We provide evidence that in all genomic structures highly stable sequences are disproportionately abundant, and in 19 of 36 cases highly unstable sequences are disproportionately abundant. This suggests that the stability of mRNA sequences is subject to natural selection. (2) Artificial Selection--motivated by these biological results, we examine the algorithmic problem of designing the most stable and unstable mRNA sequences which code for a target protein. We give a polynomial-time dynamic programming solution to the most stable sequence problem (MSSP), which is asymptotically no more complex
Demonstration of two-qubit algorithms with a superconducting quantum processor.
DiCarlo, L; Chow, J M; Gambetta, J M; Bishop, Lev S; Johnson, B R; Schuster, D I; Majer, J; Blais, A; Frunzio, L; Girvin, S M; Schoelkopf, R J
2009-07-09
Quantum computers, which harness the superposition and entanglement of physical states, could outperform their classical counterparts in solving problems with technological impact-such as factoring large numbers and searching databases. A quantum processor executes algorithms by applying a programmable sequence of gates to an initialized register of qubits, which coherently evolves into a final state containing the result of the computation. Building a quantum processor is challenging because of the need to meet simultaneously requirements that are in conflict: state preparation, long coherence times, universal gate operations and qubit readout. Processors based on a few qubits have been demonstrated using nuclear magnetic resonance, cold ion trap and optical systems, but a solid-state realization has remained an outstanding challenge. Here we demonstrate a two-qubit superconducting processor and the implementation of the Grover search and Deutsch-Jozsa quantum algorithms. We use a two-qubit interaction, tunable in strength by two orders of magnitude on nanosecond timescales, which is mediated by a cavity bus in a circuit quantum electrodynamics architecture. This interaction allows the generation of highly entangled states with concurrence up to 94 per cent. Although this processor constitutes an important step in quantum computing with integrated circuits, continuing efforts to increase qubit coherence times, gate performance and register size will be required to fulfil the promise of a scalable technology.
Low-level waste disposal site selection demonstration
International Nuclear Information System (INIS)
Rogers, V.C.
1984-01-01
This paper discusses the results of recent studies undertaken at EPRI related to low-level waste disposal technology. The initial work provided an overview of the state of the art including an assessment of its influence upon transportation costs and waste form requirements. The paper discusses work done on the overall system design aspects and computer modeling of disposal site performance characteristics. The results of this analysis are presented and provide a relative ranking of the importance of disposal parameters. This allows trade-off evaluations to be made of factors important in the design of a shallow land burial facility. To help minimize the impact of a shortage of low-level radioactive waste disposal sites, EPRI is closely observing the development of bellweather projects for developing new sites. The purpose of this activity is to provide information about lessons learned in those projects in order to expedite the development of additional disposal facilities. This paper describes most of the major stems in selecting a low-level radioactive waste disposal site in Texas. It shows how the Texas Low-Level Radioactive Waste Disposal Authority started with a wide range of potential siting areas in Texas and narrowed its attention down to a few preferred sites. The parameters used to discriminate between large areas of Texas and, eventually, 50 candidate disposal sites are described, along with the steps in the process. The Texas process is compared to those described in DOE and EPRI handbooks on site selection and to pertinent NRC requirements. The paper also describes how an inventory of low-level waste specific to Texas was developed and applied in preliminary performance assessments of two candidate sites. Finally, generic closure requirements and closure operations for low-level waste facilities in arid regions are given
Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie
2015-12-01
The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.
Directory of Open Access Journals (Sweden)
Wei Yue
2015-01-01
Full Text Available The major issues for mean-variance-skewness models are the errors in estimations that cause corner solutions and low diversity in the portfolio. In this paper, a multiobjective fuzzy portfolio selection model with transaction cost and liquidity is proposed to maintain the diversity of portfolio. In addition, we have designed a multiobjective evolutionary algorithm based on decomposition of the objective space to maintain the diversity of obtained solutions. The algorithm is used to obtain a set of Pareto-optimal portfolios with good diversity and convergence. To demonstrate the effectiveness of the proposed model and algorithm, the performance of the proposed algorithm is compared with the classic MOEA/D and NSGA-II through some numerical examples based on the data of the Shanghai Stock Exchange Market. Simulation results show that our proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms and the proposed model can maintain quite well the diversity of portfolio. The purpose of this paper is to deal with portfolio problems in the weighted possibilistic mean-variance-skewness (MVS and possibilistic mean-variance-skewness-entropy (MVS-E frameworks with transaction cost and liquidity and to provide different Pareto-optimal investment strategies as diversified as possible for investors at a time, rather than one strategy for investors at a time.
Determination of Selection Method in Genetic Algorithm for Land Suitability
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Irfianti Asti Dwi
2016-01-01
Full Text Available Genetic Algoirthm is one alternative solution in the field of modeling optimization, automatic programming and machine learning. The purpose of the study was to compare some type of selection methods in Genetic Algorithm for land suitability. Contribution of this research applies the best method to develop region based horticultural commodities. This testing is done by comparing the three methods on the method of selection, the Roulette Wheel, Tournament Selection and Stochastic Universal Sampling. Parameters of the locations used in the test scenarios include Temperature = 27°C, Rainfall = 1200 mm, hummidity = 30%, Cluster fruit = 4, Crossover Probabiitiy (Pc = 0.6, Mutation Probabilty (Pm = 0.2 and Epoch = 10. The second test epoch incluides location parameters consist of Temperature = 30°C, Rainfall = 2000 mm, Humidity = 35%, Cluster fruit = 5, Crossover Probability (Pc = 0.7, Mutation Probability (Pm = 0.3 and Epoch 10. The conclusion of this study shows that the Roulette Wheel is the best method because it produces more stable and fitness value than the other two methods.
Dynamics of the evolution of learning algorithms by selection
International Nuclear Information System (INIS)
Neirotti, Juan Pablo; Caticha, Nestor
2003-01-01
We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results
Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad
2014-10-01
Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.
An Improved User Selection Algorithm in Multiuser MIMO Broadcast with Channel Prediction
Min, Zhi; Ohtsuki, Tomoaki
In multiuser MIMO-BC (Multiple-Input Multiple-Output Broadcasting) systems, user selection is important to achieve multiuser diversity. The optimal user selection algorithm is to try all the combinations of users to find the user group that can achieve the multiuser diversity. Unfortunately, the high calculation cost of the optimal algorithm prevents its implementation. Thus, instead of the optimal algorithm, some suboptimal user selection algorithms were proposed based on semiorthogonality of user channel vectors. The purpose of this paper is to achieve multiuser diversity with a small amount of calculation. For this purpose, we propose a user selection algorithm that can improve the orthogonality of a selected user group. We also apply a channel prediction technique to a MIMO-BC system to get more accurate channel information at the transmitter. Simulation results show that the channel prediction can improve the accuracy of channel information for user selections, and the proposed user selection algorithm achieves higher sum rate capacity than the SUS (Semiorthogonal User Selection) algorithm. Also we discuss the setting of the algorithm threshold. As the result of a discussion on the calculation complexity, which uses the number of complex multiplications as the parameter, the proposed algorithm is shown to have a calculation complexity almost equal to that of the SUS algorithm, and they are much lower than that of the optimal user selection algorithm.
Selected event reconstruction algorithms for the CBM experiment at FAIR
International Nuclear Information System (INIS)
Lebedev, Semen; Höhne, Claudia; Lebedev, Andrey; Ososkov, Gennady
2014-01-01
Development of fast and efficient event reconstruction algorithms is an important and challenging task in the Compressed Baryonic Matter (CBM) experiment at the future FAIR facility. The event reconstruction algorithms have to process terabytes of input data produced in particle collisions. In this contribution, several event reconstruction algorithms are presented. Optimization of the algorithms in the following CBM detectors are discussed: Ring Imaging Cherenkov (RICH) detector, Transition Radiation Detectors (TRD) and Muon Chamber (MUCH). The ring reconstruction algorithm in the RICH is discussed. In TRD and MUCH track reconstruction algorithms are based on track following and Kalman Filter methods. All algorithms were significantly optimized to achieve maximum speed up and minimum memory consumption. Obtained results showed that a significant speed up factor for all algorithms was achieved and the reconstruction efficiency stays at high level.
Ultra-fast fluence optimization for beam angle selection algorithms
Bangert, M.; Ziegenhein, P.; Oelfke, U.
2014-03-01
Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ~ 4 s. BAS runs including FOs for 1000 different beam ensembles take ~ 15-70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.
Ultra-fast fluence optimization for beam angle selection algorithms
International Nuclear Information System (INIS)
Bangert, M; Ziegenhein, P; Oelfke, U
2014-01-01
Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ∼ 4 s. BAS runs including FOs for 1000 different beam ensembles take ∼ 15–70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.
EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal
Chen, Yong; Wu, Chun-ting; Liu, Huan-lin
2017-07-01
Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.
Synchronous Firefly Algorithm for Cluster Head Selection in WSN
Directory of Open Access Journals (Sweden)
Madhusudhanan Baskaran
2015-01-01
Full Text Available 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.
Route Selection with Unspecified Sites Using Knowledge Based Genetic Algorithm
Kanoh, Hitoshi; Nakamura, Nobuaki; Nakamura, Tomohiro
This paper addresses the problem of selecting a route to a given destination that traverses several non-specific sites (e.g. a bank, a gas station) as requested by a driver. The proposed solution uses a genetic algorithm that includes viral infection. The method is to generate two populations of viruses as domain specific knowledge in addition to a population of routes. A part of an arterial road is regarded as a main virus, and a road that includes a site is regarded as a site virus. An infection occurs between two points common to a candidate route and the virus, and involves the substitution of the intersections carried by the virus for those on the existing candidate route. Crossover and infection determine the easiest-to-drive and quasi-shortest route through the objective landmarks. Experiments using actual road maps show that this infection-based mechanism is an effective way of solving the problem. Our strategy is general, and can be effectively used in other optimization problems.
International Nuclear Information System (INIS)
Thompson, M.P.; Sessions, J.; Hamann, J.D.
2009-01-01
Genetic algorithms (GAs) have demonstrated success in solving spatial forest planning problems. We present an adaptive GA that incorporates population-level statistics to dynamically update penalty functions, a process analogous to strategic oscillation from the tabu search literature. We also explore performance of various selection strategies. The GA identified feasible solutions within 96%, 98%, and 93% of a non spatial relaxed upper bound calculated for landscapes of 100, 500, and 1000 units, respectively. The problem solved includes forest structure constraints limiting harvest opening sizes and requiring minimally sized patches of mature forest. Results suggest that the dynamic penalty strategy is superior to the more standard static penalty implementation. Results also suggest that tournament selection can be superior to the more standard implementation of proportional selection for smaller problems, but becomes susceptible to premature convergence as problem size increases. It is therefore important to balance selection pressure with appropriate disruption. We conclude that integrating intelligent search strategies into the context of genetic algorithms can yield improvements and should be investigated for future use in spatial planning with ecological goals.
A study of metaheuristic algorithms for high dimensional feature selection on microarray data
Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna
2017-11-01
Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.
National Aeronautics and Space Administration — In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used...
Selecting a general-purpose data compression algorithm
Mathews, Gary Jason
1995-01-01
The National Space Science Data Center's Common Data Formate (CDF) is capable of storing many types of data such as scalar data items, vectors, and multidimensional arrays of bytes, integers, or floating point values. However, regardless of the dimensionality and data type, the data break down into a sequence of bytes that can be fed into a data compression function to reduce the amount of data without losing data integrity and thus remaining fully reconstructible. Because of the diversity of data types and high performance speed requirements, a general-purpose, fast, simple data compression algorithm is required to incorporate data compression into CDF. The questions to ask are how to evaluate and compare compression algorithms, and what compression algorithm meets all requirements. The object of this paper is to address these questions and determine the most appropriate compression algorithm to use within the CDF data management package that would be applicable to other software packages with similar data compression needs.
Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A
2015-06-01
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.
A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems
Thammano, Arit; Teekeng, Wannaporn
2015-05-01
The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.
Hunter, H. E.
1972-01-01
The Avco Data Analysis and Prediction Techniques (ADAPT) were employed to determine laws capable of detecting failures in a heat plant up to three days in advance of the occurrence of the failure. The projected performance of algorithms yielded a detection probability of 90% with false alarm rates of the order of 1 per year for a sample rate of 1 per day with each detection, followed by 3 hourly samplings. This performance was verified on 173 independent test cases. The program also demonstrated diagnostic algorithms and the ability to predict the time of failure to approximately plus or minus 8 hours up to three days in advance of the failure. The ADAPT programs produce simple algorithms which have a unique possibility of a relatively low cost updating procedure. The algorithms were implemented on general purpose computers at Kennedy Space Flight Center and tested against current data.
The selection and implementation of hidden line algorithms
International Nuclear Information System (INIS)
Schneider, A.
1983-06-01
One of the most challenging problems in the field of computer graphics is the elimination of hidden lines in images of nontransparent bodies. In the real world the nontransparent material hinders the light ray coming from hidden regions to the observer. In the computer based image formation process there is no automatic visibility regulation of this kind. So many lines are created which result in a poor quality of the spacial representation. Therefore a three-dimensional representation on the screen is only meaningfull if the hidden lines are eliminated. For this process many algorithms have been developed in the past. A common feature of these codes is the large amount of computer time needed. In the first generation of algorithms, which are commonly used today, the bodies are modeled by plane polygons. More recently, however, also algorithms are in use, which are able to treat curved surfaces without discretisation by plane surfaces. In this paper the first group of algorithms is reviewed, and the most important codes are described. The experience obtained during the implementation of two algorithms is presented. (orig.) [de
Allen, Cheryl L.
1991-01-01
Enhanced engineering tools can be obtained through the integration of expert system methodologies and existing design software. The application of these methodologies to the spacecraft design and cost model (SDCM) software provides an improved technique for the selection of hardware for unmanned spacecraft subsystem design. The knowledge engineering system (KES) expert system development tool was used to implement a smarter equipment section algorithm than that which is currently achievable through the use of a standard data base system. The guidance, navigation, and control subsystems of the SDCM software was chosen as the initial subsystem for implementation. The portions of the SDCM code which compute the selection criteria and constraints remain intact, and the expert system equipment selection algorithm is embedded within this existing code. The architecture of this new methodology is described and its implementation is reported. The project background and a brief overview of the expert system is described, and once the details of the design are characterized, an example of its implementation is demonstrated.
Directory of Open Access Journals (Sweden)
Kuan-Cheng Lin
2015-01-01
Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.
Lee, HyungJune; Kim, HyunSeok; Chang, Ik Joon
2014-01-01
We propose a technique to optimize the energy efficiency of data collection in sensor networks by exploiting a selective data compression. To achieve such an aim, we need to make optimal decisions regarding two aspects: (1) which sensor nodes should execute compression; and (2) which compression algorithm should be used by the selected sensor nodes. We formulate this problem into binary integer programs, which provide an energy-optimal solution under the given latency constraint. Our simulation results show that the optimization algorithm significantly reduces the overall network-wide energy consumption for data collection. In the environment having a stationary sink from stationary sensor nodes, the optimized data collection shows 47% energy savings compared to the state-of-the-art collection protocol (CTP). More importantly, we demonstrate that our optimized data collection provides the best performance in an intermittent network under high interference. In such networks, we found that the selective compression for frequent packet retransmissions saves up to 55% energy compared to the best known protocol. PMID:24721763
Guo, Yanhui; Jiang, Shuang-Quan; Sun, Baiqing; Siuly, Siuly; Şengür, Abdulkadir; Tian, Jia-Wei
2017-12-01
Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images. Firstly, a thyroid ultrasound image is mapped into neutrosophic set, and an indeterminacy filter is constructed to reduce the indeterminacy of the spatial and intensity information in the image. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Then the anatomic structure is identified in thyroid ultrasound image. Finally the rendering colors on these anatomic regions are extracted and validated to find the frames which satisfy the selection criteria. To test the performance of the proposed method, a thyroid elastogram dataset is built and totally 33 cases were collected. An experienced radiologist manually evaluates the selection results of the proposed method. Experimental results demonstrate that the proposed method finds the qualified rendering frame with 100% accuracy. The proposed scheme assists the radiologists to diagnose the thyroid diseases using the qualified rendering images.
Directory of Open Access Journals (Sweden)
Fuqing Zhao
2016-01-01
Full Text Available A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.
International Nuclear Information System (INIS)
Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim
2014-01-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems
Energy Technology Data Exchange (ETDEWEB)
Elsheikh, Ahmed H., E-mail: aelsheikh@ices.utexas.edu [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom); Wheeler, Mary F. [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Hoteit, Ibrahim [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
Al-Rajab, Murad; Lu, Joan; Xu, Qiang
2017-07-01
This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.
Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm
International Nuclear Information System (INIS)
Rao, R.V.; More, K.C.
2017-01-01
Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.
The mixing evolutionary algorithm : indepedent selection and allocation of trials
C.H.M. van Kemenade
1997-01-01
textabstractWhen using an evolutionary algorithm to solve a problem involving building blocks we have to grow the building blocks and then mix these building blocks to obtain the (optimal) solution. Finding a good balance between the growing and the mixing process is a prerequisite to get a reliable
Kazmier, Kelli; Alexander, Nathan S.; Meiler, Jens; Mchaourab, Hassane S.
2010-01-01
A hybrid protein structure determination approach combining sparse Electron Paramagnetic Resonance (EPR) distance restraints and Rosetta de novo protein folding has been previously demonstrated to yield high quality models (Alexander et al., 2008). However, widespread application of this methodology to proteins of unknown structures is hindered by the lack of a general strategy to place spin label pairs in the primary sequence. In this work, we report the development of an algorithm that optimally selects spin labeling positions for the purpose of distance measurements by EPR. For the α-helical subdomain of T4 lysozyme (T4L), simulated restraints that maximize sequence separation between the two spin labels while simultaneously ensuring pairwise connectivity of secondary structure elements yielded vastly improved models by Rosetta folding. 50% of all these models have the correct fold compared to only 21% and 8% correctly folded models when randomly placed restraints or no restraints are used, respectively. Moreover, the improvements in model quality require a limited number of optimized restraints, the number of which is determined by the pairwise connectivities of T4L α-helices. The predicted improvement in Rosetta model quality was verified by experimental determination of distances between spin labels pairs selected by the algorithm. Overall, our results reinforce the rationale for the combined use of sparse EPR distance restraints and de novo folding. By alleviating the experimental bottleneck associated with restraint selection, this algorithm sets the stage for extending computational structure determination to larger, traditionally elusive protein topologies of critical structural and biochemical importance. PMID:21074624
Energy Technology Data Exchange (ETDEWEB)
Rusz, Ján, E-mail: jan.rusz@fysik.uu.se
2017-06-15
Highlights: • New algorithm for calculating double differential scattering cross-section. • Shown good convergence properties. • Outperforms older MATS algorithm, particularly in zone axis calculations. - Abstract: We present a new algorithm for calculating inelastic scattering cross-section for fast electrons. Compared to the previous Modified Automatic Term Selection (MATS) algorithm (Rusz et al. [18]), it has far better convergence properties in zone axis calculations and it allows to identify contributions of individual atoms. One can think of it as a blend of MATS algorithm and a method described by Weickenmeier and Kohl [10].
A Two-Pass Exact Algorithm for Selection on Parallel Disk Systems.
Mi, Tian; Rajasekaran, Sanguthevar
2013-07-01
Numerous OLAP queries process selection operations of "top N", median, "top 5%", in data warehousing applications. Selection is a well-studied problem that has numerous applications in the management of data and databases since, typically, any complex data query can be reduced to a series of basic operations such as sorting and selection. The parallel selection has also become an important fundamental operation, especially after parallel databases were introduced. In this paper, we present a deterministic algorithm Recursive Sampling Selection (RSS) to solve the exact out-of-core selection problem, which we show needs no more than (2 + ε ) passes ( ε being a very small fraction). We have compared our RSS algorithm with two other algorithms in the literature, namely, the Deterministic Sampling Selection and QuickSelect on the Parallel Disks Systems. Our analysis shows that DSS is a (2 + ε )-pass algorithm when the total number of input elements N is a polynomial in the memory size M (i.e., N = M c for some constant c ). While, our proposed algorithm RSS runs in (2 + ε ) passes without any assumptions. Experimental results indicate that both RSS and DSS outperform QuickSelect on the Parallel Disks Systems. Especially, the proposed algorithm RSS is more scalable and robust to handle big data when the input size is far greater than the core memory size, including the case of N ≫ M c .
Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control
Rogers, James L.
2004-01-01
The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.
Platt, R D; Griggs, R A
1993-08-01
In four experiments with 760 subjects, the present study examined Cosmides' Darwinian algorithm theory of reasoning: specifically, its explanation of facilitation on the Wason selection task. The first experiment replicated Cosmides' finding of facilitation for social contract versions of the selection task, using both her multiple-problem format and a single-problem format. Experiment 2 examined performance on Cosmides' three main social contract problems while manipulating the perspective of the subject and the presence and absence of cost-benefit information. The presence of cost-benefit information improved performance in two of the three problems while the perspective manipulation had no effect. In Experiment 3, the cost-benefit effect was replicated; and performance on one of the three problems was enhanced by the presence of explicit negatives on the NOT-P and NOT-Q cards. Experiment 4 examined the role of the deontic term "must" in the facilitation observed for two of the social contract problems. The presence of "must" led to a significant improvement in performance. The results of these experiments are strongly supportive of social contract theory in that cost-benefit information is necessary for substantial facilitation to be observed in Cosmides' problems. These findings also suggest the presence of other cues that can help guide subjects to a deontic social contract interpretation when the social contract nature of the problem is not clear.
Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm
International Nuclear Information System (INIS)
Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza
2015-01-01
Highlights: • Presenting a Morlet wavelet neural network for wind power forecasting. • Proposing improved Clonal selection algorithm for training the model. • Applying Maximum Correntropy Criterion to evaluate the training performance. • Extensive testing of the proposed wind power forecast method on real-world data. - Abstract: With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach
Indian Academy of Sciences (India)
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns
Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng
2009-11-01
Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.
2018-03-22
BASING SCIENCE AND TECHNOLOGY OBJECTIVE – DEMONSTRATION; SELECTED TECHNOLOGY ASSESSMENT by Gregg J. Gildea Paul D. Carpenter Benjamin J...Campbell William F. Harris* Michael A. McCluskey** and José A. Miletti*** *General Dynamics Information Technology Fairfax, VA 22030 **Maneuver...SCIENCE AND TECHNOLOGY OBJECTIVE – DEMONSTRATION; SELECTED TECHNOLOGY ASSESSMENT 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT
2015-12-24
manufacturing today (namely, the 14nm FinFET silicon CMOS technology). The JPEG algorithm is selected as a motivational example since it is widely...TIFF images of a U.S. Air Force F-16 aircraft provided by the University of Southern California Signal and Image Processing Institute (SIPI) image...silicon CMOS technology currently in high volume manufac- turing today (the 14 nm FinFET silicon CMOS technology). The main contribution of this
Mays, Violet; And Others
This brief report describes the selection of the pupil population of the Kamehameha Early Education Program (KEEP) Demonstration School. The pupil population must be representative of the Kalihi community (an urban area of Honolulu) from which it is drawn. An attempt was made to include 75% Hawaiian and 25 % Non-Hawaiian children, to select equal…
featsel: A framework for benchmarking of feature selection algorithms and cost functions
Marcelo S. Reis; Gustavo Estrela; Carlos Eduardo Ferreira; Junior Barrera
2017-01-01
In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions. This framework allows the user to deal with the search space as a Boolean lattice and has its core coded in C++ for computational efficiency purposes. Moreover, featsel includes Perl scripts to add new algorithms and/or cost functions, generate random instances, plot graphs and organize results into tables. Besides, this framework already comes with dozens of algorithms and co...
Xu, Xiaoguang; Wang, Jun; Zeng, Jing; Spurr, Robert; Liu, Xiong; Dubovik, Oleg; Li, Li; Li, Zhengqiang; Mishchenko, Michael I.; Siniuk, Aliaksandr;
2015-01-01
A new research algorithm is presented here as the second part of a two-part study to retrieve aerosol microphysical properties from the multispectral and multiangular photopolarimetric measurements taken by Aerosol Robotic Network's (AERONET's) new-generation Sun photometer. The algorithm uses an advanced UNified and Linearized Vector Radiative Transfer Model and incorporates a statistical optimization approach.While the new algorithmhas heritage from AERONET operational inversion algorithm in constraining a priori and retrieval smoothness, it has two new features. First, the new algorithmretrieves the effective radius, effective variance, and total volume of aerosols associated with a continuous bimodal particle size distribution (PSD) function, while the AERONET operational algorithm retrieves aerosol volume over 22 size bins. Second, our algorithm retrieves complex refractive indices for both fine and coarsemodes,while the AERONET operational algorithm assumes a size-independent aerosol refractive index. Mode-resolved refractive indices can improve the estimate of the single-scattering albedo (SSA) for each aerosol mode and thus facilitate the validation of satellite products and chemistry transport models. We applied the algorithm to a suite of real cases over Beijing_RADI site and found that our retrievals are overall consistent with AERONET operational inversions but can offer mode-resolved refractive index and SSA with acceptable accuracy for the aerosol composed by spherical particles. Along with the retrieval using both radiance and polarization, we also performed radiance-only retrieval to demonstrate the improvements by adding polarization in the inversion. Contrast analysis indicates that with polarization, retrieval error can be reduced by over 50% in PSD parameters, 10-30% in the refractive index, and 10-40% in SSA, which is consistent with theoretical analysis presented in the companion paper of this two-part study.
Self-organized spectrum chunk selection algorithm for Local Area LTE-Advanced
DEFF Research Database (Denmark)
Kumar, Sanjay; Wang, Yuanye; Marchetti, Nicola
2010-01-01
This paper presents a self organized spectrum chunk selection algorithm in order to minimize the mutual intercell interference among Home Node Bs (HeNBs), aiming to improve the system throughput performance compared to the existing frequency reuse one scheme. The proposed algorithm is useful...
Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases.
Mata-Montero, Manrique; Shalaby, Nabil; Sheppard, Bradley
2013-01-01
Obtaining unique oligos from an EST database is a problem of great importance in bioinformatics, particularly in the discovery of new genes and the mapping of the human genome. Many algorithms have been developed to find unique oligos, many of which are much less time consuming than the traditional brute force approach. An algorithm was presented by Zheng et al. (2004) which finds the solution of the unique oligos search problem efficiently. We implement this algorithm as well as several new algorithms based on some theorems included in this paper. We demonstrate how, with these new algorithms, we can obtain unique oligos much faster than with previous ones. We parallelize these new algorithms to further improve the time of finding unique oligos. All algorithms are run on ESTs obtained from a Barley EST database.
The admissible portfolio selection problem with transaction costs and an improved PSO algorithm
Chen, Wei; Zhang, Wei-Guo
2010-05-01
In this paper, we discuss the portfolio selection problem with transaction costs under the assumption that there exist admissible errors on expected returns and risks of assets. We propose a new admissible efficient portfolio selection model and design an improved particle swarm optimization (PSO) algorithm because traditional optimization algorithms fail to work efficiently for our proposed problem. Finally, we offer a numerical example to illustrate the proposed effective approaches and compare the admissible portfolio efficient frontiers under different constraints.
A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2015-11-01
Full Text Available The R function kofnGA conducts a genetic algorithm search for the best subset of k items from a set of n alternatives, given an objective function that measures the quality of a subset. The function fills a gap in the presently available subset selection software, which typically searches over a range of subset sizes, restricts the types of objective functions considered, or does not include freely available code. The new function is demonstrated on two types of problem where a fixed-size subset search is desirable: design of environmental monitoring networks, and D-optimal design of experiments. Additionally, the performance is evaluated on a class of constructed test problems with a novel design that is interesting in its own right.
Towards Feature Selection in Actor-Critic Algorithms
National Research Council Canada - National Science Library
Rohanimanesh, Khashayar; Roy, Nicholas; Tedrake, Russ
2007-01-01
.... They demonstrate that two popular representations for value methods -- the barycentric interpolators and the graph Laplacian proto-value functions -- can be used to represent the actor so as to satisfy these conditions...
Ohmichi, Yuya
2017-07-01
In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition (POD) to DMD and mode selection algorithms. By performing the preconditioning step, the DMD and mode selection can be performed with low memory consumption and therefore can be applied to large datasets. Additionally, we propose a simple mode selection algorithm based on a greedy method. The proposed framework is applied to the analysis of three-dimensional flow around a circular cylinder.
Relevant test set using feature selection algorithm for early detection ...
African Journals Online (AJOL)
The objective of feature selection is to find the most relevant features for classification. Thus, the dimensionality of the information will be reduced and may improve classification's accuracy. This paper proposed a minimum set of relevant questions that can be used for early detection of dyslexia. In this research, we ...
GMDH Method with Genetic Selection Algorithm and Cloning
Czech Academy of Sciences Publication Activity Database
Jiřina, Marcel; Jiřina jr., M.
2013-01-01
Roč. 23, č. 5 (2013), s. 451-464 ISSN 1210-0552 Institutional support: RVO:67985807 Keywords : multivariate data * GMDH * linear regression * Gauss-Markov conditions * cloning * genetic selection * classification Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.412, year: 2013
Rhodes, Danielle; Fogg, Quentin A; Lazarus, Michelle D
2018-05-01
Prevocational doctors aspiring to surgical careers are commonly recruited as anatomy demonstrators for undergraduate and graduate medical programmes. Entry into Surgical Education and Training (SET) is highly competitive and a unique opportunity exists to align anatomy demonstrator programmes with the selection criteria and core competencies of SET programmes. This study used a qualitative approach to (i) determine what criteria applicants for SET are assessed on and (ii) identify criteria that could be aligned with and enhanced by an anatomy demonstrator programme. The selection guidelines of all nine surgical specialties for the 2017 intake of SET trainees were analysed using qualitative content analysis methodology. The Royal Australasian College of Surgeons adopted a holistic approach to trainee selection that assessed both discipline-specific and discipline-independent skills. Qualitative content analysis identified eight categories of key selection criteria: medical expertise, scholarly activity, professional identity, interpersonal skills, integrity, self-management, insight and self-awareness and community involvement. The structured curriculum vitae was heavily weighted towards discipline-specific skills, such as medical expertise and scholarly activity. Insufficient information was available to determine the weighting of selection criteria assessed by the structured referee reports or interviews. Anatomy demonstrator programmes provide prevocational doctors with unique opportunities to develop surgical skills and competencies in a non-clinical setting. Constructively aligned anatomy demonstrator programmes may be particularly beneficial for prevocational doctors seeking to improve their anatomical knowledge, teaching skills or scholarly activity. © 2017 Royal Australasian College of Surgeons.
Directory of Open Access Journals (Sweden)
Boriša Ž. Jovanović
2010-10-01
Full Text Available Digitalni multimedijalni sadržaj postaje zastupljeniji i sve više se razmenjuje putem računarskih mreža i javnih kanala (satelitske komunikacije, bežične mreže, internet, itd. koji predstavljaju nebezbedne medijume za prenos informacija osetljive sadržine. Sve više na značaju dobijaju mehanizmi kriptološke zaštite slika i video sadržaja. Tradicionalni sistemi kriptografske obrade u sistemima za prenos ovih vrsta informacija garantuju visok stepen sigurnosti, ali i imaju svoje nedostatke - visoku cenu implementacije i znatno kašnjenje u prenosu podataka. Pomenuti nedostaci se prevazilaze primenom algoritama selektivnog šifrovanja. / Digital multimedia content is becoming widely used and increasingly exchanged over computer network and public channels (satelite, wireless networks, Internet, etc. which is unsecured transmission media for ex changing that kind of information. Mechanisms made to encrypt image and video data are becoming more and more significant. Traditional cryptographic techniques can guarantee a high level of security but at the cost of expensive implementation and important transmission delays. These shortcomings can be exceeded using selective encryption algorithms. Introduction In traditional image and video content protection schemes, called fully layered, the whole content is first compressed. Then, the compressed bitstream is entirely encrypted using a standard cipher (DES - Data Encryption Algorithm, IDEA - International Data Encryption Algorithm, AES - Advanced Encryption Algorithm etc.. The specific characteristics of this kind of data, high-transmission rate with limited bandwidth, make standard encryption algorithms inadequate. Another limitation of traditional systems consists of altering the whole bitstream syntax which may disable some codec functionalities on the delivery site coder and decoder on the receiving site. Selective encryption is a new trend in image and video content protection. As its
Directory of Open Access Journals (Sweden)
Yongyi Shou
2014-01-01
Full Text Available A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem.
An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection
Chen, Wei
2014-01-01
Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is ...
Fast Branch & Bound algorithms for optimal feature selection
Czech Academy of Sciences Publication Activity Database
Somol, Petr; Pudil, Pavel; Kittler, J.
2004-01-01
Roč. 26, č. 7 (2004), s. 900-912 ISSN 0162-8828 R&D Projects: GA ČR GA402/02/1271; GA ČR GA402/03/1310; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : subset search * feature selection * search tree Subject RIV: BD - Theory of Information Impact factor: 4.352, year: 2004
Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data.
Kroenke, Candyce H; Chubak, Jessica; Johnson, Lisa; Castillo, Adrienne; Weltzien, Erin; Caan, Bette J
2016-03-01
The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification. We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women's Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms-one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV-using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this "triangulation." Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. © The Author 2015. Published by Oxford University Press. All rights reserved. For
Selection of parameters for advanced machining processes using firefly algorithm
Directory of Open Access Journals (Sweden)
Rajkamal Shukla
2017-02-01
Full Text Available Advanced machining processes (AMPs are widely utilized in industries for machining complex geometries and intricate profiles. In this paper, two significant processes such as electric discharge machining (EDM and abrasive water jet machining (AWJM are considered to get the optimum values of responses for the given range of process parameters. The firefly algorithm (FA is attempted to the considered processes to obtain optimized parameters and the results obtained are compared with the results given by previous researchers. The variation of process parameters with respect to the responses are plotted to confirm the optimum results obtained using FA. In EDM process, the performance parameter “MRR” is increased from 159.70 gm/min to 181.6723 gm/min, while “Ra” and “REWR” are decreased from 6.21 μm to 3.6767 μm and 6.21% to 6.324 × 10−5% respectively. In AWJM process, the value of the “kerf” and “Ra” are decreased from 0.858 mm to 0.3704 mm and 5.41 mm to 4.443 mm respectively. In both the processes, the obtained results show a significant improvement in the responses.
Directory of Open Access Journals (Sweden)
Jing Bian
2016-01-01
Full Text Available In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as CSFSG. The evaluation function considers both feature-acquiring costs (test costs and misclassification costs in the field of network security, thereby weakening the influence of many instances from the majority of classes in large-scale datasets. The CSFSG algorithm reduces the total cost of feature selection and trades off both factors. The behavior of the CSFSG algorithm is tested on a large-scale dataset of network security, using two kinds of classifiers: C4.5 and k-nearest neighbor (KNN. The results of the experimental research show that the approach is efficient and able to effectively improve classification accuracy and to decrease classification time. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.
Auat Cheein, Fernando A.; Carelli, Ricardo
2011-01-01
This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are applied on the correction stage of the SLAM algorithm, restricting it to correct the SLAM algorithm with the most significant features. This restriction also causes a decrement in the processing time of the SLAM. Several experiments with a mobile robot are shown in this work. The experiments concern the map reconstruction and a comparison between the different proposed techniques performance. The experiments were carried out at an outdoor environment composed by trees, although the results shown herein are not restricted to a special type of features. PMID:22346568
Indian Academy of Sciences (India)
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
Soares, Sófacles Figueredo Carreiro; Galvão, Roberto Kawakami Harrop; Araújo, Mário César Ugulino; da Silva, Edvan Cirino; Pereira, Claudete Fernandes; de Andrade, Stéfani Iury Evangelista; Leite, Flaviano Carvalho
2011-03-09
This work proposes a modification to the successive projections algorithm (SPA) aimed at selecting spectral variables for multiple linear regression (MLR) in the presence of unknown interferents not included in the calibration data set. The modified algorithm favours the selection of variables in which the effect of the interferent is less pronounced. The proposed procedure can be regarded as an adaptive modelling technique, because the spectral features of the samples to be analyzed are considered in the variable selection process. The advantages of this new approach are demonstrated in two analytical problems, namely (1) ultraviolet-visible spectrometric determination of tartrazine, allure red and sunset yellow in aqueous solutions under the interference of erythrosine, and (2) near-infrared spectrometric determination of ethanol in gasoline under the interference of toluene. In these case studies, the performance of conventional MLR-SPA models is substantially degraded by the presence of the interferent. This problem is circumvented by applying the proposed Adaptive MLR-SPA approach, which results in prediction errors smaller than those obtained by three other multivariate calibration techniques, namely stepwise regression, full-spectrum partial-least-squares (PLS) and PLS with variables selected by a genetic algorithm. An inspection of the variable selection results reveals that the Adaptive approach successfully avoids spectral regions in which the interference is more intense. Copyright © 2011 Elsevier B.V. All rights reserved.
An Introduction to Model Selection: Tools and Algorithms
Directory of Open Access Journals (Sweden)
Sébastien Hélie
2006-03-01
Full Text Available Model selection is a complicated matter in science, and psychology is no exception. In particular, the high variance in the object of study (i.e., humans prevents the use of Poppers falsification principle (which is the norm in other sciences. Therefore, the desirability of quantitative psychological models must be assessed by measuring the capacity of the model to fit empirical data. In the present paper, an error measure (likelihood, as well as five methods to compare model fits (the likelihood ratio test, Akaikes information criterion, the Bayesian information criterion, bootstrapping and cross-validation, are presented. The use of each method is illustrated by an example, and the advantages and weaknesses of each method are also discussed.
The M-OLAP Cube Selection Problem: A Hyper-polymorphic Algorithm Approach
Loureiro, Jorge; Belo, Orlando
OLAP systems depend heavily on the materialization of multidimensional structures to speed-up queries, whose appropriate selection constitutes the cube selection problem. However, the recently proposed distribution of OLAP structures emerges to answer new globalization's requirements, capturing the known advantages of distributed databases. But this hardens the search for solutions, especially due to the inherent heterogeneity, imposing an extra characteristic of the algorithm that must be used: adaptability. Here the emerging concept known as hyper-heuristic can be a solution. In fact, having an algorithm where several (meta-)heuristics may be selected under the control of a heuristic has an intrinsic adaptive behavior. This paper presents a hyper-heuristic polymorphic algorithm used to solve the extended cube selection and allocation problem generated in M-OLAP architectures.
Foroughi Pour, Ali; Dalton, Lori A
2018-03-21
Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, in prior work we proposed a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms. The proposed algorithms outperform 2MNC-Robust and many other popular feature selection algorithms on synthetic data. In addition, enrichment analysis on real breast cancer, colon cancer, and Leukemia data indicates they also output many of the genes and pathways linked to the cancers under study. Bayesian feature selection is a promising framework for small-sample high-dimensional data, in particular biomarker discovery applications. When applied to cancer data these algorithms outputted many genes already shown to be involved in cancer as well as potentially new biomarkers. Furthermore, one of the proposed algorithms, SPM, outputs blocks of heavily correlated genes, particularly useful for studying gene interactions and gene networks.
Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei
2014-09-01
In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Zhongbo Hu
2014-01-01
Full Text Available Many improved differential Evolution (DE algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.
Directory of Open Access Journals (Sweden)
C. Fernandez-Lozano
2013-01-01
Full Text Available Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM. Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA, the most representative variables for a specific classification problem can be selected.
A Sensitive Secondary Users Selection Algorithm for Cognitive Radio Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Aohan Li
2016-03-01
Full Text Available Secondary Users (SUs are allowed to use the temporarily unused licensed spectrum without disturbing Primary Users (PUs in Cognitive Radio Ad Hoc Networks (CRAHNs. Existing architectures for CRAHNs impose energy-consuming Cognitive Radios (CRs on SUs. However, the advanced CRs will increase energy cost for their cognitive functionalities, which is undesirable for the battery powered devices. A new architecture referred to as spectral Requirement-based CRAHN (RCRAHN is proposed to enhance energy efficiency for CRAHNs in this paper. In RCRAHNs, only parts of SUs are equipped with CRs. SUs equipped with CRs are referred to as Cognitive Radio Users (CRUs. To further enhance energy efficiency of CRAHNs, we aim to select minimum CRUs to sense available spectrum. A non-linear programming problem is mathematically formulated under the constraints of energy efficiency and real-time. Considering the NP-hardness of the problem, a framework of a heuristic algorithm referred to as Sensitive Secondary Users Selection (SSUS was designed to compute the near-optimal solutions. The simulation results demonstrate that SSUS not only improves the energy efficiency, but also achieves satisfied performances in end-to-end delay and communication reliability.
DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm
Soufan, Othman
2015-02-26
Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem\\'s dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filteringmethods thatmay be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.
DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm
Soufan, Othman; Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.
2015-01-01
Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem's dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filteringmethods thatmay be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.
A Sensitive Secondary Users Selection Algorithm for Cognitive Radio Ad Hoc Networks.
Li, Aohan; Han, Guangjie; Wan, Liangtian; Shu, Lei
2016-03-26
Secondary Users (SUs) are allowed to use the temporarily unused licensed spectrum without disturbing Primary Users (PUs) in Cognitive Radio Ad Hoc Networks (CRAHNs). Existing architectures for CRAHNs impose energy-consuming Cognitive Radios (CRs) on SUs. However, the advanced CRs will increase energy cost for their cognitive functionalities, which is undesirable for the battery powered devices. A new architecture referred to as spectral Requirement-based CRAHN (RCRAHN) is proposed to enhance energy efficiency for CRAHNs in this paper. In RCRAHNs, only parts of SUs are equipped with CRs. SUs equipped with CRs are referred to as Cognitive Radio Users (CRUs). To further enhance energy efficiency of CRAHNs, we aim to select minimum CRUs to sense available spectrum. A non-linear programming problem is mathematically formulated under the constraints of energy efficiency and real-time. Considering the NP-hardness of the problem, a framework of a heuristic algorithm referred to as Sensitive Secondary Users Selection (SSUS) was designed to compute the near-optimal solutions. The simulation results demonstrate that SSUS not only improves the energy efficiency, but also achieves satisfied performances in end-to-end delay and communication reliability.
Hitting times of local and global optima in genetic algorithms with very high selection pressure
Directory of Open Access Journals (Sweden)
Eremeev Anton V.
2017-01-01
Full Text Available The paper is devoted to upper bounds on the expected first hitting times of the sets of local or global optima for non-elitist genetic algorithms with very high selection pressure. The results of this paper extend the range of situations where the upper bounds on the expected runtime are known for genetic algorithms and apply, in particular, to the Canonical Genetic Algorithm. The obtained bounds do not require the probability of fitness-decreasing mutation to be bounded by a constant which is less than one.
Log-linear model based behavior selection method for artificial fish swarm algorithm.
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm
Directory of Open Access Journals (Sweden)
Zhehuang Huang
2015-01-01
Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
Elsheikh, Ahmed H.
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems. © 2013 Elsevier Inc.
Gupta, Mridula; Pandher, Suneet; Kaur, Gurmeet; Rathore, Pankaj; Palli, Subba Reddy
2018-01-01
Amrasca biguttula biguttula (Ishida) commonly known as cotton leafhopper is a severe pest of cotton and okra. Not much is known on this insect at molecular level due to lack of genomic and transcriptomic data. To prepare for functional genomic studies in this insect, we evaluated 15 common housekeeping genes (Tub, B-Tub, EF alpha, GADPH, UbiCF, RP13, Ubiq, G3PD, VATPase, Actin, 18s, 28s, TATA, ETF, SOD and Cytolytic actin) during different developmental stages and under starvation stress. We selected early (1st and 2nd), late (3rd and 4th) stage nymphs and adults for identification of stable housekeeping genes using geNorm, NormFinder, BestKeeper and RefFinder software. Based on the different algorithms, RP13 and VATPase are identified as the most suitable reference genes for quantification of gene expression by reverse transcriptase quantitative PCR (RT-qPCR). Based on RefFinder which comprehended the results of three algorithms, RP13 in adults, Tubulin (Tub) in late nymphs, 28S in early nymph and UbiCF under starvation stress were identified as the most stable genes. We also developed methods for feeding double-stranded RNA (dsRNA) incorporated in the diet. Feeding dsRNA targeting Snf7, IAP, AQP1, and VATPase caused 56.17–77.12% knockdown of targeted genes compared to control and 16 to 48% mortality of treated insects when compared to control. PMID:29329327
Directory of Open Access Journals (Sweden)
Satnam Singh
Full Text Available Amrasca biguttula biguttula (Ishida commonly known as cotton leafhopper is a severe pest of cotton and okra. Not much is known on this insect at molecular level due to lack of genomic and transcriptomic data. To prepare for functional genomic studies in this insect, we evaluated 15 common housekeeping genes (Tub, B-Tub, EF alpha, GADPH, UbiCF, RP13, Ubiq, G3PD, VATPase, Actin, 18s, 28s, TATA, ETF, SOD and Cytolytic actin during different developmental stages and under starvation stress. We selected early (1st and 2nd, late (3rd and 4th stage nymphs and adults for identification of stable housekeeping genes using geNorm, NormFinder, BestKeeper and RefFinder software. Based on the different algorithms, RP13 and VATPase are identified as the most suitable reference genes for quantification of gene expression by reverse transcriptase quantitative PCR (RT-qPCR. Based on RefFinder which comprehended the results of three algorithms, RP13 in adults, Tubulin (Tub in late nymphs, 28S in early nymph and UbiCF under starvation stress were identified as the most stable genes. We also developed methods for feeding double-stranded RNA (dsRNA incorporated in the diet. Feeding dsRNA targeting Snf7, IAP, AQP1, and VATPase caused 56.17-77.12% knockdown of targeted genes compared to control and 16 to 48% mortality of treated insects when compared to control.
Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms
Directory of Open Access Journals (Sweden)
Fernando A. Auat Cheein
2013-01-01
Full Text Available Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter, the EKF (Extended Kalman Filter and the UKF (Unscented Kalman Filter. Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.
Energy Technology Data Exchange (ETDEWEB)
O`Brien, M.C.; Morrison, J.L.; Morneau, R.A.; Rudin, M.J.; Richardson, J.G.
1992-05-01
A formal methodology has been developed for identifying technology gaps and assessing innovative or postulated technologies for inclusion in proposed Buried Waste Integrated Demonstration (BWID) remediation systems. Called the Performance-Based Technology Selection Filter, the methodology provides a formalized selection process where technologies and systems are rated and assessments made based on performance measures, and regulatory and technical requirements. The results are auditable, and can be validated with field data. This analysis methodology will be applied to the remedial action of transuranic contaminated waste pits and trenches buried at the Idaho National Engineering Laboratory (INEL).
The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis
Directory of Open Access Journals (Sweden)
He Yan
2016-01-01
Full Text Available The particle swarm optimization (PSO is an optimization algorithm based on intelligent optimization. Parameters selection of PSO will play an important role in performance and efficiency of the algorithm. In this paper, the performance of PSO is analyzed when the control parameters vary, including particle number, accelerate constant, inertia weight and maximum limited velocity. And then PSO with dynamic parameters has been applied on the neural network training for gearbox fault diagnosis, the results with different parameters of PSO are compared and analyzed. At last some suggestions for parameters selection are proposed to improve the performance of PSO.
Patterson, Michael D.; Derlaga, Joseph M.; Borer, Nicholas K.
2016-01-01
Although the primary function of propellers is typically to produce thrust, aircraft equipped with distributed electric propulsion (DEP) may utilize propellers whose main purpose is to act as a form of high-lift device. These \\high-lift propellers" can be placed upstream of wing such that, when the higher-velocity ow in the propellers' slipstreams interacts with the wing, the lift is increased. This technique is a main design feature of a new NASA advanced design project called Scalable Convergent Electric Propulsion Technology Operations Research (SCEPTOR). The goal of the SCEPTOR project is design, build, and y a DEP aircraft to demonstrate that such an aircraft can be much more ecient than conventional designs. This paper provides details into the high-lift propeller system con guration selection for the SCEPTOR ight demonstrator. The methods used in the high-lift propeller system conceptual design and the tradeo s considered in selecting the number of propellers are discussed.
A Fast Algorithm of Convex Hull Vertices Selection for Online Classification.
Ding, Shuguang; Nie, Xiangli; Qiao, Hong; Zhang, Bo
2018-04-01
Reducing samples through convex hull vertices selection (CHVS) within each class is an important and effective method for online classification problems, since the classifier can be trained rapidly with the selected samples. However, the process of CHVS is NP-hard. In this paper, we propose a fast algorithm to select the convex hull vertices, based on the convex hull decomposition and the property of projection. In the proposed algorithm, the quadratic minimization problem of computing the distance between a point and a convex hull is converted into a linear equation problem with a low computational complexity. When the data dimension is high, an approximate, instead of exact, convex hull is allowed to be selected by setting an appropriate termination condition in order to delete more nonimportant samples. In addition, the impact of outliers is also considered, and the proposed algorithm is improved by deleting the outliers in the initial procedure. Furthermore, a dimension convention technique via the kernel trick is used to deal with nonlinearly separable problems. An upper bound is theoretically proved for the difference between the support vector machines based on the approximate convex hull vertices selected and all the training samples. Experimental results on both synthetic and real data sets show the effectiveness and validity of the proposed algorithm.
Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.
2018-04-01
This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.
Directory of Open Access Journals (Sweden)
Ioannis P. Panapakidis
2018-02-01
Full Text Available Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS. A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.
Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network
International Nuclear Information System (INIS)
Wang Xiaojia; Mao Qirong; Zhan Yongzhao
2008-01-01
There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions. The experiments show that this method can improve the recognition rate and the time of feature extraction
International Nuclear Information System (INIS)
Cieri, D.
2016-01-01
At the HL-LHC, proton bunches collide every 25 ns, producing an average of 140 pp interactions per bunch crossing. To operate in such an environment, the CMS experiment will need a Level-1 (L1) hardware trigger, able to identify interesting events within a latency of 12.5 μs. This novel L1 trigger will make use of data coming from the silicon tracker to constrain the trigger rate . Goal of this new track trigger will be to build L1 tracks from the tracker information. The architecture that will be implemented in future to process tracker data is still under discussion. One possibility is to adopt a system entirely based on FPGA electronic. The proposed track finding algorithm is based on the Hough transform method. The algorithm has been tested using simulated pp collision data and it is currently being demonstrated in hardware, using the “MP7”, which is a μTCA board with a powerful FPGA capable of handling data rates approaching 1 Tb/s. Two different implementations of the Hough transform technique are currently under investigation: one utilizes a systolic array to represent the Hough space, while the other exploits a pipelined approach. (paper)
AUTHOR|(CDS)2090481
2016-01-01
At the HL-LHC, proton bunches collide every 25\\,ns, producing an average of 140 pp interactions per bunch crossing. To operate in such an environment, the CMS experiment will need a Level-1 (L1) hardware trigger, able to identify interesting events within a latency of 12.5\\,$\\mu$s. This novel L1 trigger will make use of data coming from the silicon tracker to constrain the trigger rate. Goal of this new \\textit{track trigger} will be to build L1 tracks from the tracker information. The architecture that will be implemented in future to process tracker data is still under discussion. One possibility is to adopt a system entirely based on FPGA electronic. The proposed track finding algorithm is based on the Hough transform method. The algorithm has been tested using simulated pp collision data and it is currently being demonstrated in hardware, using the ``MP7'', which is a $\\mu$TCA board with a powerful FPGA capable of handling data rates approaching 1 Tb/s. Two different implementations of the Hough tran...
Automatic motor task selection via a bandit algorithm for a brain-controlled button
Fruitet, Joan; Carpentier, Alexandra; Munos, Rémi; Clerc, Maureen
2013-02-01
Objective. Brain-computer interfaces (BCIs) based on sensorimotor rhythms use a variety of motor tasks, such as imagining moving the right or left hand, the feet or the tongue. Finding the tasks that yield best performance, specifically to each user, is a time-consuming preliminary phase to a BCI experiment. This study presents a new adaptive procedure to automatically select (online) the most promising motor task for an asynchronous brain-controlled button. Approach. We develop for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory and design an EEG experiment to test our method. We compare (offline) the adaptive algorithm to a naïve selection strategy which uses uniformly distributed samples from each task. We also run the adaptive algorithm online to fully validate the approach. Main results. By not wasting time on inefficient tasks, and focusing on the most promising ones, this algorithm results in a faster task selection and a more efficient use of the BCI training session. More precisely, the offline analysis reveals that the use of this algorithm can reduce the time needed to select the most appropriate task by almost half without loss in precision, or alternatively, allow us to investigate twice the number of tasks within a similar time span. Online tests confirm that the method leads to an optimal task selection. Significance. This study is the first one to optimize the task selection phase by an adaptive procedure. By increasing the number of tasks that can be tested in a given time span, the proposed method could contribute to reducing ‘BCI illiteracy’.
Indian Academy of Sciences (India)
ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...
Threshold-selecting strategy for best possible ground state detection with genetic algorithms
Lässig, Jörg; Hoffmann, Karl Heinz
2009-04-01
Genetic algorithms are a standard heuristic to find states of low energy in complex state spaces as given by physical systems such as spin glasses but also in combinatorial optimization. The paper considers the problem of selecting individuals in the current population in genetic algorithms for crossover. Many schemes have been considered in literature as possible crossover selection strategies. We show for a large class of quality measures that the best possible probability distribution for selecting individuals in each generation of the algorithm execution is a rectangular distribution over the individuals sorted by their energy values. This means uniform probabilities have to be assigned to a group of the individuals with lowest energy in the population but probabilities equal to zero to individuals which are corresponding to energy values higher than a fixed cutoff, which is equal to a certain rank in the vector sorted by the energy of the states in the current population. The considered strategy is dubbed threshold selecting. The proof applies basic arguments of Markov chains and linear optimization and makes only a few assumptions on the underlying principles and hence applies to a large class of algorithms.
International Nuclear Information System (INIS)
Woudstra, Evert; Heijmen, Ben J M; Storchi, Pascal R M
2008-01-01
Some time ago we developed and published a new deterministic algorithm (called Cycle) for automatic selection of beam orientations in radiotherapy. This algorithm is a plan generation process aiming at the prescribed PTV dose within hard dose and dose-volume constraints. The algorithm allows a large number of input orientations to be used and selects only the most efficient orientations, surviving the selection process. Efficiency is determined by a score function and is more or less equal to the extent of uninhibited access to the PTV for a specific beam during the selection process. In this paper we compare the capabilities of fast-simulated annealing (FSA) and Cycle for cases where local optima are supposed to be present. Five pancreas and five oesophagus cases previously treated in our institute were selected for this comparison. Plans were generated for FSA and Cycle, using the same hard dose and dose-volume constraints, and the largest possible achieved PTV doses as obtained from these algorithms were compared. The largest achieved PTV dose values were generally very similar for the two algorithms. In some cases FSA resulted in a slightly higher PTV dose than Cycle, at the cost of switching on substantially more beam orientations than Cycle. In other cases, when Cycle generated the solution with the highest PTV dose using only a limited number of non-zero weight beams, FSA seemed to have some difficulty in switching off the unfavourable directions. Cycle was faster than FSA, especially for large-dimensional feasible spaces. In conclusion, for the cases studied in this paper, we have found that despite the inherent drawback of sequential search as used by Cycle (where Cycle could probably get trapped in a local optimum), Cycle is nevertheless able to find comparable or sometimes slightly better treatment plans in comparison with FSA (which in theory finds the global optimum) especially in large-dimensional beam weight spaces
Gaikwad, Akshay; Rehal, Diksha; Singh, Amandeep; Arvind, Dorai, Kavita
2018-02-01
We present the NMR implementation of a scheme for selective and efficient quantum process tomography without ancilla. We generalize this scheme such that it can be implemented efficiently using only a set of measurements involving product operators. The method allows us to estimate any element of the quantum process matrix to a desired precision, provided a set of quantum states can be prepared efficiently. Our modified technique requires fewer experimental resources as compared to the standard implementation of selective and efficient quantum process tomography, as it exploits the special nature of NMR measurements to allow us to compute specific elements of the process matrix by a restrictive set of subsystem measurements. To demonstrate the efficacy of our scheme, we experimentally tomograph the processes corresponding to "no operation," a controlled-NOT (CNOT), and a controlled-Hadamard gate on a two-qubit NMR quantum information processor, with high fidelities.
Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm
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Baljit Singh Khehra
2015-03-01
Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.
A Simple Density with Distance Based Initial Seed Selection Technique for K Means Algorithm
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Sajidha Syed Azimuddin
2017-01-01
Full Text Available Open issues with respect to K means algorithm are identifying the number of clusters, initial seed concept selection, clustering tendency, handling empty clusters, identifying outliers etc. In this paper we propose a novel and a simple technique considering both density and distance of the concepts in a dataset to identify initial seed concepts for clustering. Many authors have proposed different techniques to identify initial seed concepts; but our method ensures that the initial seed concepts are chosen from different clusters that are to be generated by the clustering solution. The hallmark of our algorithm is that it is a single pass algorithm that does not require any extra parameters to be estimated. Further, our seed concepts are one among the actual concepts and not the mean of representative concepts as is the case in many other algorithms. We have implemented our proposed algorithm and compared the results with the interval based technique of Fouad Khan. We see that our method outperforms the interval based method. We have also compared our method with the original random K means and K Means++ algorithms.
Ning, Jing; Chen, Yong; Piao, Jin
2017-07-01
Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1
Park, Thomas; Smith, Austin; Oliver, T. Emerson
2018-01-01
The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.
A Hybrid Feature Subset Selection Algorithm for Analysis of High Correlation Proteomic Data
Kordy, Hussain Montazery; Baygi, Mohammad Hossein Miran; Moradi, Mohammad Hassan
2012-01-01
Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. The surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been used to generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose the ovarian cancer patients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power. PMID:23717808
Directory of Open Access Journals (Sweden)
Wenyu Zhang
2016-01-01
Full Text Available With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition.
Directory of Open Access Journals (Sweden)
Wen Chen
2011-08-01
Full Text Available A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.
Indian Academy of Sciences (India)
algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).
Indian Academy of Sciences (India)
algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...
Selective epidemic broadcast algorithm to suppress broadcast storm in vehicular ad hoc networks
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M. Chitra
2018-03-01
Full Text Available Broadcasting in Vehicular Ad Hoc Networks is the best way to spread emergency messages all over the network. With the dynamic nature of vehicular ad hoc networks, simple broadcast or flooding faces the problem called as Broadcast Storm Problem (BSP. The issue of the BSP will degrade the performance of a message broadcasting process like increased overhead, collision and dissemination delay. The paper is motivated to solve the problems in the existing Broadcast Strom Suppression Algorithms (BSSAs like p-Persistence, TLO, VSPB, G-SAB and SIR. This paper proposes to suppress the Broadcast Storm Problem and to improve the Emergency Safety message dissemination rate through a new BSSA based on Selective Epidemic Broadcast Algorithm (SEB. The simulation results clearly show that the SEB outperforms the existing algorithms in terms of ESM Delivery Ratio, Message Overhead, Collision Ratio, Broadcast Storm Ratio and Redundant Rebroadcast Ratio with decreased Dissemination Delay.
International Nuclear Information System (INIS)
Li Yongjie; Yao Dezhong; Yao, Jonathan; Chen Wufan
2005-01-01
Automatic beam angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated
Directory of Open Access Journals (Sweden)
Aiming Liu
2017-11-01
Full Text Available Motor Imagery (MI electroencephalography (EEG is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP and local characteristic-scale decomposition (LCD algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.
Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi
2017-11-08
Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.
Video error concealment using block matching and frequency selective extrapolation algorithms
P. K., Rajani; Khaparde, Arti
2017-06-01
Error Concealment (EC) is a technique at the decoder side to hide the transmission errors. It is done by analyzing the spatial or temporal information from available video frames. It is very important to recover distorted video because they are used for various applications such as video-telephone, video-conference, TV, DVD, internet video streaming, video games etc .Retransmission-based and resilient-based methods, are also used for error removal. But these methods add delay and redundant data. So error concealment is the best option for error hiding. In this paper, the error concealment methods such as Block Matching error concealment algorithm is compared with Frequency Selective Extrapolation algorithm. Both the works are based on concealment of manually error video frames as input. The parameter used for objective quality measurement was PSNR (Peak Signal to Noise Ratio) and SSIM(Structural Similarity Index). The original video frames along with error video frames are compared with both the Error concealment algorithms. According to simulation results, Frequency Selective Extrapolation is showing better quality measures such as 48% improved PSNR and 94% increased SSIM than Block Matching Algorithm.
Computation-aware algorithm selection approach for interlaced-to-progressive conversion
Park, Sang-Jun; Jeon, Gwanggil; Jeong, Jechang
2010-05-01
We discuss deinterlacing results in a computationally constrained and varied environment. The proposed computation-aware algorithm selection approach (CASA) for fast interlaced to progressive conversion algorithm consists of three methods: the line-averaging (LA) method for plain regions, the modified edge-based line-averaging (MELA) method for medium regions, and the proposed covariance-based adaptive deinterlacing (CAD) method for complex regions. The proposed CASA uses two criteria, mean-squared error (MSE) and CPU time, for assigning the method. We proposed a CAD method. The principle idea of CAD is based on the correspondence between the high and low-resolution covariances. We estimated the local covariance coefficients from an interlaced image using Wiener filtering theory and then used these optimal minimum MSE interpolation coefficients to obtain a deinterlaced image. The CAD method, though more robust than most known methods, was not found to be very fast compared to the others. To alleviate this issue, we proposed an adaptive selection approach using a fast deinterlacing algorithm rather than using only one CAD algorithm. The proposed hybrid approach of switching between the conventional schemes (LA and MELA) and our CAD was proposed to reduce the overall computational load. A reliable condition to be used for switching the schemes was presented after a wide set of initial training processes. The results of computer simulations showed that the proposed methods outperformed a number of methods presented in the literature.
Directory of Open Access Journals (Sweden)
Rong-Chao Peng
2016-01-01
Full Text Available Smartphone photoplethysmography is a newly developed technique that can detect several physiological parameters from the photoplethysmographic signal obtained by the built-in camera of a smartphone. It is simple, low-cost, and easy-to-use, with a great potential to be used in remote medicine and home healthcare service. However, the determination of the optimal region of interest (ROI, which is an important issue for extracting photoplethysmographic signals from the camera video, has not been well studied. We herein proposed five algorithms for ROI selection: variance (VAR, spectral energy ratio (SER, template matching (TM, temporal difference (TD, and gradient (GRAD. Their performances were evaluated by a 50-subject experiment comparing the heart rates measured from the electrocardiogram and those from the smartphone using the five algorithms. The results revealed that the TM and the TD algorithms outperformed the other three as they had less standard error of estimate (<1.5 bpm and smaller limits of agreement (<3 bpm. The TD algorithm was slightly better than the TM algorithm and more suitable for smartphone applications. These results may be helpful to improve the accuracy of the physiological parameters measurement and to make the smartphone photoplethysmography technique more practical.
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
International Nuclear Information System (INIS)
An, Dawn; Kim, Nam H.; Choi, Joo-Ho
2015-01-01
This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available. - Highlights: • Practical review of data-driven and physics-based prognostics are provided. • As common prognostics algorithms, NN, GP, PF and BM are introduced. • Algorithms’ attributes, pros and cons, and applicable conditions are discussed. • This will be helpful to choose the best algorithm for different applications
Indian Academy of Sciences (India)
will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...
Momeni, Saba; Pourghassem, Hossein
2014-08-01
Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.
Sub-Circuit Selection and Replacement Algorithms Modeled as Term Rewriting Systems
2008-12-16
of Defense, or the United States Government . AFIT/GCO/ENG/09-02 Sub-circuit Selection and Replacement Algorithms Modeled as Term Rewriting Systems... unicorns and random programs”. Communications and Computer Networks, 24–30. 2005. 87 Vita Eric D. Simonaire graduated from Granite Baptist Church School in...Service to attend the Air Force Institute of Technol- ogy in 2007. Upon graduation, he will serve the federal government in an Information Assurance
Road network selection for small-scale maps using an improved centrality-based algorithm
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Roy Weiss
2014-12-01
Full Text Available The road network is one of the key feature classes in topographic maps and databases. In the task of deriving road networks for products at smaller scales, road network selection forms a prerequisite for all other generalization operators, and is thus a fundamental operation in the overall process of topographic map and database production. The objective of this work was to develop an algorithm for automated road network selection from a large-scale (1:10,000 to a small-scale database (1:200,000. The project was pursued in collaboration with swisstopo, the national mapping agency of Switzerland, with generic mapping requirements in mind. Preliminary experiments suggested that a selection algorithm based on betweenness centrality performed best for this purpose, yet also exposed problems. The main contribution of this paper thus consists of four extensions that address deficiencies of the basic centrality-based algorithm and lead to a significant improvement of the results. The first two extensions improve the formation of strokes concatenating the road segments, which is crucial since strokes provide the foundation upon which the network centrality measure is computed. Thus, the first extension ensures that roundabouts are detected and collapsed, thus avoiding interruptions of strokes by roundabouts, while the second introduces additional semantics in the process of stroke formation, allowing longer and more plausible strokes to built. The third extension detects areas of high road density (i.e., urban areas using density-based clustering and then locally increases the threshold of the centrality measure used to select road segments, such that more thinning takes place in those areas. Finally, since the basic algorithm tends to create dead-ends—which however are not tolerated in small-scale maps—the fourth extension reconnects these dead-ends to the main network, searching for the best path in the main heading of the dead-end.
Trust Based Algorithm for Candidate Node Selection in Hybrid MANET-DTN
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Jan Papaj
2014-01-01
Full Text Available The hybrid MANET - DTN is a mobile network that enables transport of the data between groups of the disconnected mobile nodes. The network provides benefits of the Mobile Ad-Hoc Networks (MANET and Delay Tolerant Network (DTN. The main problem of the MANET occurs if the communication path is broken or disconnected for some short time period. On the other side, DTN allows sending data in the disconnected environment with respect to higher tolerance to delay. Hybrid MANET - DTN provides optimal solution for emergency situation in order to transport information. Moreover, the security is the critical factor because the data are transported by mobile devices. In this paper, we investigate the issue of secure candidate node selection for transportation of the data in a disconnected environment for hybrid MANET- DTN. To achieve the secure selection of the reliable mobile nodes, the trust algorithm is introduced. The algorithm enables select reliable nodes based on collecting routing information. This algorithm is implemented to the simulator OPNET modeler.
Pashaei, Elnaz; Pashaei, Elham; Aydin, Nizamettin
2018-04-14
In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets. Copyright © 2018 Elsevier Inc. All rights reserved.
An ILP based Algorithm for Optimal Customer Selection for Demand Response in SmartGrids
Energy Technology Data Exchange (ETDEWEB)
Kuppannagari, Sanmukh R. [Univ. of Southern California, Los Angeles, CA (United States); Kannan, Rajgopal [Louisiana State Univ., Baton Rouge, LA (United States); Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States)
2015-12-07
Demand Response (DR) events are initiated by utilities during peak demand periods to curtail consumption. They ensure system reliability and minimize the utility’s expenditure. Selection of the right customers and strategies is critical for a DR event. An effective DR scheduling algorithm minimizes the curtailment error which is the absolute difference between the achieved curtailment value and the target. State-of-the-art heuristics exist for customer selection, however their curtailment errors are unbounded and can be as high as 70%. In this work, we develop an Integer Linear Programming (ILP) formulation for optimally selecting customers and curtailment strategies that minimize the curtailment error during DR events in SmartGrids. We perform experiments on real world data obtained from the University of Southern California’s SmartGrid and show that our algorithm achieves near exact curtailment values with errors in the range of 10^{-7} to 10^{-5}, which are within the range of numerical errors. We compare our results against the state-of-the-art heuristic being deployed in practice in the USC SmartGrid. We show that for the same set of available customer strategy pairs our algorithm performs 103 to 107 times better in terms of the curtailment errors incurred.
Algorithms of control parameters selection for automation of FDM 3D printing process
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Kogut Paweł
2017-01-01
Full Text Available The paper presents algorithms of control parameters selection of the Fused Deposition Modelling (FDM technology in case of an open printing solutions environment and 3DGence ONE printer. The following parameters were distinguished: model mesh density, material flow speed, cooling performance, retraction and printing speeds. These parameters are independent in principle printing system, but in fact to a certain degree that results from the selected printing equipment features. This is the first step for automation of the 3D printing process in FDM technology.
A low complexity algorithm for multiple relay selection in two-way relaying Cognitive Radio networks
Alsharoa, Ahmad M.
2013-06-01
In this paper, a multiple relay selection scheme for two-way relaying cognitive radio network is investigated. We consider a cooperative Cognitive Radio (CR) system with spectrum sharing scenario using Amplify-and-Forward (AF) protocol, where licensed users and unlicensed users operate on the same frequency band. The main objective is to maximize the sum rate of the unlicensed users allowed to share the spectrum with the licensed users by respecting a tolerated interference threshold. A practical low complexity heuristic approach is proposed to solve our formulated optimization problem. Selected numerical results show that the proposed algorithm reaches a performance close to the performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions while providing a considerable saving in terms of computational complexity. In addition, these results show that our proposed scheme significantly outperforms the single relay selection scheme. © 2013 IEEE.
Demonstration of risk-based decision analysis in remedial alternative selection and design
International Nuclear Information System (INIS)
Evans, E.K.; Duffield, G.M.; Massmann, J.W.; Freeze, R.A.; Stephenson, D.E.
1993-01-01
This study demonstrates the use of risk-based decision analysis (Massmann and Freeze 1987a, 1987b) in the selection and design of an engineering alternative for groundwater remediation at a waste site at the Savannah River Site, a US Department of Energy facility in South Carolina. The investigation focuses on the remediation and closure of the H-Area Seepage Basins, an inactive disposal site that formerly received effluent water from a nearby production facility. A previous study by Duffield et al. (1992), which used risk-based decision analysis to screen a number of ground-water remediation alternatives under consideration for this site, indicated that the most attractive remedial option is ground-water extraction by wells coupled with surface water discharge of treated effluent. The aim of the present study is to demonstrate the iterative use of risk-based decision analysis throughout the design of a particular remedial alternative. In this study, we consider the interaction between two episodes of aquifer testing over a 6-year period and the refinement of a remedial extraction well system design. Using a three-dimensional ground-water flow model, this study employs (1) geostatistics and Monte Carlo techniques to simulate hydraulic conductivity as a stochastic process and (2) Bayesian updating and conditional simulation to investigate multiple phases of aquifer testing. In our evaluation of a remedial alternative, we compute probabilistic costs associated with the failure of an alternative to completely capture a simulated contaminant plume. The results of this study demonstrate the utility of risk-based decision analysis as a tool for improving the design of a remedial alternative through the course of phased data collection at a remedial site
International Nuclear Information System (INIS)
Mazurowski, Maciej A; Tourassi, Georgia D; Malof, Jordan M
2011-01-01
When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a pool of previously acquired instances, attempt to select those that will be the most effective to construct an instance-based classifier in terms of classification performance, time efficiency and storage requirements. We evaluate seven previously proposed instance selection algorithms and compare their performance to simple random selection of instances. We perform the evaluation using k-nearest neighbor classifier and three classification problems: one with simulated Gaussian data and two based on clinical databases for breast cancer detection and diagnosis, respectively. Finally, we evaluate the impact of the number of instances available for selection on the performance of the selection algorithms and conduct initial analysis of the selected instances. The experiments show that for all investigated classification problems, it was possible to reduce the size of the original development dataset to less than 3% of its initial size while maintaining or improving the classification performance. Random mutation hill climbing emerges as the superior selection algorithm. Furthermore, we show that some previously proposed algorithms perform worse than random selection. Regarding the impact of the number of instances available for the classifier development on the performance of the selection algorithms, we confirm that the selection algorithms are generally more effective as the pool of available instances increases. In conclusion, instance selection is generally beneficial for instance-based classifiers as it can improve their performance, reduce their storage requirements and improve their response time. However, choosing the right selection algorithm is crucial.
Morello, Giuseppe; Morris, P. W.; Van Dyk, S. D.; Marston, A. P.; Mauerhan, J. C.
2018-01-01
We have investigated and applied machine-learning algorithms for infrared colour selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the Spitzer Galactic Legacy Infrared Midplane Survey Extraordinaire (GLIMPSE) catalogue of the infrared objects in the Galactic plane can be classified into different stellar populations based on the colours inferred from their broad-band photometric magnitudes [J, H and Ks from 2 Micron All Sky Survey (2MASS), and the four Spitzer/IRAC bands]. The algorithms tested in this pilot study are variants of the k-nearest neighbours approach, which is ideal for exploratory studies of classification problems where interrelations between variables and classes are complicated. The aims of this study are (1) to provide an automated tool to select reliable WR candidates and potentially other classes of objects, (2) to measure the efficiency of infrared colour selection at performing these tasks and (3) to lay the groundwork for statistically inferring the total number of WR stars in our Galaxy. We report the performance results obtained over a set of known objects and selected candidates for which we have carried out follow-up spectroscopic observations, and confirm the discovery of four new WR stars.
Directory of Open Access Journals (Sweden)
Saleh LAshkari
2016-06-01
Full Text Available Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA, it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.
Chen, Gang; Yang, Bing; Zhang, Xiaoyun; Gao, Zhiyong
2017-07-01
The latest high efficiency video coding (HEVC) standard significantly increases the encoding complexity for improving its coding efficiency. Due to the limited computational capability of handheld devices, complexity constrained video coding has drawn great attention in recent years. A complexity control algorithm based on adaptive mode selection is proposed for interframe coding in HEVC. Considering the direct proportionality between encoding time and computational complexity, the computational complexity is measured in terms of encoding time. First, complexity is mapped to a target in terms of prediction modes. Then, an adaptive mode selection algorithm is proposed for the mode decision process. Specifically, the optimal mode combination scheme that is chosen through offline statistics is developed at low complexity. If the complexity budget has not been used up, an adaptive mode sorting method is employed to further improve coding efficiency. The experimental results show that the proposed algorithm achieves a very large complexity control range (as low as 10%) for the HEVC encoder while maintaining good rate-distortion performance. For the lowdelayP condition, compared with the direct resource allocation method and the state-of-the-art method, an average gain of 0.63 and 0.17 dB in BDPSNR is observed for 18 sequences when the target complexity is around 40%.
Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider
2016-01-01
Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174
A New Feature Selection Algorithm Based on the Mean Impact Variance
Directory of Open Access Journals (Sweden)
Weidong Cheng
2014-01-01
Full Text Available The selection of fewer or more representative features from multidimensional features is important when the artificial neural network (ANN algorithm is used as a classifier. In this paper, a new feature selection method called the mean impact variance (MIVAR method is proposed to determine the feature that is more suitable for classification. Moreover, this method is constructed on the basis of the training process of the ANN algorithm. To verify the effectiveness of the proposed method, the MIVAR value is used to rank the multidimensional features of the bearing fault diagnosis. In detail, (1 70-dimensional all waveform features are extracted from a rolling bearing vibration signal with four different operating states, (2 the corresponding MIVAR values of all 70-dimensional features are calculated to rank all features, (3 14 groups of 10-dimensional features are separately generated according to the ranking results and the principal component analysis (PCA algorithm and a back propagation (BP network is constructed, and (4 the validity of the ranking result is proven by training this BP network with these seven groups of 10-dimensional features and by comparing the corresponding recognition rates. The results prove that the features with larger MIVAR value can lead to higher recognition rates.
Morita, Kenji; Jitsev, Jenia; Morrison, Abigail
2016-09-15
Value-based action selection has been suggested to be realized in the corticostriatal local circuits through competition among neural populations. In this article, we review theoretical and experimental studies that have constructed and verified this notion, and provide new perspectives on how the local-circuit selection mechanisms implement reinforcement learning (RL) algorithms and computations beyond them. The striatal neurons are mostly inhibitory, and lateral inhibition among them has been classically proposed to realize "Winner-Take-All (WTA)" selection of the maximum-valued action (i.e., 'max' operation). Although this view has been challenged by the revealed weakness, sparseness, and asymmetry of lateral inhibition, which suggest more complex dynamics, WTA-like competition could still occur on short time scales. Unlike the striatal circuit, the cortical circuit contains recurrent excitation, which may enable retention or temporal integration of information and probabilistic "soft-max" selection. The striatal "max" circuit and the cortical "soft-max" circuit might co-implement an RL algorithm called Q-learning; the cortical circuit might also similarly serve for other algorithms such as SARSA. In these implementations, the cortical circuit presumably sustains activity representing the executed action, which negatively impacts dopamine neurons so that they can calculate reward-prediction-error. Regarding the suggested more complex dynamics of striatal, as well as cortical, circuits on long time scales, which could be viewed as a sequence of short WTA fragments, computational roles remain open: such a sequence might represent (1) sequential state-action-state transitions, constituting replay or simulation of the internal model, (2) a single state/action by the whole trajectory, or (3) probabilistic sampling of state/action. Copyright © 2016. Published by Elsevier B.V.
Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Iwan Syarif
2016-12-01
Full Text Available This paper describes the advantages of using Evolutionary Algorithms (EA for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA and Particle Swarm Optimizations (PSO as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets. However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.
2016-02-01
all of the ADA called indications into three groups: true positives (TP), missed calls (MC) and false calls (FC). Note, an indication position error...data review burden and improve the reliability of the ultrasonic inspection of large composite structures, automated data analysis ( ADA ) algorithms...thickness and backwall C-scan images. 15. SUBJECT TERMS automated data analysis ( ADA ) algorithms; time-of-flight indications; backwall amplitude dropout
Directory of Open Access Journals (Sweden)
Hala Alshamlan
2015-01-01
Full Text Available An artificial bee colony (ABC is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR, and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO. The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.
Alshamlan, Hala; Badr, Ghada; Alohali, Yousef
2015-01-01
An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.
A genetic algorithm for multiple relay selection in two-way relaying cognitive radio networks
Alsharoa, Ahmad M.
2013-09-01
In this paper, we investigate a multiple relay selection scheme for two-way relaying cognitive radio networks where primary users and secondary users operate on the same frequency band. More specifically, cooperative relays using Amplifyand- Forward (AF) protocol are optimally selected to maximize the sum rate of the secondary users without degrading the Quality of Service (QoS) of the primary users by respecting a tolerated interference threshold. A strong optimization tool based on genetic algorithm is employed to solve our formulated optimization problem where discrete relay power levels are considered. Our simulation results show that the practical heuristic approach achieves almost the same performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.
Omar, A. H.; Tackett, J. L.; Vaughan, M. A.; Kar, J.; Trepte, C. R.; Winker, D. M.
2016-12-01
This presentation describes several enhancements planned for the version 4 aerosol subtyping and lidar ratio selection algorithms of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP subtyping algorithm determines the most likely aerosol type from CALIOP measurements (attenuated backscatter, estimated particulate depolarization ratios δe, layer altitude), and surface type. The aerosol type, so determined, is associated with a lidar ratio (LR) from a discrete set of values. Some of these lidar ratios have been updated in the version 4 algorithms. In particular, the dust and polluted dust will be adjusted to reflect the latest measurements and model studies of these types. Version 4 eliminates the confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions used to identify smoke aerosols over the ocean. In the subtyping algorithms pure dust is determined by high estimated particulate depolarization ratios [δe > 0.20]. Mixtures of dust and other aerosol types are determined by intermediate values of the estimated depolarization ratio [0.075limited to mixtures of dust and smoke, the so called polluted dust aerosol type. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the revised classification algorithms, polluted dust will still defined as dust + smoke/pollution but in the marine boundary layer instances of moderate depolarization will be typed as dusty marine aerosols with a lower lidar ratio than polluted dust. The dusty marine type introduced in version 4 is modeled as a mixture of dust + marine aerosol. To account for fringes, the version 4 Level 2 algorithms implement Subtype Coalescence Algorithm for AeRosol Fringes (SCAARF) routine to detect and classify fringe of aerosol plumes that are detected at 20 km or 80 km horizontal resolution at the plume base. These
Directory of Open Access Journals (Sweden)
R. Jegadeeshwaran
2015-03-01
Full Text Available In automobile, brake system is an essential part responsible for control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the vehicle cum passenger's safety. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is essential. Vibration based condition monitoring using machine learning techniques are gaining momentum. This study is one such attempt to perform the condition monitoring of a hydraulic brake system through vibration analysis. In this research, the performance of a Clonal Selection Classification Algorithm (CSCA for brake fault diagnosis has been reported. A hydraulic brake system test rig was fabricated. Under good and faulty conditions of a brake system, the vibration signals were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signal. The best feature set was identified for classification using attribute evaluator. The selected features were then classified using CSCA. The classification accuracy of such artificial intelligence technique has been compared with other machine learning approaches and discussed. The Clonal Selection Classification Algorithm performs better and gives the maximum classification accuracy (96% for the fault diagnosis of a hydraulic brake system.
A stereo remote sensing feature selection method based on artificial bee colony algorithm
Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi
2014-05-01
To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.
Elsheikh, A. H.
2013-12-01
Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam\\'s razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.
Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr
2017-12-01
There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.
Backes, Michael; Meiser, Sebastian; Slowik, Marcin
2015-01-01
In this paper, we present a rigorous methodology for quantifying the anonymity provided by Tor against a variety of structural attacks, i.e., adversaries that compromise Tor nodes and thereby perform eavesdropping attacks to deanonymize Tor users. First, we provide an algorithmic approach for computing the anonymity impact of such structural attacks against Tor. The algorithm is parametric in the considered path selection algorithm and is, hence, capable of reasoning about variants of Tor and...
Soufan, Othman
2012-01-01
proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a
International Nuclear Information System (INIS)
Xingen Wu; Zunliang Wang
2000-01-01
A genetic algorithm has been used to optimize the selection of beam weights for external beam three-dimensional conformal radiotherapy treatment planning. A fitness function is defined, which includes a difference function to achieve a least-square fit to doses at preselected points in a planning target volume, and a penalty item to constrain the maximum allowable doses delivered to critical organs. Adjustment between the dose uniformity within the target volume and the dose constraint to the critical structures can be achieved by varying the beam weight variables in the fitness function. A floating-point encoding schema and several operators, like uniform crossover, arithmetical crossover, geometrical crossover, Gaussian mutation and uniform mutation, have been used to evolve the population. Three different cases were used to verify the correctness of the algorithm and quality assessment based on dose-volume histograms and three-dimensional dose distributions were given. The results indicate that the genetic algorithm presented here has considerable potential. (author)
Energy Technology Data Exchange (ETDEWEB)
Bostock, J.; Weller, P. [School of Informatics, City University London, London EC1V 0HB (United Kingdom); Cooklin, M., E-mail: jbostock1@msn.co [Cardiovascular Directorate, Guy' s and St. Thomas' NHS Foundation Trust, London, SE1 7EH (United Kingdom)
2010-07-01
Automated diagnostic algorithms are used in implantable cardioverter-defibrillators (ICD's) to detect abnormal heart rhythms. Algorithms misdiagnose and improved specificity is needed to prevent inappropriate therapy. Knowledge engineering (KE) and artificial intelligence (AI) could improve this. A pilot study of KE was performed with artificial neural network (ANN) as AI system. A case note review analysed arrhythmic events stored in patients ICD memory. 13.2% patients received inappropriate therapy. The best ICD algorithm had sensitivity 1.00, specificity 0.69 (p<0.001 different to gold standard). A subset of data was used to train and test an ANN. A feed-forward, back-propagation network with 7 inputs, a 4 node hidden layer and 1 output had sensitivity 1.00, specificity 0.71 (p<0.001). A prospective study was performed using KE to list arrhythmias, factors and indicators for which measurable parameters were evaluated and results reviewed by a domain expert. Waveforms from electrodes in the heart and thoracic bio-impedance; temperature and motion data were collected from 65 patients during cardiac electrophysiological studies. 5 incomplete datasets were due to technical failures. We concluded that KE successfully guided selection of parameters and ANN produced a usable system and that complex data collection carries greater risk of technical failure, leading to data loss.
International Nuclear Information System (INIS)
Bostock, J.; Weller, P.; Cooklin, M.
2010-01-01
Automated diagnostic algorithms are used in implantable cardioverter-defibrillators (ICD's) to detect abnormal heart rhythms. Algorithms misdiagnose and improved specificity is needed to prevent inappropriate therapy. Knowledge engineering (KE) and artificial intelligence (AI) could improve this. A pilot study of KE was performed with artificial neural network (ANN) as AI system. A case note review analysed arrhythmic events stored in patients ICD memory. 13.2% patients received inappropriate therapy. The best ICD algorithm had sensitivity 1.00, specificity 0.69 (p<0.001 different to gold standard). A subset of data was used to train and test an ANN. A feed-forward, back-propagation network with 7 inputs, a 4 node hidden layer and 1 output had sensitivity 1.00, specificity 0.71 (p<0.001). A prospective study was performed using KE to list arrhythmias, factors and indicators for which measurable parameters were evaluated and results reviewed by a domain expert. Waveforms from electrodes in the heart and thoracic bio-impedance; temperature and motion data were collected from 65 patients during cardiac electrophysiological studies. 5 incomplete datasets were due to technical failures. We concluded that KE successfully guided selection of parameters and ANN produced a usable system and that complex data collection carries greater risk of technical failure, leading to data loss.
Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin
2015-10-01
The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.
Zeng, Xiang-Yang; Wang, Shu-Guang; Gao, Li-Ping
2010-09-01
As the basic data for virtual auditory technology, head-related transfer function (HRTF) has many applications in the areas of room acoustic modeling, spatial hearing and multimedia. How to individualize HRTF fast and effectively has become an opening problem at present. Based on the similarity and relativity of anthropometric structures, a hybrid HRTF customization algorithm, which has combined the method of principal component analysis (PCA), multiple linear regression (MLR) and database matching (DM), has been presented in this paper. The HRTFs selected by both the best match and the worst match have been applied into obtaining binaurally auralized sounds, which are then used for subjective listening experiments and the results are compared. For the area in the horizontal plane, the localization results have shown that the selection of HRTFs can enhance the localization accuracy and can also abate the problem of front-back confusion.
Re-editing and Censoring of Detectors in Negative Selection Algorithm
Directory of Open Access Journals (Sweden)
X.Z. Gao
2009-12-01
Full Text Available The Negative Selection Algorithm (NSA is a kind of novelty detection method inspired by the biological self/nonself discrimination principles. In this paper, we propose two new schemes for the detectors re-editing and censoring in the NSA. The detectors that fail to pass the negative selection phase are re-edited and updated to become qualified using the Differential Evolution (DE method. In the detectors censoring, the qualification of all the detectors is evaluated, and only those appropriate ones are retained. Prior knowledge of the anomalous data is utilized to discriminate the detectors so that their anomaly detection performances can be improved. The effectiveness of our detectors re-editing and censoring approaches is examined with both artificial signals and a practical bearings fault detection problem.
Energy Technology Data Exchange (ETDEWEB)
Pereira, Augusto, E-mail: augusto.pereira@ciemat.es [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Vega, Jesús; Moreno, Raúl [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Dormido-Canto, Sebastián [Dpto. Informática y Automática – UNED, Madrid (Spain); Rattá, Giuseppe A. [Laboratorio Nacional de Fusión, CIEMAT, Madrid (Spain); Pavón, Fernando [Dpto. Informática y Automática – UNED, Madrid (Spain)
2015-10-15
Recently, a probabilistic classifier has been developed at JET to be used as predictor from scratch. It has been applied to a database of 1237 JET ITER-like wall (ILW) discharges (of which 201 disrupted) with good results: success rate of 94% and false alarm rate of 4.21%. A combinatorial analysis between 14 features to ensure the selection of the best ones to achieve good enough results in terms of success rate and false alarm rate was performed. All possible combinations with a number of features between 2 and 7 were tested and 9893 different predictors were analyzed. An important drawback in this analysis was the time required to compute the results that can be estimated in 1731 h (∼2.4 months). Genetic algorithms (GA) are searching algorithms that simulate the process of natural selection. In this article, the GA and the Venn predictors are combined with the objective not only of finding good enough features within the 14 available ones but also of reducing the computational time requirements. Five different performance metrics as measures of the GA fitness function have been evaluated. The best metric was the measurement called Informedness, with just 6 generations (168 predictors at 29.4 h).
A standard deviation selection in evolutionary algorithm for grouper fish feed formulation
Cai-Juan, Soong; Ramli, Razamin; Rahman, Rosshairy Abdul
2016-10-01
Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment. Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price. However, the demand of grouper fish is still insufficient from the wild catch. Therefore, there is a need to farm grouper fish to cater to the market demand. In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available. Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm. Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation. Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable. The feasible and low fitness, quick solution can be obtained. These fitness can be further predicted to minimize cost in farming grouper fish.
On the Structure of a Best Possible Crossover Selection Strategy in Genetic Algorithms
Lässig, Jörg; Hoffmann, Karl Heinz
The paper considers the problem of selecting individuals in the current population in genetic algorithms for crossover to find a solution with high fitness for a given optimization problem. Many different schemes have been described in the literature as possible strategies for this task but so far comparisons have been predominantly empirical. It is shown that if one wishes to maximize any linear function of the final state probabilities, e.g. the fitness of the best individual in the final population of the algorithm, then a best probability distribution for selecting an individual in each generation is a rectangular distribution over the individuals sorted in descending sequence by their fitness values. This means uniform probabilities have to be assigned to a group of the best individuals of the population but probabilities equal to zero to individuals with lower fitness, assuming that the probability distribution to choose individuals from the current population can be chosen independently for each iteration and each individual. This result is then generalized also to typical practically applied performance measures, such as maximizing the expected fitness value of the best individual seen in any generation.
International Nuclear Information System (INIS)
Pereira, Augusto; Vega, Jesús; Moreno, Raúl; Dormido-Canto, Sebastián; Rattá, Giuseppe A.; Pavón, Fernando
2015-01-01
Recently, a probabilistic classifier has been developed at JET to be used as predictor from scratch. It has been applied to a database of 1237 JET ITER-like wall (ILW) discharges (of which 201 disrupted) with good results: success rate of 94% and false alarm rate of 4.21%. A combinatorial analysis between 14 features to ensure the selection of the best ones to achieve good enough results in terms of success rate and false alarm rate was performed. All possible combinations with a number of features between 2 and 7 were tested and 9893 different predictors were analyzed. An important drawback in this analysis was the time required to compute the results that can be estimated in 1731 h (∼2.4 months). Genetic algorithms (GA) are searching algorithms that simulate the process of natural selection. In this article, the GA and the Venn predictors are combined with the objective not only of finding good enough features within the 14 available ones but also of reducing the computational time requirements. Five different performance metrics as measures of the GA fitness function have been evaluated. The best metric was the measurement called Informedness, with just 6 generations (168 predictors at 29.4 h).
New Optimization Algorithms in Physics
Hartmann, Alexander K
2004-01-01
Many physicists are not aware of the fact that they can solve their problems by applying optimization algorithms. Since the number of such algorithms is steadily increasing, many new algorithms have not been presented comprehensively until now. This presentation of recently developed algorithms applied in physics, including demonstrations of how they work and related results, aims to encourage their application, and as such the algorithms selected cover concepts and methods from statistical physics to optimization problems emerging in theoretical computer science.
International Nuclear Information System (INIS)
Alpman, Emre
2014-01-01
The effect of selecting the twist angle and chord length distributions on the wind turbine blade design was investigated by performing aerodynamic optimization of a two-bladed stall regulated horizontal axis wind turbine. Twist angle and chord length distributions were defined using Bezier curve using 3, 5, 7 and 9 control points uniformly distributed along the span. Optimizations performed using a micro-genetic algorithm with populations composed of 5, 10, 15, 20 individuals showed that, the number of control points clearly affected the outcome of the process; however the effects were different for different population sizes. The results also showed the superiority of micro-genetic algorithm over a standard genetic algorithm, for the selected population sizes. Optimizations were also performed using a macroevolutionary algorithm and the resulting best blade design was compared with that yielded by micro-genetic algorithm
A New Spectral Shape-Based Record Selection Approach Using Np and Genetic Algorithms
Directory of Open Access Journals (Sweden)
Edén Bojórquez
2013-01-01
Full Text Available With the aim to improve code-based real records selection criteria, an approach inspired in a parameter proxy of spectral shape, named Np, is analyzed. The procedure is based on several objectives aimed to minimize the record-to-record variability of the ground motions selected for seismic structural assessment. In order to select the best ground motion set of records to be used as an input for nonlinear dynamic analysis, an optimization approach is applied using genetic algorithms focuse on finding the set of records more compatible with a target spectrum and target Np values. The results of the new Np-based approach suggest that the real accelerograms obtained with this procedure, reduce the scatter of the response spectra as compared with the traditional approach; furthermore, the mean spectrum of the set of records is very similar to the target seismic design spectrum in the range of interest periods, and at the same time, similar Np values are obtained for the selected records and the target spectrum.
Improved feature selection based on genetic algorithms for real time disruption prediction on JET
Energy Technology Data Exchange (ETDEWEB)
Ratta, G.A., E-mail: garatta@gateme.unsj.edu.ar [GATEME, Facultad de Ingenieria, Universidad Nacional de San Juan, Avda. San Martin 1109 (O), 5400 San Juan (Argentina); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Avda. Complutense, 40, 28040 Madrid (Spain); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom)
2012-09-15
Highlights: Black-Right-Pointing-Pointer A new signal selection methodology to improve disruption prediction is reported. Black-Right-Pointing-Pointer The approach is based on Genetic Algorithms. Black-Right-Pointing-Pointer An advanced predictor has been created with the new set of signals. Black-Right-Pointing-Pointer The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called 'Advanced Predictor Of Disruptions' (APODIS), developed for the 'Joint European Torus' (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals' parameters in order to maximize the performance of the predictor is reported. The approach is based on 'Genetic Algorithms' (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.
Mahalakshmi; Murugesan, R.
2018-04-01
This paper regards with the minimization of total cost of Greenhouse Gas (GHG) efficiency in Automated Storage and Retrieval System (AS/RS). A mathematical model is constructed based on tax cost, penalty cost and discount cost of GHG emission of AS/RS. A two stage algorithm namely positive selection based clonal selection principle (PSBCSP) is used to find the optimal solution of the constructed model. In the first stage positive selection principle is used to reduce the search space of the optimal solution by fixing a threshold value. In the later stage clonal selection principle is used to generate best solutions. The obtained results are compared with other existing algorithms in the literature, which shows that the proposed algorithm yields a better result compared to others.
Soufan, Othman
2012-09-01
Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics.
Directory of Open Access Journals (Sweden)
Mohammad Saied Fallah Niasar
2017-02-01
Full Text Available he school bus routing problem (SBRP represents a variant of the well-known vehicle routing problem. The main goal of this study is to pick up students allocated to some bus stops and generate routes, including the selected stops, in order to carry students to school. In this paper, we have proposed a simple but effective metaheuristic approach that employs two features: first, it utilizes large neighborhood structures for a deeper exploration of the search space; second, the proposed heuristic executes an efficient transition between the feasible and infeasible portions of the search space. Exploration of the infeasible area is controlled by a dynamic penalty function to convert the unfeasible solution into a feasible one. Two metaheuristics, called N-ILS (a variant of the Nearest Neighbourhood with Iterated Local Search algorithm and I-ILS (a variant of Insertion with Iterated Local Search algorithm are proposed to solve SBRP. Our experimental procedure is based on the two data sets. The results show that N-ILS is able to obtain better solutions in shorter computing times. Additionally, N-ILS appears to be very competitive in comparison with the best existing metaheuristics suggested for SBRP
ACTION OF UNIFORM SEARCH ALGORITHM WHEN SELECTING LANGUAGE UNITS IN THE PROCESS OF SPEECH
Directory of Open Access Journals (Sweden)
Ирина Михайловна Некипелова
2013-05-01
Full Text Available The article is devoted to research of action of uniform search algorithm when selecting by human of language units for speech produce. The process is connected with a speech optimization phenomenon. This makes it possible to shorten the time of cogitation something that human want to say, and to achieve the maximum precision in thoughts expression. The algorithm of uniform search works at consciousness and subconsciousness levels. It favours the forming of automatism produce and perception of speech. Realization of human's cognitive potential in the process of communication starts up complicated mechanism of self-organization and self-regulation of language. In turn, it results in optimization of language system, servicing needs not only human's self-actualization but realization of communication in society. The method of problem-oriented search is used for researching of optimization mechanisms, which are distinctive to speech producing and stabilization of language.DOI: http://dx.doi.org/10.12731/2218-7405-2013-4-50
Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.
1991-01-01
A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.
International Nuclear Information System (INIS)
Gomes, Alvaro; Antunes, Carlos Henggeler; Martins, Antonio Gomes
2005-01-01
This paper aims at presenting a multiple objective model to evaluate the attractiveness of the use of demand resources (through load management control actions) by different stakeholders and in diverse structure scenarios in electricity systems. For the sake of model flexibility, the multiple (and conflicting) objective functions of technical, economical and quality of service nature are able to capture distinct market scenarios and operating entities that may be interested in promoting load management activities. The computation of compromise solutions is made by resorting to evolutionary algorithms, which are well suited to tackle multiobjective problems of combinatorial nature herein involving the identification and selection of control actions to be applied to groups of loads. (Author)
Besio, Walter G; Cao, Hongbao; Zhou, Peng
2008-04-01
For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.
Latent Dirichlet Allocation (LDA) Model and kNN Algorithm to Classify Research Project Selection
Safi’ie, M. A.; Utami, E.; Fatta, H. A.
2018-03-01
Universitas Sebelas Maret has a teaching staff more than 1500 people, and one of its tasks is to carry out research. In the other side, the funding support for research and service is limited, so there is need to be evaluated to determine the Research proposal submission and devotion on society (P2M). At the selection stage, research proposal documents are collected as unstructured data and the data stored is very large. To extract information contained in the documents therein required text mining technology. This technology applied to gain knowledge to the documents by automating the information extraction. In this articles we use Latent Dirichlet Allocation (LDA) to the documents as a model in feature extraction process, to get terms that represent its documents. Hereafter we use k-Nearest Neighbour (kNN) algorithm to classify the documents based on its terms.
A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.
Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing
2017-01-01
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.
Development of algorithms for real time track selection in the TOTEM experiment
Minafra, Nicola; Radicioni, E
The TOTEM experiment at the LHC has been designed to measure the total proton-proton cross-section with a luminosity independent method and to study elastic and diffractive scattering at energy up to 14 TeV in the center of mass. Elastic interactions are detected by Roman Pot stations, placed at 147m and 220m along the two exiting beams. At the present time, data acquired by these detectors are stored on disk without any data reduction by the data acquisition chain. In this thesis several tracking and selection algorithms, suitable for real-time implementation in the firmware of the back-end electronics, have been proposed and tested using real data.
Energy Technology Data Exchange (ETDEWEB)
None
1976-12-01
This report has been prepared with the objective of providing ERDA with recommended procedures for implementing the strategies set forth in the Systems Level Plan which are considered essential to the success of the National Demonstration Program. In order to logically develop these recommendations, this report is divided into three sections: A. Overview of the Demonstration Program to date. B. Essential Overall Program Strategies. C. Candidate Screening and Selection Criteria. (WDM)
Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms
Directory of Open Access Journals (Sweden)
Shengyun Liang
2015-11-01
Full Text Available The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers participated in functional movement tests, including walking and sit-to-stand (STS. A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN, pseudo nearest neighbor (PNN, local mean pseudo nearest neighbor (LMPNN classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.
An Evaluation of Media-Oriented Rate Selection Algorithm for Multimedia Transmission in MANETs
Directory of Open Access Journals (Sweden)
Guionnet Thomas
2005-01-01
Full Text Available We focus on the optimization of real-time multimedia transmission over 802.11-based ad hoc networks. In particular, we propose a simple and efficient cross-layer mechanism that considers both the channel conditions and characteristics of the media for dynamically selecting the transmission mode. This mechanism called media-oriented rate selection algorithm (MORSA targets loss-tolerant applications such as VoD that do not require full reliable transmission. We provide an evaluation of this mechanism for MANETs using simulations with NS and analyze the video quality obtained with a fine-grain scalable video encoder based on a motion-compensated spatiotemporal wavelet transform. Our results show that MORSA achieves up to 4 Mbps increase in throughput and that the routing overhead decreases significantly. Transmission of a sample video flow over an 802.11a wireless channel has been evaluated with MORSA. Important improvement is observed in throughput, latency, and jitter while keeping a good level of video quality.
Recursive forgetting algorithms
DEFF Research Database (Denmark)
Parkum, Jens; Poulsen, Niels Kjølstad; Holst, Jan
1992-01-01
In the first part of the paper, a general forgetting algorithm is formulated and analysed. It contains most existing forgetting schemes as special cases. Conditions are given ensuring that the basic convergence properties will hold. In the second part of the paper, the results are applied...... to a specific algorithm with selective forgetting. Here, the forgetting is non-uniform in time and space. The theoretical analysis is supported by a simulation example demonstrating the practical performance of this algorithm...
System Guidelines for EMC Safety-Critical Circuits: Design, Selection, and Margin Demonstration
Lawton, R. M.
1996-01-01
Demonstration of safety margins for critical points (circuits) has traditionally been required since it first became a part of systems-level Electromagnetic Compatibility (EMC) requirements of MIL-E-6051C. The goal of this document is to present cost-effective guidelines for ensuring adequate Electromagnetic Effects (EME) safety margins on spacecraft critical circuits. It is for the use of NASA and other government agencies and their contractors to prevent loss of life, loss of spacecraft, or unacceptable degradation. This document provides practical definition and treatment guidance to contain costs within affordable limits.
Directory of Open Access Journals (Sweden)
Khalid Qaraqe
2008-10-01
Full Text Available This paper proposes a novel vertical handoff algorithm between WLAN and CDMA networks to enable the integration of these networks. The proposed vertical handoff algorithm assumes a handoff decision process (handoff triggering and network selection. The handoff trigger is decided based on the received signal strength (RSS. To reduce the likelihood of unnecessary false handoffs, the distance criterion is also considered. As a network selection mechanism, based on the wireless channel assignment algorithm, this paper proposes a context-based network selection algorithm and the corresponding communication algorithms between WLAN and CDMA networks. This paper focuses on a handoff triggering criterion which uses both the RSS and distance information, and a network selection method which uses context information such as the dropping probability, blocking probability, GoS (grade of service, and number of handoff attempts. As a decision making criterion, the velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations using four handoff strategies. The four handoff strategies are evaluated and compared with each other in terms of GOS. Finally, the proposed scheme is validated by computer simulations.
Directory of Open Access Journals (Sweden)
Kim Jang-Sub
2008-01-01
Full Text Available This paper proposes a novel vertical handoff algorithm between WLAN and CDMA networks to enable the integration of these networks. The proposed vertical handoff algorithm assumes a handoff decision process (handoff triggering and network selection. The handoff trigger is decided based on the received signal strength (RSS. To reduce the likelihood of unnecessary false handoffs, the distance criterion is also considered. As a network selection mechanism, based on the wireless channel assignment algorithm, this paper proposes a context-based network selection algorithm and the corresponding communication algorithms between WLAN and CDMA networks. This paper focuses on a handoff triggering criterion which uses both the RSS and distance information, and a network selection method which uses context information such as the dropping probability, blocking probability, GoS (grade of service, and number of handoff attempts. As a decision making criterion, the velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations using four handoff strategies. The four handoff strategies are evaluated and compared with each other in terms of GOS. Finally, the proposed scheme is validated by computer simulations.
Directory of Open Access Journals (Sweden)
Chiang Mung
2006-01-01
Full Text Available The problem of choosing the optimal multipath components to be employed at a minimum mean square error (MMSE selective Rake receiver is considered for an impulse radio ultra-wideband system. First, the optimal finger selection problem is formulated as an integer programming problem with a nonconvex objective function. Then, the objective function is approximated by a convex function and the integer programming problem is solved by means of constraint relaxation techniques. The proposed algorithms are suboptimal due to the approximate objective function and the constraint relaxation steps. However, they perform better than the conventional finger selection algorithm, which is suboptimal since it ignores the correlation between multipath components, and they can get quite close to the optimal scheme that cannot be implemented in practice due to its complexity. In addition to the convex relaxation techniques, a genetic-algorithm- (GA- based approach is proposed, which does not need any approximations or integer relaxations. This iterative algorithm is based on the direct evaluation of the objective function, and can achieve near-optimal performance with a reasonable number of iterations. Simulation results are presented to compare the performance of the proposed finger selection algorithms with that of the conventional and the optimal schemes.
Improved feature selection based on genetic algorithms for real time disruption prediction on JET
International Nuclear Information System (INIS)
Rattá, G.A.; Vega, J.; Murari, A.
2012-01-01
Highlights: ► A new signal selection methodology to improve disruption prediction is reported. ► The approach is based on Genetic Algorithms. ► An advanced predictor has been created with the new set of signals. ► The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called “Advanced Predictor Of Disruptions” (APODIS), developed for the “Joint European Torus” (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals’ parameters in order to maximize the performance of the predictor is reported. The approach is based on “Genetic Algorithms” (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.
Crump, Matthew J C
2016-03-01
Multiple lines of evidence from the attention and performance literature show that attention filtering can be controlled by higher level voluntary processes and lower-level cue-driven processes (for recent reviews see Bugg, 2012; Bugg & Crump, 2012; Egner, 2008). The experiments were designed to test a general hypothesis that cue-driven control learns from context-specific histories of prior acts of selective attention. Several web-based flanker studies were conducted via Amazon Mechanical Turk. Attention filtering demands were induced by a secondary one-back memory task after each trial prompting recall of the last target or distractor letter. Blocking recall demands produced larger flanker effects for the distractor than target recall conditions. Mixing recall demands and associating them with particular stimulus-cues (location, colour, letter, and font) sometimes showed rapid, contextual control of flanker interference, and sometimes did not. The results show that subtle methodological parameters can influence whether or not contextual control is observed. More generally, the results show that contextual control phenomena can be influenced by other sources of control, including other cue-driven sources competing for control. (c) 2016 APA, all rights reserved).
Selective automation and skill transfer in medical robotics: a demonstration on surgical knot-tying.
Knoll, Alois; Mayer, Hermann; Staub, Christoph; Bauernschmitt, Robert
2012-12-01
Transferring non-trivial human manipulation skills to robot systems is a challenging task. There have been a number of attempts to design research systems for skill transfer, but the level of the complexity of the actual skills transferable to the robot was rather limited, and delicate operations requiring a high dexterity and long action sequences with many sub-operations were impossible to transfer. A novel approach to human-machine skill transfer for multi-arm robot systems is presented. The methodology capitalizes on the metaphor of 'scaffolded learning', which has gained widespread acceptance in psychology. The main idea is to formalize the superior knowledge of a teacher in a certain way to generate support for a trainee. In our case, the scaffolding is constituted by abstract patterns, which facilitate the structuring and segmentation of information during 'learning by demonstration'. The actual skill generalization is then based on simulating fluid dynamics. The approach has been successfully evaluated in the medical domain for the delicate task of automated knot-tying for suturing with standard surgical instruments and a realistic minimally invasive robotic surgery system. Copyright © 2012 John Wiley & Sons, Ltd.
Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz
2015-10-06
In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.
Directory of Open Access Journals (Sweden)
Ji She
2016-12-01
Full Text Available Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance.
Keller, M. M.; d'Oliveira, M. N.; Takemura, C. M.; Vitoria, D.; Araujo, L. S.; Morton, D. C.
2012-12-01
Selective logging, the removal of several valuable timber trees per hectare, is an important land use in the Brazilian Amazon and may degrade forests through long term changes in structure, loss of forest carbon and species diversity. Similar to deforestation, the annual area affected by selected logging has declined significantly in the past decade. Nonetheless, this land use affects several thousand km2 per year in Brazil. We studied a 1000 ha area of the Antimary State Forest (FEA) in the State of Acre, Brazil (9.304 ○S, 68.281 ○W) that has a basal area of 22.5 m2 ha-1 and an above-ground biomass of 231 Mg ha-1. Logging intensity was low, approximately 10 to 15 m3 ha-1. We collected small-footprint airborne lidar data using an Optech ALTM 3100EA over the study area once each in 2010 and 2011. The study area contained both recent and older logging that used both conventional and technologically advanced logging techniques. Lidar return density averaged over 20 m-2 for both collection periods with estimated horizontal and vertical precision of 0.30 and 0.15 m. A relative density model comparing returns from 0 to 1 m elevation to returns in 1-5 m elevation range revealed the pattern of roads and skid trails. These patterns were confirmed by ground-based GPS survey. A GIS model of the road and skid network was built using lidar and ground data. We tested and compared two pattern recognition approaches used to automate logging detection. Both segmentation using commercial eCognition segmentation and a Frangi filter algorithm identified the road and skid trail network compared to the GIS model. We report on the effectiveness of these two techniques.
A genetic algorithm-based framework for wavelength selection on sample categorization.
Anzanello, Michel J; Yamashita, Gabrielli; Marcelo, Marcelo; Fogliatto, Flávio S; Ortiz, Rafael S; Mariotti, Kristiane; Ferrão, Marco F
2017-08-01
In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
International Nuclear Information System (INIS)
Lins, Isis Didier; Rêgo, Leandro Chaves; Moura, Márcio das Chagas
2013-01-01
This work analyzes the strategic interaction between a defender and an intelligent attacker by means of a game and reliability framework involving a multi-objective approach and imperfect information so as to support decision-makers in choosing efficiently designed security systems. A multi-objective genetic algorithm is used to determine the optimal security system's configurations representing the tradeoff between the probability of a successful defense and the acquisition and operational costs. Games with imperfect information are considered, in which the attacker has limited knowledge about the actual security system. The types of security alternatives are readily observable, but the number of redundancies actually implemented in each security subsystem is not known. The proposed methodology is applied to an illustrative example considering power transmission lines in the Northeast of Brazil, which are often targets for attackers who aims at selling the aluminum conductors. The empirical results show that the framework succeeds in handling this sort of strategic interaction. -- Highlights: ► Security components must have feasible costs and must be reliable. ► The optimal design of security systems considers a multi-objective approach. ► Games of imperfect information enable the choice of non-dominated configurations. ► MOGA, reliability and games support the entire defender's decision process. ► The selection of effective security systems may discourage attacker's actions
Selection Algorithm for the CALIPSO Lidar Aerosol Extinction-to-Backscatter Ratio
Omar, Ali H.; Winker, David M.; Vaughan, Mark A.
2006-01-01
The extinction-to-backscatter ratio (S(sub a)) is an important parameter used in the determination of the aerosol extinction and subsequently the optical depth from lidar backscatter measurements. We outline the algorithm used to determine Sa for the Cloud and Aerosol Lidar and Infrared Pathfinder Spaceborne Observations (CALIPSO) lidar. S(sub a) for the CALIPSO lidar will either be selected from a look-up table or calculated using the lidar measurements depending on the characteristics of aerosol layer. Whenever suitable lofted layers are encountered, S(sub a) is computed directly from the integrated backscatter and transmittance. In all other cases, the CALIPSO observables: the depolarization ratio, delta, the layer integrated attenuated backscatter, beta, and the mean layer total attenuated color ratio, gamma, together with the surface type, are used to aid in aerosol typing. Once the type is identified, a look-up-table developed primarily from worldwide observations, is used to determine the S(sub a) value. The CALIPSO aerosol models include desert dust, biomass burning, background, polluted continental, polluted dust, and marine aerosols.
Energy Technology Data Exchange (ETDEWEB)
Anusionwu, Princess [Medical Physics, CancerCare Manitoba, Winnipeg Canada (Canada); Department of Physics & Astronomy, University of Manitoba, Winnipeg Canada (Canada); Alpuche Aviles, Jorge E. [Medical Physics, CancerCare Manitoba, Winnipeg Canada (Canada); Pistorius, Stephen [Medical Physics, CancerCare Manitoba, Winnipeg Canada (Canada); Department of Physics & Astronomy, University of Manitoba, Winnipeg Canada (Canada); Department of Radiology, University of Manitoba, Winnipeg (Canada)
2016-08-15
Objective: Commissioning of a Monte Carlo based electron dose calculation algorithm requires percentage depth doses (PDDs) and beam profiles which can be measured with multiple detectors. Electron dosimetry is commonly performed with cylindrical chambers but parallel plate chambers and diodes can also be used. The purpose of this study was to determine the most appropriate detector to perform the commissioning measurements. Methods: PDDs and beam profiles were measured for beams with energies ranging from 6 MeV to 15 MeV and field sizes ranging from 6 cm × 6 cm to 40 cm × 40 cm. Detectors used included diodes, cylindrical and parallel plate ionization chambers. Beam profiles were measured in water (100 cm source to surface distance) and in air (95 cm source to detector distance). Results: PDDs for the cylindrical chambers were shallower (1.3 mm averaged over all energies and field sizes) than those measured with the parallel plate chambers and diodes. Surface doses measured with the diode and cylindrical chamber were on average larger by 1.6 % and 3% respectively than those of the parallel plate chamber. Profiles measured with a diode resulted in penumbra values smaller than those measured with the cylindrical chamber by 2 mm. Conclusion: The diode was selected as the most appropriate detector since PDDs agreed with those measured with parallel plate chambers (typically recommended for low energies) and results in sharper profiles. Unlike ion chambers, no corrections are needed to measure PDDs, making it more convenient to use.
A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era
Directory of Open Access Journals (Sweden)
Fangdong Zhu
2017-01-01
Full Text Available Negative selection algorithm (NSA is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional “Random-Discard” model to the “Computing-Designated” model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.
Scarino, B. R.; Minnis, P.; Yost, C. R.; Chee, T.; Palikonda, R.
2015-12-01
Single-channel algorithms for satellite thermal-infrared- (TIR-) derived land and sea surface skin temperature (LST and SST) are advantageous in that they can be easily applied to a variety of satellite sensors. They can also accommodate decade-spanning instrument series, particularly for periods when split-window capabilities are not available. However, the benefit of one unified retrieval methodology for all sensors comes at the cost of critical sensitivity to surface emissivity (ɛs) and atmospheric transmittance estimation. It has been demonstrated that as little as 0.01 variance in ɛs can amount to more than a 0.5-K adjustment in retrieved LST values. Atmospheric transmittance requires calculations that employ vertical profiles of temperature and humidity from numerical weather prediction (NWP) models. Selection of a given NWP model can significantly affect LST and SST agreement relative to their respective validation sources. Thus, it is necessary to understand the accuracies of the retrievals for various NWP models to ensure the best LST/SST retrievals. The sensitivities of the single-channel retrievals to surface emittance and NWP profiles are investigated using NASA Langley historic land and ocean clear-sky skin temperature (Ts) values derived from high-resolution 11-μm TIR brightness temperature measured from geostationary satellites (GEOSat) and Advanced Very High Resolution Radiometers (AVHRR). It is shown that mean GEOSat-derived, anisotropy-corrected LST can vary by up to ±0.8 K depending on whether CERES or MODIS ɛs sources are used. Furthermore, the use of either NOAA Global Forecast System (GFS) or NASA Goddard Modern-Era Retrospective Analysis for Research and Applications (MERRA) for the radiative transfer model initial atmospheric state can account for more than 0.5-K variation in mean Ts. The results are compared to measurements from the Surface Radiation Budget Network (SURFRAD), an Atmospheric Radiation Measurement (ARM) Program ground
Yavari, Somayeh; Valadan Zoej, Mohammad Javad; Salehi, Bahram
2018-05-01
The procedure of selecting an optimum number and best distribution of ground control information is important in order to reach accurate and robust registration results. This paper proposes a new general procedure based on Genetic Algorithm (GA) which is applicable for all kinds of features (point, line, and areal features). However, linear features due to their unique characteristics are of interest in this investigation. This method is called Optimum number of Well-Distributed ground control Information Selection (OWDIS) procedure. Using this method, a population of binary chromosomes is randomly initialized. The ones indicate the presence of a pair of conjugate lines as a GCL and zeros specify the absence. The chromosome length is considered equal to the number of all conjugate lines. For each chromosome, the unknown parameters of a proper mathematical model can be calculated using the selected GCLs (ones in each chromosome). Then, a limited number of Check Points (CPs) are used to evaluate the Root Mean Square Error (RMSE) of each chromosome as its fitness value. The procedure continues until reaching a stopping criterion. The number and position of ones in the best chromosome indicate the selected GCLs among all conjugate lines. To evaluate the proposed method, a GeoEye and an Ikonos Images are used over different areas of Iran. Comparing the obtained results by the proposed method in a traditional RFM with conventional methods that use all conjugate lines as GCLs shows five times the accuracy improvement (pixel level accuracy) as well as the strength of the proposed method. To prevent an over-parametrization error in a traditional RFM due to the selection of a high number of improper correlated terms, an optimized line-based RFM is also proposed. The results show the superiority of the combination of the proposed OWDIS method with an optimized line-based RFM in terms of increasing the accuracy to better than 0.7 pixel, reliability, and reducing systematic
Optimization of Selected Remote Sensing Algorithms for Embedded NVIDIA Kepler GPU Architecture
Riha, Lubomir; Le Moigne, Jacqueline; El-Ghazawi, Tarek
2015-01-01
This paper evaluates the potential of embedded Graphic Processing Units in the Nvidias Tegra K1 for onboard processing. The performance is compared to a general purpose multi-core CPU and full fledge GPU accelerator. This study uses two algorithms: Wavelet Spectral Dimension Reduction of Hyperspectral Imagery and Automated Cloud-Cover Assessment (ACCA) Algorithm. Tegra K1 achieved 51 for ACCA algorithm and 20 for the dimension reduction algorithm, as compared to the performance of the high-end 8-core server Intel Xeon CPU with 13.5 times higher power consumption.
International Nuclear Information System (INIS)
1993-01-01
In August 1991, the Joint Contractors came to agreement with Sandia National Laboratories (SNL) and the Department of Energy (DOE) on a workscope for the cost-shared Early Site Permit Demonstration Program. One task within the scope was the development of a guide for site selection criteria and procedures. A generic Siting Guide his been prepared that is a roadmap and tool for applicants to use developing detailed siting plans for their specific region of the country. The guide presents three fundamental principles that, if used, ensure a high degree of success for an ESP applicant. First, the site selection process should take into consideration environmentally diverse site locations within a given region of interest. Second, the process should contain appropriate opportunities for input from the public. Third, the process should be applied so that it is clearly reasonable to an impartial observer, based on appropriately selected criteria, including criteria which demonstrate that the site can host an advanced light water reactor (ALWR). The Siting Guide provides for a systematic, comprehensive site selection process in which three basic types of criteria (exclusionary, avoidance, and suitability) are presented via a four-step procedure. It provides a check list of the criteria for each one of these steps. Criteria are applied qualitatively, as well as presented numerically, within the guide. The applicant should use the generic guide as an exhaustive checklist, customizing the guide to his individual situation
Energy Technology Data Exchange (ETDEWEB)
1993-03-24
In August 1991, the Joint Contractors came to agreement with Sandia National Laboratories (SNL) and the Department of Energy (DOE) on a workscope for the cost-shared Early Site Permit Demonstration Program. One task within the scope was the development of a guide for site selection criteria and procedures. A generic Siting Guide his been prepared that is a roadmap and tool for applicants to use developing detailed siting plans for their specific region of the country. The guide presents three fundamental principles that, if used, ensure a high degree of success for an ESP applicant. First, the site selection process should take into consideration environmentally diverse site locations within a given region of interest. Second, the process should contain appropriate opportunities for input from the public. Third, the process should be applied so that it is clearly reasonable to an impartial observer, based on appropriately selected criteria, including criteria which demonstrate that the site can host an advanced light water reactor (ALWR). The Siting Guide provides for a systematic, comprehensive site selection process in which three basic types of criteria (exclusionary, avoidance, and suitability) are presented via a four-step procedure. It provides a check list of the criteria for each one of these steps. Criteria are applied qualitatively, as well as presented numerically, within the guide. The applicant should use the generic guide as an exhaustive checklist, customizing the guide to his individual situation.
Directory of Open Access Journals (Sweden)
Taegwon Jeong
2011-05-01
Full Text Available 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.
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.
Xiang, Min; Qu, Qinqin; Chen, Cheng; Tian, Li; Zeng, Lingkang
2017-11-01
To improve the reliability of communication service in smart distribution grid (SDG), an access selection algorithm based on dynamic network status and different service types for heterogeneous wireless networks was proposed. The network performance index values were obtained in real time by multimode terminal and the variation trend of index values was analyzed by the growth matrix. The index weights were calculated by entropy-weight and then modified by rough set to get the final weights. Combining the grey relational analysis to sort the candidate networks, and the optimum communication network is selected. Simulation results show that the proposed algorithm can implement dynamically access selection in heterogeneous wireless networks of SDG effectively and reduce the network blocking probability.
Kipli, Kuryati; Kouzani, Abbas Z
2015-07-01
Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression. A feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation. The DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score. DoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.
Directory of Open Access Journals (Sweden)
HwiRim Byun
2016-05-01
Full Text Available Following recent technological advances in diverse mobile devices, including smartphones, tablets and smartwatches, in-depth studies aimed at improving the quality of augmented reality (AR are currently ongoing. Smartphones feature the essential elements of AR implementation, such as a camera, a processor and a display in a single device. As a result, additional hardware expansion for AR implementation has become unnecessary, popularizing AR technology at the user level. In the early stages, low-level AR technology was used mainly in limited fields, including simple road guides and marker-based recognition. Due to advances in AR technology, the range of usage has expanded as diverse technologies and purposes are combined. Users’ expectations of AR technology have also increased with this trend, and a high quality of service (QoS, with high-resolution, high-quality images, is now available. However, there are limitations in terms of processing speed and graphic treatment with smart devices, which, due to their small size, have inferior performance compared to the desktop environment when processing data for the implementation of high-resolution, high-quality images. This paper proposes an optional frame-selection algorithm (OFSA, which eliminates the unnecessary work involved with redundant frames during rendering for adaptive symmetric service of augmented reality big data on smart devices. Moreover, the memory read-write delay of the internally-operating OFSA, is minimized by adding an adaptive operation function. It is possible to provide adaptive common AR images at an improved frame rate in heterogeneous smart devices with different levels of performance.
Directory of Open Access Journals (Sweden)
S. Alireza Mohades Kasaei
2010-04-01
Full Text Available Robocup is an international competition for multi agent research and related subject like: Artificial intelligence, Image processing, machine learning, robot path planning, control, and
obstacle avoidance. In a soccer robot game, the environment is highly competitive and dynamic. In order to work in the dynamically changing environment, the decision-making system of a soccer robot system should have the features of flexibility and real-time adaptation. In this paper we will
focus on the Middle Size Soccer Robot league (MSL and new hierarchical hybrid fuzzy methods for decision making and action selection of a robot in Middle Size Soccer Robot league (MSL are presented. First, the behaviors of an agent are introduced, implemented and classified in two layers,
the Low_Level_Behaviors and the High_Level_Behaviors. In the second layer, a two phase mechanism for decision making is introduced. In phase one, some useful methods are implemented which check the robot’s situation for performing required behaviors. In the next phase, the team strategy, team formation, robot’s role and the robot’s positioning system are introduced. A fuzzy logical approach is employed to recognize the team strategy and further more to tell the player the
best position to move. We believe that a Dynamic role engine is necessary for a successful team. Dynamic role engine and formation control during offensive or defensive play, help us to prevent collision avoidance among own players when attacking the ball and obstacle avoidance of the opponents. At last, we comprised our implemented algorithm in the Robocup 2007 and 2008 and results showed the efficiency of the introduced methodology. The results are satisfactory which has already been successfully implemented in ADRO RoboCup team. This project is still in progress and some new interesting methods are described in the current report.
Directory of Open Access Journals (Sweden)
Mariela Cerrada
2015-09-01
Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.
Moore, Timothy S; Dowell, Mark D; Bradt, Shane; Verdu, Antonio Ruiz
2014-03-05
Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll- a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll- a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll- a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll- a algorithms-the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands-with RMS error of 0.416 and 0.437 for each in log chlorophyll- a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll- a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll- a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.
Energy Technology Data Exchange (ETDEWEB)
Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.
2017-07-01
The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world
Directory of Open Access Journals (Sweden)
Muhammad Hashim
2017-05-01
Full Text Available Purpose: The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM. Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM with fuzzy coefficients constructed in present research should be helpful for
International Nuclear Information System (INIS)
Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.
2017-01-01
The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world
CSIR Research Space (South Africa)
Abu-Mahfouz, Adnan M
2013-05-01
Full Text Available Determining the location of nodes is a key part of wireless sensor networks (WSNs). Many WSN applications require knowledge of nodes’ locations to perform their functions successfully. Several localisation algorithms rely on using all or most...
Elsheikh, A. H.; Wheeler, M. F.; Hoteit, Ibrahim
2013-01-01
Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known
International Nuclear Information System (INIS)
Kurt, Ünal
2014-01-01
The location selection for nuclear power plant (NPP) is a strategic decision, which has significant impact on the economic operation of the plant and sustainable development of the region. This paper proposes fuzzy TOPSIS and generalized Choquet fuzzy integral algorithm for evaluation and selection of optimal locations for NPP in Turkey. Many sub-criteria such as geological, social, touristic, transportation abilities, cooling water capacity and nearest to consumptions markets are taken into account. Among the evaluated locations, according to generalized Choquet fuzzy integral method, Inceburun–Sinop was selected as a study site due to its highest performance and meeting most of the investigated criteria. The Inceburun-Sinop is selected by generalized Choquet fuzzy integral and fuzzy TOPSIS Iğneada–Kırklareli took place in the first turn. The Mersin–Akkuyu is not selected in both methods. (author)
International Nuclear Information System (INIS)
1979-01-01
A 1972 survey of 30 Ottawa secondary schools revealed a total of 347 actual or potential X-ray sources available in these schools. More than half of these sources were gas discharge tubes. Some gas discharge tubes, in particular the cold cathode type, can emit X-rays at significantly high levels. Unless such tubes are used carefully, and with regard for good radiation safety practices, they can result in exposures to students that are in excess of the maximum levels recommended by the International Commission on Radiological Protection. Several cases of the recommended dose being exceeded were found in the classes surveyed. This document has been prepared to assist science teachers and others using demonstration-type gas discharge devices to select and use such devices so as to present negligible risk to themselves and students. Useful information on safety procedures to be followed when performing demonstrations or experiments is included. (J.T.A.)
International Nuclear Information System (INIS)
Amjady, Nima; Keynia, Farshid
2009-01-01
With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.
Energy Technology Data Exchange (ETDEWEB)
Kwak, Noh Sung; Lee, Jongsoo [Yonsei University, Seoul (Korea, Republic of)
2016-01-15
The present study aims to implement a new selection method and a novel crossover operation in a real-coded genetic algorithm. The proposed selection method facilitates the establishment of a successively evolved population by combining several subpopulations: an elitist subpopulation, an off-spring subpopulation and a mutated subpopulation. A probabilistic crossover is performed based on the measure of probabilistic distance between the individuals. The concept of ‘allowance’ is suggested to describe the level of variance in the crossover operation. A number of nonlinear/non-convex functions and engineering optimization problems are explored to verify the capacities of the proposed strategies. The results are compared with those obtained from other genetic and nature-inspired algorithms.
Directory of Open Access Journals (Sweden)
Tiannan Ma
2016-12-01
Full Text Available Accurate forecasting of icing thickness has great significance for ensuring the security and stability of the power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on the fireworks algorithm and weighted least square support vector machine (W-LSSVM. The method of the fireworks algorithm is employed to select the proper input features with the purpose of eliminating redundant influence. In addition, the aim of the W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through simulation experiments using real-world icing data from the monitoring center of the key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.
Directory of Open Access Journals (Sweden)
Takeshi Kawanishi
Full Text Available Culturing is an indispensable technique in microbiological research, and culturing with selective media has played a crucial role in the detection of pathogenic microorganisms and the isolation of commercially useful microorganisms from environmental samples. Although numerous selective media have been developed in empirical studies, unintended microorganisms often grow on such media probably due to the enormous numbers of microorganisms in the environment. Here, we present a novel strategy for designing highly selective media based on two selective agents, a carbon source and antimicrobials. We named our strategy SMART for highly Selective Medium-design Algorithm Restricted by Two constraints. To test whether the SMART method is applicable to a wide range of microorganisms, we developed selective media for Burkholderia glumae, Acidovorax avenae, Pectobacterium carotovorum, Ralstonia solanacearum, and Xanthomonas campestris. The series of media developed by SMART specifically allowed growth of the targeted bacteria. Because these selective media exhibited high specificity for growth of the target bacteria compared to established selective media, we applied three notable detection technologies: paper-based, flow cytometry-based, and color change-based detection systems for target bacteria species. SMART facilitates not only the development of novel techniques for detecting specific bacteria, but also our understanding of the ecology and epidemiology of the targeted bacteria.
Directory of Open Access Journals (Sweden)
V. Proschek
2011-10-01
Full Text Available Measuring greenhouse gas (GHG profiles with global coverage and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS is key for improved monitoring of GHG concentrations in the free atmosphere. In this respect a new satellite mission concept adding an infrared-laser part to the already well studied microwave occultation technique exploits the joint propagation of infrared-laser and microwave signals between Low Earth Orbit (LEO satellites. This synergetic combination, referred to as LEO-LEO microwave and infrared-laser occultation (LMIO method, enables to retrieve thermodynamic profiles (pressure, temperature, humidity and accurate altitude levels from the microwave signals and GHG profiles from the simultaneously measured infrared-laser signals. However, due to the novelty of the LMIO method, a retrieval algorithm for GHG profiling is not yet available. Here we introduce such an algorithm for retrieving GHGs from LEO-LEO infrared-laser occultation (LIO data, applied as a second step after retrieving thermodynamic profiles from LEO-LEO microwave occultation (LMO data. We thoroughly describe the LIO retrieval algorithm and unveil the synergy with the LMO-retrieved pressure, temperature, and altitude information. We furthermore demonstrate the effective independence of the GHG retrieval results from background (a priori information in discussing demonstration results from LMIO end-to-end simulations for a representative set of GHG profiles, including carbon dioxide (CO_{2}, water vapor (H_{2}O, methane (CH_{4}, and ozone (O_{3}. The GHGs except for ozone are well retrieved throughout the UTLS, while ozone is well retrieved from about 10 km to 15 km upwards, since the ozone layer resides in the lower stratosphere. The GHG retrieval errors are generally smaller than 1% to 3% r.m.s., at a vertical resolution of about 1 km. The retrieved profiles also appear unbiased, which points
Eigenvector Subset Selection Using Bayesian Optimization Algorithm%基于贝叶斯优化算法的脸面特征向量子集选择
Institute of Scientific and Technical Information of China (English)
郭卫锋; 林亚平; 罗光平
2002-01-01
Eigenvector subset selection is the key to face recognition. In this paper ,we propose ESS-BOA, a newrandomized, population-based evolutionary algorithm which deals with the Eigenvector Subset Selection (ESS)prob-lem on face recognition application. In ESS-BOA ,the ESS problem, stated as a search problem ,uses the BayesianOptimization Algorithm (BOA) as searching engine and the distance degree as the object function to select eigenvec-tor. Experimental results show that ESS-BOA outperforms the traditional the eigenface selection algorithm.
International Nuclear Information System (INIS)
Rippe, Lars; Nilsson, Mattias; Kroell, Stefan; Klieber, Robert; Suter, Dieter
2005-01-01
In optically controlled quantum computers it may be favorable to address different qubits using light with different frequencies, since the optical diffraction does not then limit the distance between qubits. Using qubits that are close to each other enables qubit-qubit interactions and gate operations that are strong and fast in comparison to qubit-environment interactions and decoherence rates. However, as qubits are addressed in frequency space, great care has to be taken when designing the laser pulses, so that they perform the desired operation on one qubit, without affecting other qubits. Complex hyperbolic secant pulses have theoretically been shown to be excellent for such frequency-addressed quantum computing [I. Roos and K. Molmer, Phys. Rev. A 69, 022321 (2004)] - e.g., for use in quantum computers based on optical interactions in rare-earth-metal-ion-doped crystals. The optical transition lines of the rare-earth-metal-ions are inhomogeneously broadened and therefore the frequency of the excitation pulses can be used to selectively address qubit ions that are spatially separated by a distance much less than a wavelength. Here, frequency-selective transfer of qubit ions between qubit states using complex hyperbolic secant pulses is experimentally demonstrated. Transfer efficiencies better than 90% were obtained. Using the complex hyperbolic secant pulses it was also possible to create two groups of ions, absorbing at specific frequencies, where 85% of the ions at one of the frequencies was shifted out of resonance with the field when ions in the other frequency group were excited. This procedure of selecting interacting ions, called qubit distillation, was carried out in preparation for two-qubit gate operations in the rare-earth-metal-ion-doped crystals. The techniques for frequency-selective state-to-state transfer developed here may be also useful also for other quantum optics and quantum information experiments in these long-coherence-time solid
Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun
2017-11-27
Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.
Nankali, Saber; Torshabi, Ahmad Esmaili; Miandoab, Payam Samadi; Baghizadeh, Amin
2016-01-08
In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in
Nankali, Saber; Miandoab, Payam Samadi; Baghizadeh, Amin
2016-01-01
In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature
International Nuclear Information System (INIS)
Zhang, Leihong; Liang, Dong
2016-01-01
In order to solve the problem that reconstruction efficiency and precision is not high, in this paper different samples are selected to reconstruct spectral reflectance, and a new kind of spectral reflectance reconstruction method based on the algorithm of compressive sensing is provided. Four different color numbers of matte color cards such as the ColorChecker Color Rendition Chart and Color Checker SG, the copperplate paper spot color card of Panton, and the Munsell colors card are chosen as training samples, the spectral image is reconstructed respectively by the algorithm of compressive sensing and pseudo-inverse and Wiener, and the results are compared. These methods of spectral reconstruction are evaluated by root mean square error and color difference accuracy. The experiments show that the cumulative contribution rate and color difference of the Munsell colors card are better than those of the other three numbers of color cards in the same conditions of reconstruction, and the accuracy of the spectral reconstruction will be affected by the training sample of different numbers of color cards. The key technology of reconstruction means that the uniformity and representation of the training sample selection has important significance upon reconstruction. In this paper, the influence of the sample selection on the spectral image reconstruction is studied. The precision of the spectral reconstruction based on the algorithm of compressive sensing is higher than that of the traditional algorithm of spectral reconstruction. By the MATLAB simulation results, it can be seen that the spectral reconstruction precision and efficiency are affected by the different color numbers of the training sample. (paper)
International Nuclear Information System (INIS)
Jerry B. Urbas
1999-01-01
The U. S. Department of Energy (DOE), Electric Power Research Institute (EPRI), Pennsylvania Electric Energy Research Council, (PEERC), New York State Electric and Gas and GPU Generation, Inc. jointly funded a demonstration to determine the capabilities for Hybrid SNCR/SCR (Selective Non-Catalytic Reduction/Selective Catalytic Reduction) technology. The demonstration site was GPU Generation's Seward Unit No.5 (147MW) located in Seward Pennsylvania. The demonstration began in October of 1997 and ended in December 1998. DOE funding was provided through Grant No. DE-FG22-96PC96256 with T. J. Feeley as the Project Manager. EPRI funding was provided through agreements TC4599-001-26999 and TC4599-002-26999 with E. Hughes as the Project Manager. This project demonstrated the operation of the Hybrid SNCR/SCR NO(sub x) control process on a full-scale coal fired utility boiler. The hybrid technology was expected to provide a cost-effective method of reducing NO(sub x) while balancing capital and operation costs. An existing urea based SNCR system was modified with an expanded-duct catalyst to provide increased NO(sub x) reduction efficiency from the SNCR while producing increased ammonia slip levels to the catalyst. The catalyst was sized to reduce the ammonia slip to the air heaters to less than 2 ppm while providing equivalent NO(sub x) reductions. The project goals were to demonstrate hybrid technology is capable of achieving at least a 55% reduction in NO(sub x) emissions while maintaining less than 2ppm ammonia slip to the air heaters, maintain flyash marketability, verify the cost benefit and applicability of Hybrid post combustion technology, and reduce forced outages due to ammonium bisulfate (ABS) fouling of the air heaters. Early system limitations, due to gas temperature stratification, restricted the Hybrid NO(sub x) reduction capabilities to 48% with an ammonia slip of 6.1 mg/Nm(sup 3) (8 ppm) at the catalyst inlet. After resolving the stratification problem
Energy Technology Data Exchange (ETDEWEB)
Jerry B. Urbas
1999-05-01
The U. S. Department of Energy (DOE), Electric Power Research Institute (EPRI), Pennsylvania Electric Energy Research Council, (PEERC), New York State Electric and Gas and GPU Generation, Inc. jointly funded a demonstration to determine the capabilities for Hybrid SNCR/SCR (Selective Non-Catalytic Reduction/Selective Catalytic Reduction) technology. The demonstration site was GPU Generation's Seward Unit No.5 (147MW) located in Seward Pennsylvania. The demonstration began in October of 1997 and ended in December 1998. DOE funding was provided through Grant No. DE-FG22-96PC96256 with T. J. Feeley as the Project Manager. EPRI funding was provided through agreements TC4599-001-26999 and TC4599-002-26999 with E. Hughes as the Project Manager. This project demonstrated the operation of the Hybrid SNCR/SCR NO{sub x} control process on a full-scale coal fired utility boiler. The hybrid technology was expected to provide a cost-effective method of reducing NO{sub x} while balancing capital and operation costs. An existing urea based SNCR system was modified with an expanded-duct catalyst to provide increased NO{sub x} reduction efficiency from the SNCR while producing increased ammonia slip levels to the catalyst. The catalyst was sized to reduce the ammonia slip to the air heaters to less than 2 ppm while providing equivalent NO{sub x} reductions. The project goals were to demonstrate hybrid technology is capable of achieving at least a 55% reduction in NO{sub x} emissions while maintaining less than 2ppm ammonia slip to the air heaters, maintain flyash marketability, verify the cost benefit and applicability of Hybrid post combustion technology, and reduce forced outages due to ammonium bisulfate (ABS) fouling of the air heaters. Early system limitations, due to gas temperature stratification, restricted the Hybrid NO{sub x} reduction capabilities to 48% with an ammonia slip of 6.1 mg/Nm{sup 3} (8 ppm) at the catalyst inlet. After resolving the stratification
Energy Technology Data Exchange (ETDEWEB)
Priewasser, Reinhold [Linz Univ., Inst. for Environmental Management in Enterprises and Regions, Linz (Austria)
1999-07-01
Facing the aim of ecological sustainability only little emphasis has been placed on the fact that the extent of environmental stresses is not only a consequence of certain factual or structural conditions but also essentially determined by varying human behaviour patterns. Technologies and structures are not ecologically effective by themselves, their environmental relevance strongly depends on the persons' way of acting within the prevailing system. Recognising the importance of that perspective psychological and social theories about the generation of personal behaviour as well as the theoretical models of learning can offer useful indications concerning the interpersonal and extrapersonal preconditions of environmentally oriented acting. With reference to the decision-making process of selecting a certain transportation mean, the influences of rational and emotional factors and obstacles to an ecologically sustainable personal act should be exemplary demonstrated. At the same time very effective points of departure for behavioural change can be identified. (Author)
Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai
2017-02-01
It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.
Directory of Open Access Journals (Sweden)
Sheng Yang
2015-01-01
Full Text Available Sequencing is widely used to discover associations between microRNAs (miRNAs and diseases. However, the negative binomial distribution (NB and high dimensionality of data obtained using sequencing can lead to low-power results and low reproducibility. Several statistical learning algorithms have been proposed to address sequencing data, and although evaluation of these methods is essential, such studies are relatively rare. The performance of seven feature selection (FS algorithms, including baySeq, DESeq, edgeR, the rank sum test, lasso, particle swarm optimistic decision tree, and random forest (RF, was compared by simulation under different conditions based on the difference of the mean, the dispersion parameter of the NB, and the signal to noise ratio. Real data were used to evaluate the performance of RF, logistic regression, and support vector machine. Based on the simulation and real data, we discuss the behaviour of the FS and classification algorithms. The Apriori algorithm identified frequent item sets (mir-133a, mir-133b, mir-183, mir-937, and mir-96 from among the deregulated miRNAs of six datasets from The Cancer Genomics Atlas. Taking these findings altogether and considering computational memory requirements, we propose a strategy that combines edgeR and DESeq for large sample sizes.
2015-01-01
Performance characteristics of gas-phase microsensors will determine the ultimate utility of these devices for a wide range of chemical monitoring applications. Commonly employed chemiresistor elements are quite sensitive to selected analytes, and relatively new methods have increased the selectivity to specific compounds, even in the presence of interfering species. Here, we have focused on determining whether purposefully driven temperature modulation can produce faster sensor-response characteristics, which could enable measurements for a broader range of applications involving dynamic compositional analysis. We investigated the response speed of a single chemiresitive In2O3 microhotplate sensor to four analytes (methanol, ethanol, acetone, 2-butanone) by systematically varying the oscillating frequency (semicycle periods of 20–120 ms) of a bilevel temperature cycle applied to the sensing element. It was determined that the fastest response (≈ 9 s), as indicated by a 98% signal-change metric, occurred for a period of 30 ms and that responses under such modulation were dramatically faster than for isothermal operation of the same device (>300 s). Rapid modulation between 150 and 450 °C exerts kinetic control over transient processes, including adsorption, desorption, diffusion, and reaction phenomena, which are important for charge transfer occurring in transduction processes and the observed response times. We also demonstrate that the fastest operation is accompanied by excellent discrimination within a challenging 16-category recognition problem (consisting of the four analytes at four separate concentrations). This critical finding demonstrates that both speed and high discriminatory capabilities can be realized through temperature modulation. PMID:24931319
Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina
2014-03-01
Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Li, Shuo; Peng, Jun; Liu, Weirong; Zhu, Zhengfa; Lin, Kuo-Chi
2014-01-01
Recent research has indicated that using the mobility of the actuator in wireless sensor and actuator networks (WSANs) to achieve mobile data collection can greatly increase the sensor network lifetime. However, mobile data collection may result in unacceptable collection delays in the network if the path of the actuator is too long. Because real-time network applications require meeting data collection delay constraints, planning the path of the actuator is a very important issue to balance the prolongation of the network lifetime and the reduction of the data collection delay. In this paper, a multi-hop routing mobile data collection algorithm is proposed based on dynamic polling point selection with delay constraints to address this issue. The algorithm can actively update the selection of the actuator's polling points according to the sensor nodes' residual energies and their locations while also considering the collection delay constraint. It also dynamically constructs the multi-hop routing trees rooted by these polling points to balance the sensor node energy consumption and the extension of the network lifetime. The effectiveness of the algorithm is validated by simulation. PMID:24451455
Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei
2018-01-01
The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.
Synthesis of Greedy Algorithms Using Dominance Relations
Nedunuri, Srinivas; Smith, Douglas R.; Cook, William R.
2010-01-01
Greedy algorithms exploit problem structure and constraints to achieve linear-time performance. Yet there is still no completely satisfactory way of constructing greedy algorithms. For example, the Greedy Algorithm of Edmonds depends upon translating a problem into an algebraic structure called a matroid, but the existence of such a translation can be as hard to determine as the existence of a greedy algorithm itself. An alternative characterization of greedy algorithms is in terms of dominance relations, a well-known algorithmic technique used to prune search spaces. We demonstrate a process by which dominance relations can be methodically derived for a number of greedy algorithms, including activity selection, and prefix-free codes. By incorporating our approach into an existing framework for algorithm synthesis, we demonstrate that it could be the basis for an effective engineering method for greedy algorithms. We also compare our approach with other characterizations of greedy algorithms.
DEFF Research Database (Denmark)
Omidikia, Nematollah; Kompany-Zareh, Mohsen
2013-01-01
Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as colline......Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well...... as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram-Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination...
Radaydeh, Redha Mahmoud Mesleh
2010-01-01
The performance of transmit antenna selection for threshold-based maximal ratio combining (MRC) diversity receivers in the presence of multiple co-channel interfering signals is studied. The impact of imperfect channel estimation of desired user signals is considered, and the effect of phase and time misalignments between desired and undesired signals is incorporated in the analysis. Precise formulation for Nakagami-m faded interfering signals is presented. The analysis is applicable for arbitrary transmit antenna selection, which is based on the receiver combined signal-to-noise ratios (SNRs) or combined signal-to-interference-plus-noise ratios (SINRs) for different transmit channels. New expressions for the distribution of combined SINR and outage probability performance are derived considering SNR-based as well as SINR-based selection algorithms. The results can be used to study the performance of different system architectures under various channel conditions when the implementation complexity is of interest. ©2010 IEEE.
Analysis of stock investment selection based on CAPM using covariance and genetic algorithm approach
Sukono; Susanti, D.; Najmia, M.; Lesmana, E.; Napitupulu, H.; Supian, S.; Putra, A. S.
2018-03-01
Investment is one of the economic growth factors of countries, especially in Indonesia. Stocks is a form of investment, which is liquid. In determining the stock investment decisions which need to be considered by investors is to choose stocks that can generate maximum returns with a minimum risk level. Therefore, we need to know how to allocate the capital which may give the optimal benefit. This study discusses the issue of stock investment based on CAPM which is estimated using covariance and Genetic Algorithm approach. It is assumed that the stocks analyzed follow the CAPM model. To do the estimation of beta parameter on CAPM equation is done by two approach, first is to be represented by covariance approach, and second with genetic algorithm optimization. As a numerical illustration, in this paper analyzed ten stocks traded on the capital market in Indonesia. The results of the analysis show that estimation of beta parameters using covariance and genetic algorithm approach, give the same decision, that is, six underpriced stocks with buying decision, and four overpriced stocks with a sales decision. Based on the analysis, it can be concluded that the results can be used as a consideration for investors buying six under-priced stocks, and selling four overpriced stocks.
Chawla, Viveak Kumar; Chanda, Arindam Kumar; Angra, Surjit
2018-03-01
The flexible manufacturing system (FMS) constitute of several programmable production work centers, material handling systems (MHSs), assembly stations and automatic storage and retrieval systems. In FMS, the automatic guided vehicles (AGVs) play a vital role in material handling operations and enhance the performance of the FMS in its overall operations. To achieve low makespan and high throughput yield in the FMS operations, it is highly imperative to integrate the production work centers schedules with the AGVs schedules. The Production schedule for work centers is generated by application of the Giffler and Thompson algorithm under four kind of priority hybrid dispatching rules. Then the clonal selection algorithm (CSA) is applied for the simultaneous scheduling to reduce backtracking as well as distance travel of AGVs within the FMS facility. The proposed procedure is computationally tested on the benchmark FMS configuration from the literature and findings from the investigations clearly indicates that the CSA yields best results in comparison of other applied methods from the literature.
Clustering Words to Match Conditions: An Algorithm for Stimuli Selection in Factorial Designs
Guasch, Marc; Haro, Juan; Boada, Roger
2017-01-01
With the increasing refinement of language processing models and the new discoveries about which variables can modulate these processes, stimuli selection for experiments with a factorial design is becoming a tough task. Selecting sets of words that differ in one variable, while matching these same words into dozens of other confounding variables…
Aalaei, Shokoufeh; Shahraki, Hadi; Rowhanimanesh, Alireza; Eslami, Saeid
2016-01-01
This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. To
Cao, Yang; Liu, Chun; Huang, Yuehui; Wang, Tieqiang; Sun, Chenjun; Yuan, Yue; Zhang, Xinsong; Wu, Shuyun
2017-02-01
With the development of roof photovoltaic power (PV) generation technology and the increasingly urgent need to improve supply reliability levels in remote areas, islanded microgrid with photovoltaic and energy storage systems (IMPE) is developing rapidly. The high costs of photovoltaic panel material and energy storage battery material have become the primary factors that hinder the development of IMPE. The advantages and disadvantages of different types of photovoltaic panel materials and energy storage battery materials are analyzed in this paper, and guidance is provided on material selection for IMPE planners. The time sequential simulation method is applied to optimize material demands of the IMPE. The model is solved by parallel algorithms that are provided by a commercial solver named CPLEX. Finally, to verify the model, an actual IMPE is selected as a case system. Simulation results on the case system indicate that the optimization model and corresponding algorithm is feasible. Guidance for material selection and quantity demand for IMPEs in remote areas is provided by this method.
Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo
2017-01-01
In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human
A genetic algorithm for multiple relay selection in two-way relaying cognitive radio networks
Alsharoa, Ahmad M.; Ghazzai, Hakim; Alouini, Mohamed-Slim
2013-01-01
In this paper, we investigate a multiple relay selection scheme for two-way relaying cognitive radio networks where primary users and secondary users operate on the same frequency band. More specifically, cooperative relays using Amplifyand- Forward
Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao
2015-01-01
Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of
A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Michala Jakubcová
2014-01-01
Full Text Available We compare 27 modifications of the original particle swarm optimization (PSO algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO, the second strategy periodically partitions the population into equally large complexes according to the particle’s functional value (SCE-PSO, and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO. All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process.
Qi, Nathan R.
2018-01-01
High capacity and low capacity running rats, HCR and LCR respectively, have been bred to represent two extremes of running endurance and have recently demonstrated disparities in fuel usage during transient aerobic exercise. HCR rats can maintain fatty acid (FA) utilization throughout the course of transient aerobic exercise whereas LCR rats rely predominantly on glucose utilization. We hypothesized that the difference between HCR and LCR fuel utilization could be explained by a difference in mitochondrial density. To test this hypothesis and to investigate mechanisms of fuel selection, we used a constraint-based kinetic analysis of whole-body metabolism to analyze transient exercise data from these rats. Our model analysis used a thermodynamically constrained kinetic framework that accounts for glycolysis, the TCA cycle, and mitochondrial FA transport and oxidation. The model can effectively match the observed relative rates of oxidation of glucose versus FA, as a function of ATP demand. In searching for the minimal differences required to explain metabolic function in HCR versus LCR rats, it was determined that the whole-body metabolic phenotype of LCR, compared to the HCR, could be explained by a ~50% reduction in total mitochondrial activity with an additional 5-fold reduction in mitochondrial FA transport activity. Finally, we postulate that over sustained periods of exercise that LCR can partly overcome the initial deficit in FA catabolic activity by upregulating FA transport and/or oxidation processes. PMID:29474500
Directory of Open Access Journals (Sweden)
Rui Zhang
2012-01-01
Full Text Available Most existing research on the job shop scheduling problem has been focused on the minimization of makespan (i.e., the completion time of the last job. However, in the fiercely competitive market nowadays, delivery punctuality is more important for maintaining a high service reputation. So in this paper, we aim at solving job shop scheduling problems with the total weighted tardiness objective. Several dispatching rules are adopted in the Giffler-Thompson algorithm for constructing active schedules. It is noticeable that the rule selections for scheduling consecutive operations are not mutually independent but actually interrelated. Under such circumstances, a probabilistic model-building genetic algorithm (PMBGA is proposed to optimize the sequence of selected rules. First, we use Bayesian networks to model the distribution characteristics of high-quality solutions in the population. Then, the new generation of individuals is produced by sampling the established Bayesian network. Finally, some elitist individuals are further improved by a special local search module based on parameter perturbation. The superiority of the proposed approach is verified by extensive computational experiments and comparisons.
Directory of Open Access Journals (Sweden)
Simon Fong
2015-01-01
Full Text Available Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.
Selection of events at Ukrainian NPPs using the algorithm based on accident precursor method
International Nuclear Information System (INIS)
Vorontsov, D.V.; Lyigots'kij, O.Yi.; Serafin, R.Yi.; Tkachova, L.M.
2012-01-01
The paper describes a general approach to the first stage of research and development on analysis of Ukrainian NPP operation events from 1 January 2000 to 31 December 2010 using the accident precursor approach. Groups of potentially important events formed after their selection and classification are provided
CSIR Research Space (South Africa)
De Klerk, HM
2016-11-01
Full Text Available Traditionally, to map environmental features using remote sensing, practitioners will use training data to develop models on various satellite data sets using a number of classification approaches and use test data to select a single ‘best performer...
Lu, Karen H; Skates, Steven; Hernandez, Mary A; Bedi, Deepak; Bevers, Therese; Leeds, Leroy; Moore, Richard; Granai, Cornelius; Harris, Steven; Newland, William; Adeyinka, Olasunkanmi; Geffen, Jeremy; Deavers, Michael T; Sun, Charlotte C; Horick, Nora; Fritsche, Herbert; Bast, Robert C
2013-10-01
A 2-stage ovarian cancer screening strategy was evaluated that incorporates change of carbohydrate antigen 125 (CA125) levels over time and age to estimate risk of ovarian cancer. Women with high-risk scores were referred for transvaginal ultrasound (TVS). A single-arm, prospective study of postmenopausal women was conducted. Participants underwent an annual CA125 blood test. Based on the Risk of Ovarian Cancer Algorithm (ROCA) result, women were triaged to next annual CA125 test (low risk), repeat CA125 test in 3 months (intermediate risk), or TVS and referral to a gynecologic oncologist (high risk). A total of 4051 women participated over 11 years. The average annual rate of referral to a CA125 test in 3 months was 5.8%, and the average annual referral rate to TVS and review by a gynecologic oncologist was 0.9%. Ten women underwent surgery on the basis of TVS, with 4 invasive ovarian cancers (1 with stage IA disease, 2 with stage IC disease, and 1 with stage IIB disease), 2 ovarian tumors of low malignant potential (both stage IA), 1 endometrial cancer (stage I), and 3 benign ovarian tumors, providing a positive predictive value of 40% (95% confidence interval = 12.2%, 73.8%) for detecting invasive ovarian cancer. The specificity was 99.9% (95% confidence interval = 99.7%, 100%). All 4 women with invasive ovarian cancer were enrolled in the study for at least 3 years with low-risk annual CA125 test values prior to rising CA125 levels. ROCA followed by TVS demonstrated excellent specificity and positive predictive value in a population of US women at average risk for ovarian cancer. Copyright © 2013 American Cancer Society.
Trusiak, Maciej; Patorski, Krzysztof; Wielgus, Maciej
2012-10-08
Presented method for fringe pattern enhancement has been designed for processing and analyzing low quality fringe patterns. It uses a modified fast and adaptive bidimensional empirical mode decomposition (FABEMD) for the extraction of bidimensional intrinsic mode functions (BIMFs) from an interferogram. Fringe pattern is then selectively reconstructed (SR) taking the regions of selected BIMFs with high modulation values only. Amplitude demodulation and normalization of the reconstructed image is conducted using the spiral phase Hilbert transform (HS). It has been tested using computer generated interferograms and real data. The performance of the presented SR-FABEMD-HS method is compared with other normalization techniques. Its superiority, potential and robustness to high fringe density variations and the presence of noise, modulation and background illumination defects in analyzed fringe patterns has been corroborated.
Zhang, Wenyu; Yang, Yushu; Zhang, Shuai; Yu, Dejian; Xu, Yangbing
2016-01-01
With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the s...
Chemical library subset selection algorithms: a unified derivation using spatial statistics.
Hamprecht, Fred A; Thiel, Walter; van Gunsteren, Wilfred F
2002-01-01
If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.
Selection of core animals in the Algorithm for Proven and Young using a simulation model.
Bradford, H L; Pocrnić, I; Fragomeni, B O; Lourenco, D A L; Misztal, I
2017-12-01
The Algorithm for Proven and Young (APY) enables the implementation of single-step genomic BLUP (ssGBLUP) in large, genotyped populations by separating genotyped animals into core and non-core subsets and creating a computationally efficient inverse for the genomic relationship matrix (G). As APY became the choice for large-scale genomic evaluations in BLUP-based methods, a common question is how to choose the animals in the core subset. We compared several core definitions to answer this question. Simulations comprised a moderately heritable trait for 95,010 animals and 50,000 genotypes for animals across five generations. Genotypes consisted of 25,500 SNP distributed across 15 chromosomes. Genotyping errors and missing pedigree were also mimicked. Core animals were defined based on individual generations, equal representation across generations, and at random. For a sufficiently large core size, core definitions had the same accuracies and biases, even if the core animals had imperfect genotypes. When genotyped animals had unknown parents, accuracy and bias were significantly better (p ≤ .05) for random and across generation core definitions. © 2017 The Authors. Journal of Animal Breeding and Genetics Published by Blackwell Verlag GmbH.
A Parallel Approach To Optimum Actuator Selection With a Genetic Algorithm
Rogers, James L.
2000-01-01
Recent discoveries in smart technologies have created a variety of aerodynamic actuators which have great potential to enable entirely new approaches to aerospace vehicle flight control. For a revolutionary concept such as a seamless aircraft with no moving control surfaces, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements. The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement Maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. Genetic algorithms have been instrumental in achieving good solutions to discrete optimization problems, such as the actuator placement problem. As a proof of concept, a genetic has been developed to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control for a simplified, untapered, unswept wing model. To find the optimum placement by searching all possible combinations would require 1,100 hours. Formulating the problem and as a multi-objective problem and modifying it to take advantage of the parallel processing capabilities of a multi-processor computer, reduces the optimization time to 22 hours.
Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops.
Laursen, Morten Stigaard; Jørgensen, Rasmus Nyholm; Midtiby, Henrik Skov; Jensen, Kjeld; Christiansen, Martin Peter; Giselsson, Thomas Mosgaard; Mortensen, Anders Krogh; Jensen, Peter Kryger
2016-11-04
The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.
Directory of Open Access Journals (Sweden)
Wendong Yang
2017-01-01
Full Text Available Machine learning plays a vital role in several modern economic and industrial fields, and selecting an optimized machine learning method to improve time series’ forecasting accuracy is challenging. Advanced machine learning methods, e.g., the support vector regression (SVR model, are widely employed in forecasting fields, but the individual SVR pays no attention to the significance of data selection, signal processing and optimization, which cannot always satisfy the requirements of time series forecasting. By preprocessing and analyzing the original time series, in this paper, a hybrid SVR model is developed, considering periodicity, trend and randomness, and combined with data selection, signal processing and an optimization algorithm for short-term load forecasting. Case studies of electricity power data from New South Wales and Singapore are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed hybrid method is not only robust but also capable of achieving significant improvement compared with the traditional single models and can be an effective and efficient tool for power load forecasting.
International Nuclear Information System (INIS)
Perkins, F.K.; Onellion, M.; Lee, S.; Bowben, T.A.
1992-01-01
The scanning tunneling microscope (STM) can be used to selectively deposit material from a gaseous precursor compound. Ultrasmall (less than a 100 nm across) spatial dimensions for selective area deposition may be achieved by this means. In this paper the authors outline a scheme foreselecting and designing main group cluster compounds and organometallics for this type of selective area deposition using nido-decaborane(14) as an example
Hougardy, Stefan
2016-01-01
Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.
Directory of Open Access Journals (Sweden)
L. S. Sindhuja
2016-01-01
Full Text Available Security of Mobile Wireless Sensor Networks is a vital challenge as the sensor nodes are deployed in unattended environment and they are prone to various attacks. One among them is the node replication attack. In this, the physically insecure nodes are acquired by the adversary to clone them by having the same identity of the captured node, and the adversary deploys an unpredictable number of replicas throughout the network. Hence replica node detection is an important challenge in Mobile Wireless Sensor Networks. Various replica node detection techniques have been proposed to detect these replica nodes. These methods incur control overheads and the detection accuracy is low when the replica is selected as a witness node. This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. The advantages of the proposed method include (i increase in the detection ratio, (ii decrease in the control overhead, and (iii increase in throughput. The performance of the proposed work is measured using detection ratio, false detection ratio, packet delivery ratio, average delay, control overheads, and throughput. The implementation is done using ns-2 to exhibit the actuality of the proposed work.
Gu, Xiao-Yu; Xu, Ke-Xin; Wang, Yan
2006-09-01
Near infrared (NIR) spectroscopy has raised a lot of interest in the pharmaceutical industry because it is a rapid and cost-effective analytical type of spectroscopy with no need for extensive sample preparation, and with the easy-realizable ability of on-line application. The NIR technology can increase the quality control standard of the Chinese medicine and accelerate the entry into the international market. In the present paper, two methods for wavelength selection are applied to the measurement of borneol, one of which is the multiple-chain stepwise, which tends to select many variables in the same area containing valuable information, and the other is the mixture genetic algorithm, which incorporates simulated annealing so as to improve the local searching ability while maintaining the global searching ability. The results present that the number of wavelength is reduced to 16% compared with the original number of wavelength, and the prediction accuracy has increased 47.6%. Therefore, the method of wavelength selection is a good way to enhance the prediction accuracy and simplify the model in NIR region.
Cohen-Khait, Ruth; Schreiber, Gideon
2018-04-27
Protein-protein interactions mediate the vast majority of cellular processes. Though protein interactions obey basic chemical principles also within the cell, the in vivo physiological environment may not allow for equilibrium to be reached. Thus, in vitro measured thermodynamic affinity may not provide a complete picture of protein interactions in the biological context. Binding kinetics composed of the association and dissociation rate constants are relevant and important in the cell. Therefore, changes in protein-protein interaction kinetics have a significant impact on the in vivo activity of the proteins. The common protocol for the selection of tighter binders from a mutant library selects for protein complexes with slower dissociation rate constants. Here we describe a method to specifically select for variants with faster association rate constants by using pre-equilibrium selection, starting from a large random library. Toward this end, we refine the selection conditions of a TEM1-β-lactamase library against its natural nanomolar affinity binder β-lactamase inhibitor protein (BLIP). The optimal selection conditions depend on the ligand concentration and on the incubation time. In addition, we show that a second sort of the library helps to separate signal from noise, resulting in a higher percent of faster binders in the selected library. Fast associating protein variants are of particular interest for drug development and other biotechnological applications.
Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes.
Chua, Sook-Ling; Foo, Lee Kien
2017-08-18
Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant's activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.
Rochman, Auliya Noor; Prasetyo, Hari; Nugroho, Munajat Tri
2017-06-01
Vehicle Routing Problem (VRP) often occurs when the manufacturers need to distribute their product to some customers/outlets. The distribution process is typically restricted by the capacity of the vehicle and the working hours at the distributor. This type of VRP is also known as Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). A Biased Random Key Genetic Algorithm (BRKGA) was designed and coded in MATLAB to solve the CVRPTW case of soft drink distribution. The standard BRKGA was then modified by applying chromosome insertion into the initial population and defining chromosome gender for parent undergoing crossover operation. The performance of the established algorithms was then compared to a heuristic procedure for solving a soft drink distribution. Some findings are revealed (1) the total distribution cost of BRKGA with insertion (BRKGA-I) results in a cost saving of 39% compared to the total cost of heuristic method, (2) BRKGA with the gender selection (BRKGA-GS) could further improve the performance of the heuristic method. However, the BRKGA-GS tends to yield worse results compared to that obtained from the standard BRKGA.
Xue, Yu; Liu, Zexian; Cao, Jun; Ma, Qian; Gao, Xinjiao; Wang, Qingqi; Jin, Changjiang; Zhou, Yanhong; Wen, Longping; Ren, Jian
2011-03-01
As the most important post-translational modification of proteins, phosphorylation plays essential roles in all aspects of biological processes. Besides experimental approaches, computational prediction of phosphorylated proteins with their kinase-specific phosphorylation sites has also emerged as a popular strategy, for its low-cost, fast-speed and convenience. In this work, we developed a kinase-specific phosphorylation sites predictor of GPS 2.1 (Group-based Prediction System), with a novel but simple approach of motif length selection (MLS). By this approach, the robustness of the prediction system was greatly improved. All algorithms in GPS old versions were also reserved and integrated in GPS 2.1. The online service and local packages of GPS 2.1 were implemented in JAVA 1.5 (J2SE 5.0) and freely available for academic researches at: http://gps.biocuckoo.org.
Amin, Nor Azrita Mohd; Adam, Mohd Bakri; Ibrahim, Noor Akma
2014-09-01
The Multiple-try Metropolis (MTM) algorithm is the new alternatives in the field of Bayesian extremes for summarizing the posterior distribution. MTM produce efficient estimation scheme for modelling extreme data in term of the convergence and small burn-in periods. The main objective is to explore the accuracy of the parameter estimation to the change of priors and compare the results with a classical likelihood-based analysis. Focus is on modelling the extreme data based on block maxima approach using Gumbel distribution. The comparative study between MTM and MLE is shown by the numerical problems. Several goodness of fit tests are compute for selecting the best model. The application is on the monthly maxima PM10 data for Johor state.
Directory of Open Access Journals (Sweden)
Mustafa Serter Uzer
2013-01-01
Full Text Available This paper offers a hybrid approach that uses the artificial bee colony (ABC algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.
Sotov, A. V.; Agapovichev, A. V.; Smelov, V. G.; Kyarimov, R. R.
2018-01-01
The technology of the selective laser melting (SLM) allows making products from powders of aluminum, titanium, heat-resistant alloys and stainless steels. Today the use of SLM technology develops at manufacture of the functional parts. This in turn requires development of a methodology projection of technological processes (TP) for manufacturing parts including databases of standard TP. Use of a technique will allow to exclude influence of technologist’s qualification on made products quality, and also to reduce labor input and energy consumption by development of TP due to use of the databases of standard TP integrated into a methodology. As approbation of the developed methodology the research of influence of the modes of a laser emission on a roughness of a surface of synthesized material was presented. It is established that the best values of a roughness of exemplars in the longitudinal and transversal directions make 1.98 μm and 3.59 μm respectively. These values of a roughness were received at specific density of energy 6.25 J/mm2 that corresponds to power and the speed of scanning of 200 W and 400 mm/s, respectively, and a hatch distance of 0.08 mm.
International Nuclear Information System (INIS)
Kim, Dae-Won; Protopapas, Pavlos; Alcock, Charles; Trichas, Markos; Byun, Yong-Ik; Khardon, Roni
2011-01-01
We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ∼80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million light curves, and found 1620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.
Gonzalez, T.; Ruvalcaba, A.; Oliver, L.
2016-12-01
The electricity generation from renewable resources has acquired a leading role. Mexico particularrly it has great interest in renewable natural resources for power generation, especially wind energy. Therefore, the country is rapidly entering in the development of wind power generators sites. The development of a wind places as an energy project, does not have a standardized methodology. Techniques vary according to the developer to select the best place to install a wind turbine system. Generally to install the system the developers consider three key factors: 1) the characteristics of the wind, 2) the potential distribution of electricity and 3) transport access to the site. This paper presents a study with a different methodology which is carried out in two stages: the first at regional scale uses "space" and "natural" criteria in order to select a region based on its cartographic features such as politics and physiographic division, location of conservation natural areas, water bodies, urban criteria; and natural criteria such as the amount and direction of the wind, the type and land use, vegetation, topography and biodiversity of the site. The result of the application of these criteria, gives a first optimal selection area. The second part of the methodology includes criteria and variables on detail scale. The analysis of all data information collected will provide new parameters (decision variables) for the site. The overall analysis of the information, based in these criteria, indicates that the best location that the best location of the field would be the southern Coahuila and the central part of Nuevo Leon. The wind power site will contribute to the economy grow of important cities including Monterrey. Finally, computational model of genetic algorithm will be used as a tool to determine the best site selection depending on the parameters considered.
Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T
2015-01-01
MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.
International Nuclear Information System (INIS)
Ahmed, M; Gu, F; Ball, A
2011-01-01
Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.
Energy Technology Data Exchange (ETDEWEB)
Ahmed, M; Gu, F; Ball, A, E-mail: M.Ahmed@hud.ac.uk [Diagnostic Engineering Research Group, University of Huddersfield, HD1 3DH (United Kingdom)
2011-07-19
Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.
Directory of Open Access Journals (Sweden)
Min Jin
2014-01-01
Full Text Available There is recently a great deal of interest and excitement in understanding the role of inertia and acceleration in the motion equation of discrete particle swarm optimization (DPSO algorithms. It still remains unknown whether the inertia section should be abandoned and how to select the appropriate acceleration in order for DPSO to show the best convergence performance. Adopting channel assignment as a case study, this paper systematically conducts experimental filtering research on this issue. Compared with other channel assignment schemes, the proposed scheme and the selection of inertia and acceleration are verified to have the advantage to channel assignment in three respects of convergence rate, convergence speed, and the independency of the quality of initial solution. Furthermore, the experimental result implies that DSPO might have the best convergence performance when its motion equation includes an inertia section in a less medium weight, a bigger acceleration coefficient for global-search optimum, and a smaller acceleration coefficient for individual-search optimum.
FRAMEWORK FOR COMPARING SEGMENTATION ALGORITHMS
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G. Sithole
2015-05-01
Full Text Available The notion of a ‘Best’ segmentation does not exist. A segmentation algorithm is chosen based on the features it yields, the properties of the segments (point sets it generates, and the complexity of its algorithm. The segmentation is then assessed based on a variety of metrics such as homogeneity, heterogeneity, fragmentation, etc. Even after an algorithm is chosen its performance is still uncertain because the landscape/scenarios represented in a point cloud have a strong influence on the eventual segmentation. Thus selecting an appropriate segmentation algorithm is a process of trial and error. Automating the selection of segmentation algorithms and their parameters first requires methods to evaluate segmentations. Three common approaches for evaluating segmentation algorithms are ‘goodness methods’, ‘discrepancy methods’ and ‘benchmarks’. Benchmarks are considered the most comprehensive method of evaluation. This paper shortcomings in current benchmark methods are identified and a framework is proposed that permits both a visual and numerical evaluation of segmentations for different algorithms, algorithm parameters and evaluation metrics. The concept of the framework is demonstrated on a real point cloud. Current results are promising and suggest that it can be used to predict the performance of segmentation algorithms.
Yang, Qian; Wu, Xue-Bing; Fan, Xiaohui; Jiang, Linhua; McGreer, Ian; Green, Richard; Yang, Jinyi; Schindler, Jan-Torge; Wang, Feige; Zuo, Wenwen; Fu, Yuming
2017-12-01
We present a new algorithm to estimate quasar photometric redshifts (photo-zs), by considering the asymmetries in the relative flux distributions of quasars. The relative flux models are built with multivariate Skew-t distributions in the multidimensional space of relative fluxes as a function of redshift and magnitude. For 151,392 quasars in the SDSS, we achieve a photo-z accuracy, defined as the fraction of quasars with the difference between the photo-z z p and the spectroscopic redshift z s , | {{Δ }}z| =| {z}s-{z}p| /(1+{z}s) within 0.1, of 74%. Combining the WISE W1 and W2 infrared data with the SDSS data, the photo-z accuracy is enhanced to 87%. Using the Pan-STARRS1 or DECaLS photometry with WISE W1 and W2 data, the photo-z accuracies are 79% and 72%, respectively. The prior probabilities as a function of magnitude for quasars, stars, and galaxies are calculated, respectively, based on (1) the quasar luminosity function, (2) the Milky Way synthetic simulation with the Besançon model, and (3) the Bayesian Galaxy Photometric Redshift estimation. The relative fluxes of stars are obtained with the Padova isochrones, and the relative fluxes of galaxies are modeled through galaxy templates. We test our classification method to select quasars using the DECaLS g, r, z, and WISE W1 and W2 photometry. The quasar selection completeness is higher than 70% for a wide redshift range 0.5publicly available.
Lee, Tae Hoon; Hwang, Soon Oh; Choi, Hyun Jong; Jung, Yunho; Cha, Sang Woo; Chung, Il-Kwun; Moon, Jong Ho; Cho, Young Deok; Park, Sang-Heum; Kim, Sun-Joo
2014-02-17
Numerous clinical trials to improve the success rate of biliary access in difficult biliary cannulation (DBC) during ERCP have been reported. However, standard guidelines or sequential protocol analysis according to different methods are limited in place. We planned to investigate a sequential protocol to facilitate selective biliary access for DBC during ERCP. This prospective clinical study enrolled 711 patients with naïve papillae at a tertiary referral center. If wire-guided cannulation was deemed to have failed due to the DBC criteria, then according to the cannulation algorithm early precut fistulotomy (EPF; cannulation time > 5 min, papillary contacts > 5 times, or hook-nose-shaped papilla), double-guidewire cannulation (DGC; unintentional pancreatic duct cannulation ≥ 3 times), and precut after placement of a pancreatic stent (PPS; if DGC was difficult or failed) were performed sequentially. The main outcome measurements were the technical success, procedure outcomes, and complications. Initially, a total of 140 (19.7%) patients with DBC underwent EPF (n = 71) and DGC (n = 69). Then, in DGC group 36 patients switched to PPS due to difficulty criteria. The successful biliary cannulation rate was 97.1% (136/140; 94.4% [67/71] with EPF, 47.8% [33/69] with DGC, and 100% [36/36] with PPS; P EPF, 314.8 (65.2) seconds in DGC, and 706.0 (469.4) seconds in PPS (P EPF, DGC, and PPS may be safe and feasible for DBC. The use of EPF in selected DBC criteria, DGC in unintentional pancreatic duct cannulations, and PPS in failed or difficult DGC may facilitate successful biliary cannulation.
Maloney, Vincent; Szczepanski, Zach
2017-01-01
A simple, inexpensive, discovery-based experiment for undergraduate organic laboratories has been developed that demonstrates the Lewis acid and shape-selective properties of zeolites. Calcined zeolite Na-Y promotes the electrophilic aromatic bromination of toluene with a significantly higher para/ortho ratio than observed under conventional…
International Nuclear Information System (INIS)
Anav, A.; Rafanelli, C.; Di Menno, I.; Di Menno, M.
2004-01-01
The paper shows a semi-analytical method for environmental and dosimetric applications to evaluate, in clear sky conditions, the solar irradiance and the effective dose rates for some action spectra using only four spectral irradiance values at selected wavelengths in the UV-B and UV-A regions (305, 320, 340 and 380 nm). The method, named WL4UV, is based on the reconstruction of an approximated spectral irradiance that can be integrated, to obtain the solar irradiance, or convoluted with an action spectrum to obtain an effective dose rate. The parameters required in the algorithm are deduced from archived solar spectral irradiance data. This database contains measurements carried out by some Brewer spectrophotometers located in various geographical positions, at similar altitudes, with very different environmental characteristics: Rome (Italy), Ny Aalesund (Svalbard Islands (Norway)) and Ushuaia (Tierra del Fuego (Argentina)). To evaluate the precision of the method, a double test was performed with data not used in developing the model. Archived Brewer measurement data, in clear sky conditions, from Rome and from the National Science Foundation UV data set in San Diego (CA, USA) and Ushuaia, where SUV 100 spectro-radiometers operate, were drawn randomly. The comparison of measured and computed irradiance has a relative deviation of about ±2%. The effective dose rates for action spectra of Erythema, DNA and non-Melanoma skin cancer have a relative deviation of less than ∼20% for solar zenith angles <50 deg.. (authors)
Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A
2015-07-01
Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.
Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire
2017-05-04
Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.
Shao, Chi-Yu; Chen, Sing-Zuo; Su, Bo-Han; Tseng, Yufeng J; Esposito, Emilio Xavier; Hopfinger, Anton J
2013-01-28
Little attention has been given to the selection of trial descriptor sets when designing a QSAR analysis even though a great number of descriptor classes, and often a greater number of descriptors within a given class, are now available. This paper reports an effort to explore interrelationships between QSAR models and descriptor sets. Zhou and co-workers (Zhou et al., Nano Lett. 2008, 8 (3), 859-865) designed, synthesized, and tested a combinatorial library of 80 surface modified, that is decorated, multi-walled carbon nanotubes for their composite nanotoxicity using six endpoints all based on a common 0 to 100 activity scale. Each of the six endpoints for the 29 most nanotoxic decorated nanotubes were incorporated as the training set for this study. The study reported here includes trial descriptor sets for all possible combinations of MOE, VolSurf, and 4D-fingerprints (FP) descriptor classes, as well as including and excluding explicit spatial contributions from the nanotube. Optimized QSAR models were constructed from these multiple trial descriptor sets. It was found that (a) both the form and quality of the best QSAR models for each of the endpoints are distinct and (b) some endpoints are quite dependent upon 4D-FP descriptors of the entire nanotube-decorator complex. However, other endpoints yielded equally good models only using decorator descriptors with and without the decorator-only 4D-FP descriptors. Lastly, and most importantly, the quality, significance, and interpretation of a QSAR model were found to be critically dependent on the trial descriptor sets used within a given QSAR endpoint study.
International Nuclear Information System (INIS)
Bracht, M.; De Groot, A.; Gregoire Padro, C.E.; Schucan, T.H.; Skolnik, E.
1998-06-01
As part of the International Energy Agency Hydrogen Implementing Agreement, an evaluation tool to assist in the design, operation and optimisation of hydrogen demonstration facilities is under development. Using commercially available flowsheet simulation software (ASPEN- Plus) as the integrating platform, this tool is designed to provide system developers with a comprehensive data base or library of component models and an integrating platform through which these models may be linked. By combining several energy system components a conceptual design of a integrated hydrogen energy system can be made. As a part of the tool and connected to the library are design guidelines which can help finding the optimal configuration in the design process. The component categories considered include: production, storage, transport, distribution and end use. Many component models have already been included in the initial test platform. The use of the tool will be illustrated by presenting the results of a specific sample system that has been designed and assessed with use of the tool. The system considered is a decentralized renewable hydrogen system in which the hydrogen is produced by biomass gasification or pyrolysis, the produced hydrogen is transported through a pipeline or with a tank truck. The storage options that are considered are liquid hydrogen and compressed gas. The hydrogen is dispensed through a refueling station. Several options for integration are conceivable; i.e. storage of the hydrogen can take place centrally or district heat of a gasification unit can be used to generate electricity for liquefaction, etc. With use of the tool several configurations with different components and various integration options have been examined. Both the results of the modeling effort and an assessment of the evaluation tool will be presented. 5 refs
Cui, Ying; Chen, Qinggang; Li, Yaxiao; Tang, Ling
2017-02-01
Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R 2 ) of the whole training set (training and prediction) was 0.924, and the R 2 of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q 2 value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.
Directory of Open Access Journals (Sweden)
Faosan Mapa
2014-01-01
Full Text Available Normal 0 false false false IN X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Wireless Mesh Network (WMN adalah suatu konektivitas jaringan yang self-organized, self-configured dan multi-hop. Tujuan dari WMN adalah menawarkan pengguna suatu bentuk jaringan nirkabel yang dapat dengan mudah berkomunikasi dengan jaringan konvensional dengan kecepatan tinggi dan dengan cakupan yang lebih luas serta biaya awal yang minimal. Diperlukan suatu desain protokol routing yang efisien untuk WMN yang secara adaptif dapat mendukung mesh routers dan mesh clients. Dalam tulisan ini, diusulkan untuk mengoptimalkan protokol OLSR, yang merupakan protokol routing proaktif. Digunakan heuristik yang meningkatkan protokol OLSR melalui adaptive refreshing time interval dan memperbaiki metode MPR selecting algorithm. Suatu analisa dalam meningkatkan protokol OLSR melalui adaptive refreshing time interval dan memperbaiki algoritma pemilihan MPR menunjukkan kinerja yang signifikan dalam hal throughput jika dibandingkan dengan protokol OLSR yang asli. Akan tetapi, terdapat kenaikan dalam hal delay. Pada simulasi yang dilakukan dapat disimpulkan bahwa OLSR dapat dioptimalkan dengan memodifikasi pemilihan node MPR berdasarkan cost effective dan penyesuaian waktu interval refreshing hello message sesuai dengan keadaan
Rodriguez, Robert M; Hendey, Gregory W; Mower, William R
2017-01-01
Chest imaging plays a prominent role in blunt trauma patient evaluation, but indiscriminate imaging is expensive, may delay care, and unnecessarily exposes patients to potentially harmful ionizing radiation. To improve diagnostic chest imaging utilization, we conducted 3 prospective multicenter studies over 12years to derive and validate decision instruments (DIs) to guide the use of chest x-ray (CXR) and chest computed tomography (CT). The first DI, NEXUS Chest x-ray, consists of seven criteria (Age >60years; rapid deceleration mechanism; chest pain; intoxication; altered mental status; distracting painful injury; and chest wall tenderness) and exhibits a sensitivity of 99.0% (95% confidence interval [CI] 98.2-99.4%) and a specificity of 13.3% (95% CI, 12.6%-14.0%) for detecting clinically significant injuries. We developed two NEXUS Chest CT DIs, which are both highly reliable in detecting clinically major injuries (sensitivity of 99.2%; 95% CI 95.4-100%). Designed primarily to focus on detecting major injuries, the NEXUS Chest CT-Major DI consists of six criteria (abnormal CXR; distracting injury; chest wall tenderness; sternal tenderness; thoracic spine tenderness; and scapular tenderness) and exhibits higher specificity (37.9%; 95% CI 35.8-40.1%). Designed to reliability detect both major and minor injuries (sensitivity 95.4%; 95% CI 93.6-96.9%) with resulting lower specificity (25.5%; 95% CI 23.5-27.5%), the NEXUS CT-All rule consists of seven elements (the six NEXUS CT-Major criteria plus rapid deceleration mechanism). The purpose of this review is to synthesize the three DIs into a novel, cohesive summary algorithm with practical implementation recommendations to guide selective chest imaging in adult blunt trauma patients. Copyright © 2016 Elsevier Inc. All rights reserved.
Conroy-Beam, Daniel; Buss, David M
2016-01-01
Prior mate preference research has focused on the content of mate preferences. Yet in real life, people must select mates among potentials who vary along myriad dimensions. How do people incorporate information on many different mate preferences in order to choose which partner to pursue? Here, in Study 1, we compare seven candidate algorithms for integrating multiple mate preferences in a competitive agent-based model of human mate choice evolution. This model shows that a Euclidean algorithm is the most evolvable solution to the problem of selecting fitness-beneficial mates. Next, across three studies of actual couples (Study 2: n = 214; Study 3: n = 259; Study 4: n = 294) we apply the Euclidean algorithm toward predicting mate preference fulfillment overall and preference fulfillment as a function of mate value. Consistent with the hypothesis that mate preferences are integrated according to a Euclidean algorithm, we find that actual mates lie close in multidimensional preference space to the preferences of their partners. Moreover, this Euclidean preference fulfillment is greater for people who are higher in mate value, highlighting theoretically-predictable individual differences in who gets what they want. These new Euclidean tools have important implications for understanding real-world dynamics of mate selection.
Directory of Open Access Journals (Sweden)
Daniel Conroy-Beam
Full Text Available Prior mate preference research has focused on the content of mate preferences. Yet in real life, people must select mates among potentials who vary along myriad dimensions. How do people incorporate information on many different mate preferences in order to choose which partner to pursue? Here, in Study 1, we compare seven candidate algorithms for integrating multiple mate preferences in a competitive agent-based model of human mate choice evolution. This model shows that a Euclidean algorithm is the most evolvable solution to the problem of selecting fitness-beneficial mates. Next, across three studies of actual couples (Study 2: n = 214; Study 3: n = 259; Study 4: n = 294 we apply the Euclidean algorithm toward predicting mate preference fulfillment overall and preference fulfillment as a function of mate value. Consistent with the hypothesis that mate preferences are integrated according to a Euclidean algorithm, we find that actual mates lie close in multidimensional preference space to the preferences of their partners. Moreover, this Euclidean preference fulfillment is greater for people who are higher in mate value, highlighting theoretically-predictable individual differences in who gets what they want. These new Euclidean tools have important implications for understanding real-world dynamics of mate selection.
Using a genetic algorithm to solve fluid-flow problems
International Nuclear Information System (INIS)
Pryor, R.J.
1990-01-01
Genetic algorithms are based on the mechanics of the natural selection and natural genetics processes. These algorithms are finding increasing application to a wide variety of engineering optimization and machine learning problems. In this paper, the authors demonstrate the use of a genetic algorithm to solve fluid flow problems. Specifically, the authors use the algorithm to solve the one-dimensional flow equations for a pipe
Directory of Open Access Journals (Sweden)
D. Ramyachitra
2015-09-01
Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.
Ramyachitra, D; Sofia, M; Manikandan, P
2015-09-01
Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.
Rammal, Abbas; Perrin, Eric; Vrabie, Valeriu; Assaf, Rabih; Fenniri, Hassan
2017-07-01
Infrared spectroscopy provides useful information on the molecular compositions of biological systems related to molecular vibrations, overtones, and combinations of fundamental vibrations. Mid-infrared (MIR) spectroscopy is sensitive to organic and mineral components and has attracted growing interest in the development of biomarkers related to intrinsic characteristics of lignocellulose biomass. However, not all spectral information is valuable for biomarker construction or for applying analysis methods such as classification. Better processing and interpretation can be achieved by identifying discriminating wavenumbers. The selection of wavenumbers has been addressed through several variable- or feature-selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune, and others require additional information, such as concentrations. This paper proposes a new approach by combining a naïve Bayesian classifier with a genetic algorithm to identify discriminating spectral wavenumbers. The genetic algorithm uses a linear combination of an a posteriori probability and the Bayes error rate as the fitness function for optimization. Such a function allows the improvement of both the compactness and the separation of classes. This approach was tested to classify a small set of maize roots in soil according to their biodegradation process based on their MIR spectra. The results show that this optimization method allows better discrimination of the biodegradation process, compared with using the information of the entire MIR spectrum, the use of the spectral information at wavenumbers selected by a genetic algorithm based on a classical validity index or the use of the spectral information selected by combining a genetic algorithm with other methods, such as Linear Discriminant Analysis. The proposed method selects wavenumbers that correspond to principal vibrations of chemical functional groups of compounds that undergo degradation
de Munck, Jan C; van Houdt, Petra J; Gonçalves, Sónia I; van Wegen, Erwin; Ossenblok, Pauly P W
2013-01-01
Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time. Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3T scanner when using a sampling frequency of 2048 Hz. It even gives clean EEG when the EEG is sampled with only 256 Hz. Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with
Directory of Open Access Journals (Sweden)
Andreea Koreanschi
2017-02-01
Full Text Available In this paper, an ‘in-house’ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm’s performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods, namely the artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated.
Wisesty, Untari N.; Warastri, Riris S.; Puspitasari, Shinta Y.
2018-03-01
Cancer is one of the major causes of mordibility and mortality problems in the worldwide. Therefore, the need of a system that can analyze and identify a person suffering from a cancer by using microarray data derived from the patient’s Deoxyribonucleic Acid (DNA). But on microarray data has thousands of attributes, thus making the challenges in data processing. This is often referred to as the curse of dimensionality. Therefore, in this study built a system capable of detecting a patient whether contracted cancer or not. The algorithm used is Genetic Algorithm as feature selection and Momentum Backpropagation Neural Network as a classification method, with data used from the Kent Ridge Bio-medical Dataset. Based on system testing that has been done, the system can detect Leukemia and Colon Tumor with best accuracy equal to 98.33% for colon tumor data and 100% for leukimia data. Genetic Algorithm as feature selection algorithm can improve system accuracy, which is from 64.52% to 98.33% for colon tumor data and 65.28% to 100% for leukemia data, and the use of momentum parameters can accelerate the convergence of the system in the training process of Neural Network.
Lestari, A. W.; Rustam, Z.
2017-07-01
In the last decade, breast cancer has become the focus of world attention as this disease is one of the primary leading cause of death for women. Therefore, it is necessary to have the correct precautions and treatment. In previous studies, Fuzzy Kennel K-Medoid algorithm has been used for multi-class data. This paper proposes an algorithm to classify the high dimensional data of breast cancer using Fuzzy Possibilistic C-means (FPCM) and a new method based on clustering analysis using Normed Kernel Function-Based Fuzzy Possibilistic C-Means (NKFPCM). The objective of this paper is to obtain the best accuracy in classification of breast cancer data. In order to improve the accuracy of the two methods, the features candidates are evaluated using feature selection, where Laplacian Score is used. The results show the comparison accuracy and running time of FPCM and NKFPCM with and without feature selection.
de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino
2018-05-01
This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.
Khan, Fouad
2016-01-01
K-means is one of the most widely used clustering algorithms in various disciplines, especially for large datasets. However the method is known to be highly sensitive to initial seed selection of cluster centers. K-means++ has been proposed to overcome this problem and has been shown to have better accuracy and computational efficiency than k-means. In many clustering problems though -such as when classifying georeferenced data for mapping applications- standardization of clustering methodolo...
Liang, Jun; Van Abbema, Anne; Balazs, Mercedesz; Barrett, Kathy; Berezhkovsky, Leo; Blair, Wade S; Chang, Christine; Delarosa, Donnie; DeVoss, Jason; Driscoll, Jim; Eigenbrot, Charles; Goodacre, Simon; Ghilardi, Nico; MacLeod, Calum; Johnson, Adam; Bir Kohli, Pawan; Lai, Yingjie; Lin, Zhonghua; Mantik, Priscilla; Menghrajani, Kapil; Nguyen, Hieu; Peng, Ivan; Sambrone, Amy; Shia, Steven; Smith, Jan; Sohn, Sue; Tsui, Vickie; Ultsch, Mark; Williams, Karen; Wu, Lawren C; Yang, Wenqian; Zhang, Birong; Magnuson, Steven
2017-09-15
Herein we report identification of an imidazopyridine class of potent and selective TYK2 inhibitors, exemplified by prototype 6, through constraint of the rotatable amide bond connecting the pyridine and aryl rings of compound 1. Further optimization led to generation of compound 30 that potently inhibits the TYK2 enzyme and the IL-23 pathway in cells, exhibits selectivity against cellular JAK2 activity, and has good pharmacokinetic properties. In mice, compound 30 demonstrated dose-dependent reduction of IL-17 production in a PK/PD model as well as in an imiquimod-induced psoriasis model. In this efficacy model, the IL-17 decrease was accompanied by a reduction of ear thickness indicating the potential of TYK2 inhibition as a therapeutic approach for psoriasis patients. Copyright © 2017 Elsevier Ltd. All rights reserved.
Dorn-Wallenstein, Trevor Z.; Levesque, Emily
2017-11-01
Thanks to incredible advances in instrumentation, surveys like the Sloan Digital Sky Survey have been able to find and catalog billions of objects, ranging from local M dwarfs to distant quasars. Machine learning algorithms have greatly aided in the effort to classify these objects; however, there are regimes where these algorithms fail, where interesting oddities may be found. We present here an X-ray bright quasar misidentified as a red supergiant/X-ray binary, and a subsequent search of the SDSS quasar catalog for X-ray bright stars misidentified as quasars.
WDM Multicast Tree Construction Algorithms and Their Comparative Evaluations
Makabe, Tsutomu; Mikoshi, Taiju; Takenaka, Toyofumi
We propose novel tree construction algorithms for multicast communication in photonic networks. Since multicast communications consume many more link resources than unicast communications, effective algorithms for route selection and wavelength assignment are required. We propose a novel tree construction algorithm, called the Weighted Steiner Tree (WST) algorithm and a variation of the WST algorithm, called the Composite Weighted Steiner Tree (CWST) algorithm. Because these algorithms are based on the Steiner Tree algorithm, link resources among source and destination pairs tend to be commonly used and link utilization ratios are improved. Because of this, these algorithms can accept many more multicast requests than other multicast tree construction algorithms based on the Dijkstra algorithm. However, under certain delay constraints, the blocking characteristics of the proposed Weighted Steiner Tree algorithm deteriorate since some light paths between source and destinations use many hops and cannot satisfy the delay constraint. In order to adapt the approach to the delay-sensitive environments, we have devised the Composite Weighted Steiner Tree algorithm comprising the Weighted Steiner Tree algorithm and the Dijkstra algorithm for use in a delay constrained environment such as an IPTV application. In this paper, we also give the results of simulation experiments which demonstrate the superiority of the proposed Composite Weighted Steiner Tree algorithm compared with the Distributed Minimum Hop Tree (DMHT) algorithm, from the viewpoint of the light-tree request blocking.
Directory of Open Access Journals (Sweden)
Aref Yadollahi
2016-06-01
Full Text Available Axial fully-reversed strain-controlled (R=−1 fatigue experiments were performed to obtain data demonstrating the effects of building orientation (i.e. vertical versus horizontal and heat treatment on the fatigue behavior of 17–4 PH stainless steel (SS fabricated via Selective Laser Melting (SLM (Yadollahi et al., submitted for publication [1]. This data article provides detailed experimental data including cyclic stress-strain responses, variations of peak stresses during cyclic deformation, and fractography of post-mortem specimens for SLM 17–4 PH SS.
DEFF Research Database (Denmark)
Marinakis, Yannis; Dounias, Georgios; Jantzen, Jan
2009-01-01
The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper a metaheuristic algorithm is proposed in order to classify t...... other previously applied intelligent approaches....
Dasarathy, B. V.
1976-01-01
An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.
International Nuclear Information System (INIS)
Zhang, Chu; Zhou, Jianzhong; Li, Chaoshun; Fu, Wenlong; Peng, Tian
2017-01-01
Highlights: • A novel hybrid approach is proposed for wind speed forecasting. • The variational mode decomposition (VMD) is optimized to decompose the original wind speed series. • The input matrix and parameters of ELM are optimized simultaneously by using a hybrid BSA. • Results show that OVMD-HBSA-ELM achieves better performance in terms of prediction accuracy. - Abstract: Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the
Sakkas, Georgios; Sakellariou, Nikolaos
2018-05-01
Strong motion recordings are the key in many earthquake engineering applications and are also fundamental for seismic design. The present study focuses on the automated correction of accelerograms, analog and digital. The main feature of the proposed algorithm is the automatic selection for the cut-off frequencies based on a minimum spectral value in a predefined frequency bandwidth, instead of the typical signal-to-noise approach. The algorithm follows the basic steps of the correction procedure (instrument correction, baseline correction and appropriate filtering). Besides the corrected time histories, Peak Ground Acceleration, Peak Ground Velocity, Peak Ground Displacement values and the corrected Fourier Spectra are also calculated as well as the response spectra. The algorithm is written in Matlab environment, is fast enough and can be used for batch processing or in real-time applications. In addition, the possibility to also perform a signal-to-noise ratio is added as well as to perform causal or acausal filtering. The algorithm has been tested in six significant earthquakes (Kozani-Grevena 1995, Aigio 1995, Athens 1999, Lefkada 2003 and Kefalonia 2014) of the Greek territory with analog and digital accelerograms.
Energy Technology Data Exchange (ETDEWEB)
Mahmood, U; Erdi, Y; Wang, W [Memorial Sloan Kettering Cancer Center, NY, NY (United States)
2014-06-01
Purpose: To assess the impact of General Electrics automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of a pediatric anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, 80 mA, 0.7s rotation time. Image quality was assessed by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: For the baseline protocol, CNR was found to decrease from 0.460 ± 0.182 to 0.420 ± 0.057 when kVa was activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.620 ± 0.040. The liver dose decreased by 30% with kVa activation. Conclusion: Application of kVa reduces the liver dose up to 30%. However, reduction in image quality for abdominal scans occurs when using the automated tube voltage selection feature at the baseline protocol. As demonstrated by the CNR and NPS analysis, the texture and magnitude of the noise in reconstructed images at ASiR 40% was found to be the same as our baseline images. We have demonstrated that 30% dose reduction is possible when using 40% ASiR with kVa in pediatric patients.
International Nuclear Information System (INIS)
Mahmood, U; Erdi, Y; Wang, W
2014-01-01
Purpose: To assess the impact of General Electrics automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of a pediatric anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, 80 mA, 0.7s rotation time. Image quality was assessed by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: For the baseline protocol, CNR was found to decrease from 0.460 ± 0.182 to 0.420 ± 0.057 when kVa was activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.620 ± 0.040. The liver dose decreased by 30% with kVa activation. Conclusion: Application of kVa reduces the liver dose up to 30%. However, reduction in image quality for abdominal scans occurs when using the automated tube voltage selection feature at the baseline protocol. As demonstrated by the CNR and NPS analysis, the texture and magnitude of the noise in reconstructed images at ASiR 40% was found to be the same as our baseline images. We have demonstrated that 30% dose reduction is possible when using 40% ASiR with kVa in pediatric patients
Fouad, Marwa A; Tolba, Enas H; El-Shal, Manal A; El Kerdawy, Ahmed M
2018-05-11
The justified continuous emerging of new β-lactam antibiotics provokes the need for developing suitable analytical methods that accelerate and facilitate their analysis. A face central composite experimental design was adopted using different levels of phosphate buffer pH, acetonitrile percentage at zero time and after 15 min in a gradient program to obtain the optimum chromatographic conditions for the elution of 31 β-lactam antibiotics. Retention factors were used as the target property to build two QSRR models utilizing the conventional forward selection and the advanced nature-inspired firefly algorithm for descriptor selection, coupled with multiple linear regression. The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability. Williams-Hotelling test and student's t-test showed that there is no statistical significant difference between the models' results. Y-randomization validation showed that the obtained models are due to significant correlation between the selected molecular descriptors and the analytes' chromatographic retention. These results indicate that the generated FS-MLR and FFA-MLR models are showing comparable quality on both the training and validation levels. They also gave comparable information about the molecular features that influence the retention behavior of β-lactams under the current chromatographic conditions. We can conclude that in some cases simple conventional feature selection algorithm can be used to generate robust and predictive models comparable to that are generated using advanced ones. Copyright © 2018 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Federal Energy Technology Center
1999-01-01
The goal of the U.S. Department of Energy (DOE) Clean Coal Technology (CCT) program is to furnish the energy marketplace with a number of advanced, more efficient, and environmentally responsible coal utilization technologies through demonstration projects. These projects seek to establish the commercial feasibility of the most promising advanced coal technologies that have developed beyond the proof-of-concept stage. This document serves as a DOE post-project assessment of a project selected in CCT Round 2. The project is described in the report ''Demonstration of Selective Catalytic Reduction (SCR) Technology for the Control of Nitrogen Oxide (NO(sub x)) Emissions from High-Sulfur, Coal-Fired Boilers'' (Southern Company Services 1990). In June 1990, Southern Company Services (Southern) entered into a cooperative agreement to conduct the study. Southern was a cofunder and served as the host at Gulf Power Company's Plant Crist. Other participants and cofunders were EPRI (formerly the Electric Power Research Institute) and Ontario Hydro. DOE provided 40 percent of the total project cost of$23 million. The long-term operation phase of the demonstration was started in July 1993 and was completed in July 1995. This independent evaluation is based primarily on information from Southern's Final Report (Southern Company Services 1996). The SCR process consists of injecting ammonia (NH(sub 3)) into boiler flue gas and passing the 3 flue gas through a catalyst bed where the NO(sub x) and NH(sub 3) react to form nitrogen and water vapor. The objectives of the demonstration project were to investigate: Performance of a wide variety of SCR catalyst compositions, geometries, and manufacturing methods at typical U.S. high-sulfur coal-fired utility operating conditions; Catalyst resistance to poisoning by trace metal species present in U.S. coals but not present, or present at much lower concentrations, in fuels from other countries; and Effects on the balance-of-plant equipment
Directory of Open Access Journals (Sweden)
Aitzol Astigarraga
2016-01-01
Full Text Available Brain-Computer Interfaces (BCIs have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG- based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.
Optimisation algorithms for ECG data compression.
Haugland, D; Heber, J G; Husøy, J H
1997-07-01
The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.
International Nuclear Information System (INIS)
Morris, Michael I.; Hulet, Greg A.
2003-01-01
The U.S. Department of Energy's (DOE's) Transuranic and Mixed Waste Focus Area (TMFA), funded from fiscal year (FY) 1996 though FY 2002, was tasked with finding solutions for the mixed waste treatment problems of the DOE complex. During TMFA's initial technical baseline development process, three of the top four technology deficiencies identified were the need for amalgamation, stabilization, and separation/removal technologies for the treatment of mercury-contaminated mixed waste. The Mercury Working Group (HgWG), a selected group of representatives from DOE sites with significant mercury waste inventories, assisted TMFA in soliciting, identifying, initiating, and managing efforts to address these areas. Solicitations and contract awards were made to the private sector to demonstrate both the amalgamation and stabilization processes using both actual mixed wastes and surrogate samples. The goal was to develop separation and removal processes that will meet DOE's needs. This paper discusses the technology selection process, development activities, and the accomplishments of TMFA through these various activities
Mercader, Andrew G; Duchowicz, Pablo R; Fernández, Francisco M; Castro, Eduardo A
2010-09-27
We compare three methods for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. On the one hand is our enhanced replacement method (ERM) and on the other is the simpler replacement method (RM) and the genetic algorithm (GA). These methods avoid the impracticable full search for optimal variables in large sets of molecular descriptors. Present results for 10 different experimental databases suggest that the ERM is clearly preferable to the GA that is slightly better than the RM. However, the latter approach requires the smallest amount of linear regressions and, consequently, the lowest computation time.
Radaydeh, Redha Mahmoud Mesleh; Alouini, Mohamed-Slim
2010-01-01
The performance of transmit antenna selection for threshold-based maximal ratio combining (MRC) diversity receivers in the presence of multiple co-channel interfering signals is studied. The impact of imperfect channel estimation of desired user
International Nuclear Information System (INIS)
Lin Lin; Chao Yang; Jiangfeng Lu; Lexing Ying; Weinan, E.
2009-01-01
We present an efficient parallel algorithm and its implementation for computing the diagonal of H -1 where H is a 2D Kohn-Sham Hamiltonian discretized on a rectangular domain using a standard second order finite difference scheme. This type of calculation can be used to obtain an accurate approximation to the diagonal of a Fermi-Dirac function of H through a recently developed pole-expansion technique LinLuYingE2009. The diagonal elements are needed in electronic structure calculations for quantum mechanical systems HohenbergKohn1964, KohnSham 1965,DreizlerGross1990. We show how elimination tree is used to organize the parallel computation and how synchronization overhead is reduced by passing data level by level along this tree using the technique of local buffers and relative indices. We analyze the performance of our implementation by examining its load balance and communication overhead. We show that our implementation exhibits an excellent weak scaling on a large-scale high performance distributed parallel machine. When compared with standard approach for evaluating the diagonal a Fermi-Dirac function of a Kohn-Sham Hamiltonian associated a 2D electron quantum dot, the new pole-expansion technique that uses our algorithm to compute the diagonal of (H-z i I) -1 for a small number of poles z i is much faster, especially when the quantum dot contains many electrons.
Energy Technology Data Exchange (ETDEWEB)
Lin, Lin; Yang, Chao; Lu, Jiangfeng; Ying, Lexing; E, Weinan
2009-09-25
We present an efficient parallel algorithm and its implementation for computing the diagonal of $H^-1$ where $H$ is a 2D Kohn-Sham Hamiltonian discretized on a rectangular domain using a standard second order finite difference scheme. This type of calculation can be used to obtain an accurate approximation to the diagonal of a Fermi-Dirac function of $H$ through a recently developed pole-expansion technique \\cite{LinLuYingE2009}. The diagonal elements are needed in electronic structure calculations for quantum mechanical systems \\citeHohenbergKohn1964, KohnSham 1965,DreizlerGross1990. We show how elimination tree is used to organize the parallel computation and how synchronization overhead is reduced by passing data level by level along this tree using the technique of local buffers and relative indices. We analyze the performance of our implementation by examining its load balance and communication overhead. We show that our implementation exhibits an excellent weak scaling on a large-scale high performance distributed parallel machine. When compared with standard approach for evaluating the diagonal a Fermi-Dirac function of a Kohn-Sham Hamiltonian associated a 2D electron quantum dot, the new pole-expansion technique that uses our algorithm to compute the diagonal of $(H-z_i I)^-1$ for a small number of poles $z_i$ is much faster, especially when the quantum dot contains many electrons.
Directory of Open Access Journals (Sweden)
Mohammad Ali Barati
2016-04-01
Full Text Available Multi-period models of portfolio selection have been developed in the literature with respect to certain assumptions. In this study, for the first time, the portfolio selection problem has been modeled based on mean-semi variance with transaction cost and minimum transaction lots considering functional constraints and fuzzy parameters. Functional constraints such as transaction cost and minimum transaction lots were included. In addition, the returns on assets parameters were considered as trapezoidal fuzzy numbers. An efficient genetic algorithm (GA was designed, results were analyzed using numerical instances and sensitivity analysis were executed. In the numerical study, the problem was solved based on the presence or absence of each mode of constraints including transaction costs and minimum transaction lots. In addition, with the use of sensitivity analysis, the results of the model were presented with the variations of minimum expected rate of programming periods.
Directory of Open Access Journals (Sweden)
Thakral B
2006-01-01
Full Text Available Background: HIV/AIDS pandemic brought into focus the importance of safe blood donor pool. Aims: To analyze true seroprevalence of HIV infection in our blood donors and devise an algorithm for donor recall avoiding unnecessary referrals to voluntary counseling and testing centre (VCTC. Materials and Methods: 39,784 blood units were screened for anti-HIV 1/2 using ELISA immunoassay (IA-1. Samples which were repeat reactive on IA-1 were further tested using two different immunoassays (IA-2 and IA-3 and Western blot (WB. Based on results of these sequential IAs and WB, an algorithm for recall of true HIV seroreactive blood donors is suggested for countries like India where nucleic acid testing or p24 antigen assays are not mandatory and given the limited resources may not be feasible. Results: The anti-HIV seroreactivity by repeat IA-1, IA-2, IA-3 and WB were 0.16%, 0.11%, 0.098% and 0.07% respectively. Of the 44 IA-1 reactive samples, 95.2% (20/21 of the seroreactive samples by both IA-2 and IA-3 were also WB positive and 100% (6/6 of the non-reactive samples by these IAs were WB negative. IA signal/cutoff ratio was significantly low in biological false reactive donors. WB indeterminate results were largely due to non-specific reactivity to gag protein (p55. Conclusions: HIV seroreactivity by sequential immunoassays (IA-1, IA-2 and IA-3; comparable to WHO Strategy-III prior to donor recall results in decreased referral to VCTC as compared to single IA (WHO Strategy-I being followed currently in India. Moreover, this strategy will repose donor confidence in our blood transfusion services and strengthen voluntary blood donation program.
Directory of Open Access Journals (Sweden)
Suyan Tian
Full Text Available The existence of fundamental differences between lung adenocarcinoma (AC and squamous cell carcinoma (SCC in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.
International Nuclear Information System (INIS)
Escalier des Orres, P.; Greneche, D.
1993-01-01
A regulatory guidance has been recently set up in France for the safety assessment of radwaste deep geological disposal: the present paper deals with the methodology related to the safety demonstration of such a disposal, particularly the situations to be taken into account to address the potential evolution of the repository under natural or human induced events. This approach, based on a selection of events considered as reasonably envisageable, relies on a reference scenario characterized by a great stability of the geological formation and on hypothetical situations corresponding to the occurrence of random events of natural origin or of conventional nature. The implementation of this methodology within the framework of the IPSN (Protection and Nuclear Safety Institute, CEA) participation in the CEC EVEREST project is addressed. This programme consists in the evaluation of the sensitivity of the radiological consequences associated to deep radwaste disposal systems to the different elements of the performance assessment (scenario characteristics, phenomena, physico-chemical parameters) in three types of geological formations (granite, salt and clay).(author). 11 refs., 3 tabs
Directory of Open Access Journals (Sweden)
Geoff Sanders
2008-04-01
Full Text Available Women have been reported to perform better with hand rather than arm movements (Sanders and Walsh, 2007 and with visual stimuli in near rather than far space (Sanders, Sinclair and Walsh, 2007. Men performed better with the arm and in far space. These reciprocal patterns of sex differences appear as Muscle*Sex and Space*Sex interactions. We investigated these claims using target cancellation tasks in which task difficulty was manipulated by varying target size or the number of distracters. In Study 1 we did not find the Muscle*Sex or the Space*Sex interaction. We argue that ballistic movement was too simple to reveal the Muscle*Sex interaction. However, a trend for the Space*Sex interaction suggested task difficulty was set too high. Study 2 introduced easier levels of difficulty and the overall Space*Sex interaction narrowly failed to reach significance (p = 0.051. In Study 3 the Space*Sex interaction was significant (p = 0.001. A review of the present, and four previously published, studies indicates that task selection is critical if the Space*Sex interaction and its associated reciprocal within-sex differences are to be demonstrated without the obscuring effects of Space and Difficulty. These sex differences are compatible with predictions from the hunter-gatherer hypothesis. Implications for two-visual-system-models are considered.
Heim, Catherine; Cole, Elaine; West, Anita; Tai, Nigel; Brohi, Karim
2016-09-01
Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement. We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator. Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care. TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs. Copyright © 2016 Elsevier
Directory of Open Access Journals (Sweden)
Anna Bourmistrova
2011-02-01
Full Text Available The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS vehicle, though it is also applicable to two-wheel-steering (TWS vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while the dynamic center of rotation is the output of dynamic equations of motion of the vehicle using steering angle and velocity measurements as inputs. We use kinematic condition of steering to set the steering angles in such a way that the kinematic center of rotation of the vehicle sits at a desired point. At low speeds the ideal and actual paths of the vehicle are very close. With increase of forward speed the road and tire characteristics, along with the motion dynamics of the vehicle cause the vehicle to turn about time-varying points. By adjusting the steering angles, our algorithm controls the dynamic turning center of the vehicle so that it coincides with the road curvature center, hence keeping the vehicle on a given road autonomously. The position and orientation errors are used as feedback signals in a closed loop control to adjust the steering angles. The application of the presented autodriver algorithm demonstrates reliable performance under different driving conditions.
Oh, Duk-Soon; Widlund, Olof B.; Zampini, Stefano; Dohrmann, Clark R.
2017-01-01
A BDDC domain decomposition preconditioner is defined by a coarse component, expressed in terms of primal constraints, a weighted average across the interface between the subdomains, and local components given in terms of solvers of local subdomain problems. BDDC methods for vector field problems discretized with Raviart-Thomas finite elements are introduced. The methods are based on a deluxe type of weighted average and an adaptive selection of primal constraints developed to deal with coefficients with high contrast even inside individual subdomains. For problems with very many subdomains, a third level of the preconditioner is introduced.
Oh, Duk-Soon
2017-06-13
A BDDC domain decomposition preconditioner is defined by a coarse component, expressed in terms of primal constraints, a weighted average across the interface between the subdomains, and local components given in terms of solvers of local subdomain problems. BDDC methods for vector field problems discretized with Raviart-Thomas finite elements are introduced. The methods are based on a deluxe type of weighted average and an adaptive selection of primal constraints developed to deal with coefficients with high contrast even inside individual subdomains. For problems with very many subdomains, a third level of the preconditioner is introduced.
A New Modified Firefly Algorithm
Directory of Open Access Journals (Sweden)
Medha Gupta
2016-07-01
Full Text Available Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.
International Nuclear Information System (INIS)
Monsma, F.J. Jr.; Sibley, D.R.
1989-01-01
The presence of D1 dopamine receptors in the parathyroid gland has been proposed based on the demonstration of dopaminergic regulation of adenylate cyclase activity and parathyroid hormone release in dispersed bovine parathyroid cells. Using a radioiodinated D1 selective antagonist [125I]-SCH 23982, we have now directly labeled and characterized the D1 dopamine receptors in bovine parathyroid gland membranes. [125I]-SCH 23982 binds in a saturable manner with high affinity and low nonspecific binding to membranes prepared from bovine parathyroid glands. D1 dopamine receptors are present in this preparation at a concentration of approximately 130 fMoles/mg protein and [125I]-SCH 23982 binding increases with increasing protein concentration in a linear fashion. Determination of the Kd using the association (k1) and dissociation (k-1) rate constants revealed good agreement with the Kd determined by saturation analysis (390 pM vs. 682 pM, respectively). Inhibition of 0.3 nM [125I]-SCH 23982 binding by a series of dopaminergic antagonists verified the D1 nature of this binding site, exhibiting appropriate affinities and rank order of potency. The competition curves of all antagonists exhibited Hill coefficients that were not significantly different from 1. Inhibition of [125I]-SCH 23982 binding by dopamine and other dopaminergic agonists revealed the presence of high and low affinity agonist binding sites. Addition of 200 microM GppNHp effected a complete conversion of high affinity dopamine binding sites to a homogeneous population of low affinity dopamine sites. The D1 receptors identified in the parathyroid gland with [125I]-SCH 23982 appear to be pharmacologically identical with those previously characterized in the central nervous system
Poluyan, A. Y.; Fugarov, D. D.; Purchina, O. A.; Nesterchuk, V. V.; Smirnova, O. V.; Petrenkova, S. B.
2018-05-01
To date, the problems associated with the detection of errors in digital equipment (DE) systems for the automation of explosive objects of the oil and gas complex are extremely actual. Especially this problem is actual for facilities where a violation of the accuracy of the DE will inevitably lead to man-made disasters and essential material damage, at such facilities, the diagnostics of the accuracy of the DE operation is one of the main elements of the industrial safety management system. In the work, the solution of the problem of selecting the optimal variant of the errors detection system of errors detection by a validation criterion. Known methods for solving these problems have an exponential valuation of labor intensity. Thus, with a view to reduce time for solving the problem, a validation criterion is compiled as an adaptive bionic algorithm. Bionic algorithms (BA) have proven effective in solving optimization problems. The advantages of bionic search include adaptability, learning ability, parallelism, the ability to build hybrid systems based on combining. [1].
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Givianrad Hadi Mohammad
2013-01-01
Full Text Available The simultaneous determination of sulfamethoxazole (SMX and trimethoprim (TMP mixtures in bovine milk by spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By means of multivariate calibration methods, such as partial least square (PLS regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Genetic algorithm (GA is a suitable method for selecting wavelengths for PLS calibration of mixtures with almost identical spectra without loss of prediction capacity using the spectrophotometric method. In this study, the calibration model based on absorption spectra in the 200-400 nm range for 25 different mixtures of SMX and TMP Calibration matrices were formed form samples containing 0.25-20 and 0.3-21 μg mL-1 for SMX and TMP, at pH=10, respectively. The root mean squared error of deviation (RMSED for SMX and TMP with PLS and genetic algorithm partial least square (GAPLS were 0.242, 0.066 μgmL-1 and 0.074, 0.027 μg mL-1, respectively. This procedure was allowed the simultaneous determination of SMX and TMP in synthetic and real samples and good reliability of the determination was proved.
Directory of Open Access Journals (Sweden)
Jin-peng Liu
2017-07-01
Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.
Ghavami, Raoof; Najafi, Amir; Sajadi, Mohammad; Djannaty, Farhad
2008-09-01
In order to accurately simulate (13)C NMR spectra of hydroxy, polyhydroxy and methoxy substituted flavonoid a quantitative structure-property relationship (QSPR) model, relating atom-based calculated descriptors to (13)C NMR chemical shifts (ppm, TMS=0), is developed. A dataset consisting of 50 flavonoid derivatives was employed for the present analysis. A set of 417 topological, geometrical, and electronic descriptors representing various structural characteristics was calculated and separate multilinear QSPR models were developed between each carbon atom of flavonoid and the calculated descriptors. Genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used to select the descriptors and to generate the correlation models. Analysis of the results revealed a correlation coefficient and root mean square error (RMSE) of 0.994 and 2.53ppm, respectively, for the prediction set.
Gu, Huidong; Liu, Guowen; Wang, Jian; Aubry, Anne-Françoise; Arnold, Mark E
2014-09-16
A simple procedure for selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bioanalytical LC-MS/MS assays is reported. The correct weighting factor is determined by the relationship between the standard deviation of instrument responses (σ) and the concentrations (x). The weighting factor of 1, 1/x, or 1/x(2) should be selected if, over the entire concentration range, σ is a constant, σ(2) is proportional to x, or σ is proportional to x, respectively. For the first time, we demonstrated with detailed scientific reasoning, solid historical data, and convincing justification that 1/x(2) should always be used as the weighting factor for all bioanalytical LC-MS/MS assays. The impacts of using incorrect weighting factors on curve stability, data quality, and assay performance were thoroughly investigated. It was found that the most stable curve could be obtained when the correct weighting factor was used, whereas other curves using incorrect weighting factors were unstable. It was also found that there was a very insignificant impact on the concentrations reported with calibration curves using incorrect weighting factors as the concentrations were always reported with the passing curves which actually overlapped with or were very close to the curves using the correct weighting factor. However, the use of incorrect weighting factors did impact the assay performance significantly. Finally, the difference between the weighting factors of 1/x(2) and 1/y(2) was discussed. All of the findings can be generalized and applied into other quantitative analysis techniques using calibration curves with weighted least-squares regression algorithm.
DEFF Research Database (Denmark)
Mahnke, Martina; Uprichard, Emma
2014-01-01
Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...
Gilbert, George L.
1983-01-01
An apparatus is described in which effects of pressure, volume, and temperature changes on a gas can be observed simultaneously. Includes use of the apparatus in demonstrating Boyle's, Gay-Lussac's, and Charles' Laws, attractive forces, Dalton's Law of Partial pressures, and in illustrating measurable vapor pressures of liquids and some solids.…
Gilbert, George L., Ed.
1987-01-01
Describes two demonstrations to illustrate characteristics of substances. Outlines a method to detect the changes in pH levels during the electrolysis of water. Uses water pistols, one filled with methane gas and the other filled with water, to illustrate the differences in these two substances. (TW)
Directory of Open Access Journals (Sweden)
Lei Shi
2018-01-01
Full Text Available In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA and tabu search (TS is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy.
Shi, Lei; Wan, Youchuan; Gao, Xianjun
2018-01-01
In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. PMID:29581721
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Asenjo Juan A
2011-04-01
's primary sequence into a group of functionally non-disruptive amino acids and a second group of functionally deleterious amino acids. Conclusions With this approach, not only conserved amino acid positions in a protein family can be labeled as functionally relevant, but also non-conserved amino acid positions can be identified to have a physicochemically meaningful functional effect. These results become a discriminative tool in the selection and elaboration of rational mutagenesis strategies for the protein. They can also be used to predict if a given nsSNP, identified, for instance, in a genomic-scale analysis, can have a functional implication for a particular protein and which nsSNPs are most likely to be functionally silent for a protein. This analytical tool could be used to rapidly and automatically discard any irrelevant nsSNP and guide the research focus toward functionally significant mutations. Based on preliminary results and applications, this technique shows promising performance as a valuable bioinformatics tool to aid in the development of new protein variants and in the understanding of function-structure relationships in proteins.
Zahl Eriksson, Ane Gerda; Ducie, Jen; Ali, Narisha; McGree, Michaela E; Weaver, Amy L; Bogani, Giorgio; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N; Abu-Rustum, Nadeem R; Mariani, Andrea; Leitao, Mario M
2016-03-01
To assess clinicopathologic outcomes between two nodal assessment approaches in patients with endometrioid endometrial carcinoma and limited myoinvasion. Patients with endometrial cancer at two institutions were reviewed. At one institution, a complete pelvic and para-aortic lymphadenectomy to the renal veins was performed in select cases deemed at risk for nodal metastasis due to grade 3 cancer and/or primary tumor diameter>2cm (LND cohort). This is a historic approach at this institution. At the other institution, a sentinel lymph node mapping algorithm was used per institutional protocol (SLN cohort). Low risk was defined as endometrioid adenocarcinoma with myometrial invasion <50%. Macrometastasis, micrometastasis, and isolated tumor cells were all considered node-positive. Of 1135 cases identified, 642 (57%) were managed with an SLN approach and 493 (43%) with an LND approach. Pelvic nodes (PLNs) were removed in 93% and 58% of patients, respectively (P<0.001); para-aortic nodes (PANs) were removed in 14.5% and 50% of patients, respectively (P<0.001). Median number of PLNs removed was 6 and 34, respectively; median number of PANs removed was 5 and 16, respectively (both P<0.001). Metastasis to PLNs was detected in 5.1% and 2.6% of patients, respectively (P=0.03), and to PANs in 0.8% and 1.0%, respectively (P=0.75). The 3-year disease-free survival rates were 94.9% (95% CI, 92.4-97.5) and 96.8% (95% CI, 95.2-98.5), respectively. Our findings support the use of either strategy for endometrial cancer staging, with no apparent detriment in adhering to the SLN algorithm. The clinical significance of disease detected on ultrastaging and the role of adjuvant therapy is yet to be determined. Copyright © 2016 Elsevier Inc. All rights reserved.
DEFF Research Database (Denmark)
Christensen, Lars P.B.; Larsen, Jan
2006-01-01
A general Variational Bayesian framework for iterative data and parameter estimation for coherent detection is introduced as a generalization of the EM-algorithm. Explicit solutions are given for MIMO channel estimation with Gaussian prior and noise covariance estimation with inverse-Wishart prior....... Simulation of a GSM-like system provides empirical proof that the VBEM-algorithm is able to provide better performance than the EM-algorithm. However, if the posterior distribution is highly peaked, the VBEM-algorithm approaches the EM-algorithm and the gain disappears. The potential gain is therefore...
Valera, Pamela; McClernon, F Joseph; Burkholder, Greer; Mugavero, Michael J; Willig, James; O'Cleirigh, Conall; Cropsey, Karen L
2017-07-01
Algorithm-based treatments (AT) may be an effective clinical tool to aid HIV clinicians in prescribing pharmacotherapy to increase smoking cessation among people living with HIV (PLWH). Initial results from AT indicated significant increases in abstinence self-efficacy and medication utilization and declines in cigarettes smoked per day across time. Given historical racial disparities, it is unclear if both African Americans and White smokers would benefit equally from this type of intervention. Thus, the aim of this study was to examine racial differences in response to AT guided smoking cessation for African American and White smokers living with HIV. One hundred PLWH smokers (n = 100) were randomized to receive either AT guided smoking cessation or Treatment as Usual (TAU) which consisted of instructing participants to talk to a provider about smoking cessation assistance when ready to make a quit attempt. Participants were African American (75%) and White (25%) and majority men (71%) who had never been married (56%). African Americans smoked fewer cigarettes and were more likely to smoke mentholated cigarettes compared to White smokers at baseline. African Americans increased their use of other tobacco products (cigars/cigarillos) over time relative to White smokers. A significant interaction between race and quit goal was observed, with White smokers who reported complete abstinence as their goal having higher quit rates, while African Americans who reported a goal other than complete abstinence demonstrating higher quit rates. The increased use of cigars/cigarillos during quit attempts as well as having a goal other than complete abstinence should be considered when applying algorithm based interventions for PLWH African American smokers.
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Dezhi Wang
2018-02-01
Full Text Available In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms—decision tree (DT, support vector machine (SVM, and random forest (RF—were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%; for object-based image analysis, RF could achieve the highest overall accuracy (82.40%, and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar’s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1 as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT could well be identified. However, for complex and easily
De Götzen , Amalia; Mion , Luca; Tache , Olivier
2007-01-01
International audience; We call sound algorithms the categories of algorithms that deal with digital sound signal. Sound algorithms appeared in the very infancy of computer. Sound algorithms present strong specificities that are the consequence of two dual considerations: the properties of the digital sound signal itself and its uses, and the properties of auditory perception.
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
DEFF Research Database (Denmark)
Husfeldt, Thore
2015-01-01
This chapter presents an introduction to graph colouring algorithms. The focus is on vertex-colouring algorithms that work for general classes of graphs with worst-case performance guarantees in a sequential model of computation. The presentation aims to demonstrate the breadth of available...
Vasquez, A K; Nydam, D V; Foditsch, C; Wieland, M; Lynch, R; Eicker, S; Virkler, P D
2018-06-01
An algorithm using only computer-based records to guide selective dry-cow therapy was evaluated at a New York State dairy farm via a randomized field trial. DairyComp 305 (Valley Ag Software, Tulare, CA) and Dairy Herd Improvement Association test-day data were used to identify cows as low risk (cows that might not benefit from dry-cow antibiotics) or high risk (cows that will likely benefit). Low-risk cows were those that had all of the following: somatic cell count (SCC) ≤200,000 cells/mL at last test, an average SCC ≤200,000 cells/mL over the last 3 tests, no signs of clinical mastitis at dry-off, and no more than 1 clinical mastitis event in the current lactation. Low-risk cows were randomly assigned to receive intramammary antibiotics and external teat sealant (ABXTS) or external teat sealant only (TS) at dry-off. Using pre-dry-off and postcalving quarter-level culture results, low-risk quarters were assessed for microbiological cure risk and new infection risk. Groups were also assessed for differences in first-test milk yield and linear scores, individual milk weights for the first 30 d, and culling and mastitis events before 30 d in milk. A total of 304 cows and 1,040 quarters in the ABXTS group and 307 cows and 1,058 quarters in the TS group were enrolled. Among cows to be dried, the proportion of cows that met low-risk criteria was 64% (n = 611/953). Of cultures eligible for bacteriological cure analysis (n = 171), 93% of ABXTS cured, whereas 88% of TS cured. Of the non-cures, 95% were contributed by the minor pathogens coagulase-negative staphylococci (n = 19/20). These organisms also accounted for 57.5% of new infections (n = 77/134). We found no statistical differences between treatment groups for new infection risk (TS = 7.3% quarters experiencing new infections; ABXTS = 5.5%), milk production (ABXTS = 40.5 kg; TS = 41.2 kg), linear scores (ABXTS = 2.5; TS = 2.7), culling events (ABXTS, n = 18; TS, n = 15), or clinical mastitis events (ABXTS, n
Distributed picture compilation demonstration
Alexander, Richard; Anderson, John; Leal, Jeff; Mullin, David; Nicholson, David; Watson, Graham
2004-08-01
A physical demonstration of distributed surveillance and tracking is described. The demonstration environment is an outdoor car park overlooked by a system of four rooftop cameras. The cameras extract moving objects from the scene, and these objects are tracked in a decentralized way, over a real communication network, using the information form of the standard Kalman filter. Each node therefore has timely access to the complete global picture and because there is no single point of failure in the system, it is robust. The demonstration system and its main components are described here, with an emphasis on some of the lessons we have learned as a result of applying a corpus of distributed data fusion theory and algorithms in practice. Initial results are presented and future plans to scale up the network are also outlined.
Selecting materialized views in a data warehouse
Zhou, Lijuan; Liu, Chi; Liu, Daxin
2003-01-01
A Data Warehouse contains lots of materialized views over the data provided by the distributed heterogeneous databases for the purpose of efficiently implementing decision-support or OLAP queries. It is important to select the right view to materialize that answer a given set of queries. In this paper, we have addressed and designed algorithm to select a set of views to materialize in order to answer the most queries under the constraint of a given space. The algorithm presented in this paper aim at making out a minimum set of views, by which we can directly respond to as many as possible user"s query requests. We use experiments to demonstrate our approach. The results show that our algorithm works better. We implemented our algorithms and a performance study of the algorithm shows that the proposed algorithm gives a less complexity and higher speeds and feasible expandability.
Joux, Antoine
2009-01-01
Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic
Empirical study of self-configuring genetic programming algorithm performance and behaviour
International Nuclear Information System (INIS)
KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkin, E; KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkina, M
2015-01-01
The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems
Krepper, Gabriela; Romeo, Florencia; Fernandes, David Douglas de Sousa; Diniz, Paulo Henrique Gonçalves Dias; de Araújo, Mário César Ugulino; Di Nezio, María Susana; Pistonesi, Marcelo Fabián; Centurión, María Eugenia
2018-01-01
Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12 mg kg- 1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (w w- 1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59 mg kg- 1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.
Weigel, K A; VanRaden, P M; Norman, H D; Grosu, H
2017-12-01
In the early 1900s, breed society herdbooks had been established and milk-recording programs were in their infancy. Farmers wanted to improve the productivity of their cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not been laid. Early animal breeders struggled to identify genetically superior families using performance records that were influenced by local environmental conditions and herd-specific management practices. Daughter-dam comparisons were used for more than 30 yr and, although genetic progress was minimal, the attention given to performance recording, genetic theory, and statistical methods paid off in future years. Contemporary (herdmate) comparison methods allowed more accurate accounting for environmental factors and genetic progress began to accelerate when these methods were coupled with artificial insemination and progeny testing. Advances in computing facilitated the implementation of mixed linear models that used pedigree and performance data optimally and enabled accurate selection decisions. Sequencing of the bovine genome led to a revolution in dairy cattle breeding, and the pace of scientific discovery and genetic progress accelerated rapidly. Pedigree-based models have given way to whole-genome prediction, and Bayesian regression models and machine learning algorithms have joined mixed linear models in the toolbox of modern animal breeders. Future developments will likely include elucidation of the mechanisms of genetic inheritance and epigenetic modification in key biological pathways, and genomic data will be used with data from on-farm sensors to facilitate precision management on modern dairy farms. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Mingwei Leng
2013-01-01
Full Text Available The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced.
Semioptimal practicable algorithmic cooling
International Nuclear Information System (INIS)
Elias, Yuval; Mor, Tal; Weinstein, Yossi
2011-01-01
Algorithmic cooling (AC) of spins applies entropy manipulation algorithms in open spin systems in order to cool spins far beyond Shannon's entropy bound. Algorithmic cooling of nuclear spins was demonstrated experimentally and may contribute to nuclear magnetic resonance spectroscopy. Several cooling algorithms were suggested in recent years, including practicable algorithmic cooling (PAC) and exhaustive AC. Practicable algorithms have simple implementations, yet their level of cooling is far from optimal; exhaustive algorithms, on the other hand, cool much better, and some even reach (asymptotically) an optimal level of cooling, but they are not practicable. We introduce here semioptimal practicable AC (SOPAC), wherein a few cycles (typically two to six) are performed at each recursive level. Two classes of SOPAC algorithms are proposed and analyzed. Both attain cooling levels significantly better than PAC and are much more efficient than the exhaustive algorithms. These algorithms are shown to bridge the gap between PAC and exhaustive AC. In addition, we calculated the number of spins required by SOPAC in order to purify qubits for quantum computation. As few as 12 and 7 spins are required (in an ideal scenario) to yield a mildly pure spin (60% polarized) from initial polarizations of 1% and 10%, respectively. In the latter case, about five more spins are sufficient to produce a highly pure spin (99.99% polarized), which could be relevant for fault-tolerant quantum computing.
International Nuclear Information System (INIS)
1984-04-01
Low level radioactive wastes, arising from the use of radioisotopes in medicine and industry are accumulating throughout Australia. The rate of accumulation has not been large and storage of these wastes close to the point of use has proved practicable to date, but consideration must now be given to a central repository or repositories for these low level wastes. This report considers the question of selecting a site suitable for disposal of wastes by shallow ground burial. It attempts to asses the practicability of using factors suggested by the IAEA for the initial phase of site screening. The screening process described has essentially two stages. In the first, New South Wales was divided into broad structural units and these ranked in order of suitability. In the second stage, survey sites in which thick clay beds outcropped were delineated in the five highest ranking structural units. These survey sites were ranked on the basis of various geomorphological properties which largely described the hydrogeology of the site
Devos, Olivier; Downey, Gerard; Duponchel, Ludovic
2014-04-01
Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.
Moran, Yehu; Kahn, Roy; Cohen, Lior; Gur, Maya; Karbat, Izhar; Gordon, Dalia; Gurevitz, Michael
2007-08-15
Av3 is a short peptide toxin from the sea anemone Anemonia viridis shown to be active on crustaceans and inactive on mammals. It inhibits inactivation of Na(v)s (voltage-gated Na+ channels) like the structurally dissimilar scorpion alpha-toxins and type I sea anemone toxins that bind to receptor site-3. To examine the potency and mode of interaction of Av3 with insect Na(v)s, we established a system for its expression, mutagenized it throughout, and analysed it in toxicity, binding and electrophysiological assays. The recombinant Av3 was found to be highly toxic to blowfly larvae (ED50=2.65+/-0.46 pmol/100 mg), to compete well with the site-3 toxin LqhalphaIT (from the scorpion Leiurus quinquestriatus) on binding to cockroach neuronal membranes (K(i)=21.4+/-7.1 nM), and to inhibit the inactivation of Drosophila melanogaster channel, DmNa(v)1, but not that of mammalian Na(v)s expressed in Xenopus oocytes. Moreover, like other site-3 toxins, the activity of Av3 was synergically enhanced by ligands of receptor site-4 (e.g. scorpion beta-toxins). The bioactive surface of Av3 was found to consist mainly of aromatic residues and did not resemble any of the bioactive surfaces of other site-3 toxins. These analyses have portrayed a toxin that might interact with receptor site-3 in a different fashion compared with other ligands of this site. This assumption was corroborated by a D1701R mutation in DmNa(v)1, which has been shown to abolish the activity of all other site-3 ligands, except Av3. All in all, the present study provides further evidence for the heterogeneity of receptor site-3, and raises Av3 as a unique model for design of selective anti-insect compounds.
Zheng, Feifei; Maier, Holger R.; Wu, Wenyan; Dandy, Graeme C.; Gupta, Hoshin V.; Zhang, Tuqiao
2018-02-01
Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data-driven artificial neural network (ANN) models of streamflow. Four well-known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data-driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall-runoff models.
Tel, G.
We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of
Foundations of genetic algorithms 1991
1991-01-01
Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems.This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; condition
Q-learning-based adjustable fixed-phase quantum Grover search algorithm
International Nuclear Information System (INIS)
Guo Ying; Shi Wensha; Wang Yijun; Hu, Jiankun
2017-01-01
We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one. (author)
DEFF Research Database (Denmark)
Ødegård, Jørgen; Meuwissen, Theo HE; Heringstad, Bjørg
2010-01-01
Background In the genetic analysis of binary traits with one observation per animal, animal threshold models frequently give biased heritability estimates. In some cases, this problem can be circumvented by fitting sire- or sire-dam models. However, these models are not appropriate in cases where...... records exist for the parents). Furthermore, the new algorithm showed much faster Markov chain mixing properties for genetic parameters (similar to the sire-dam model). Conclusions The new algorithm to estimate genetic parameters via Gibbs sampling solves the bias problems typically occurring in animal...... individual records exist on parents. Therefore, the aim of our study was to develop a new Gibbs sampling algorithm for a proper estimation of genetic (co)variance components within an animal threshold model framework. Methods In the proposed algorithm, individuals are classified as either "informative...
Python algorithms mastering basic algorithms in the Python language
Hetland, Magnus Lie
2014-01-01
Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data struc
Unsupervised Classification Using Immune Algorithm
Al-Muallim, M. T.; El-Kouatly, R.
2012-01-01
Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed U...
International Nuclear Information System (INIS)
Noga, M.T.
1984-01-01
This thesis addresses a number of important problems that fall within the framework of the new discipline of Computational Geometry. The list of topics covered includes sorting and selection, convex hull algorithms, the L 1 hull, determination of the minimum encasing rectangle of a set of points, the Euclidean and L 1 diameter of a set of points, the metric traveling salesman problem, and finding the superrange of star-shaped and monotype polygons. The main theme of all the work was to develop a set of very fast state-of-the-art algorithms that supersede any rivals in terms of speed and ease of implementation. In some cases existing algorithms were refined; for others new techniques were developed that add to the present database of fast adaptive geometric algorithms. What emerges is a collection of techniques that is successful at merging modern tools developed in analysis of algorithms with those of classical geometry
Where genetic algorithms excel.
Baum, E B; Boneh, D; Garrett, C
2001-01-01
We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve "implicit parallelism" in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GAs can beat competing methods.
Application of genetic algorithms for parameter estimation in liquid chromatography
International Nuclear Information System (INIS)
Hernandez Torres, Reynier; Irizar Mesa, Mirtha; Tavares Camara, Leoncio Diogenes
2012-01-01
In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography
Vector Network Coding Algorithms
Ebrahimi, Javad; Fragouli, Christina
2010-01-01
We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L x L coding matrices that play a similar role as coding c in scalar coding. Our algorithms for scalar network jointly optimize the employed field size while selecting the coding coefficients. Similarly, for vector coding, our algori...
Algorithm 426 : Merge sort algorithm [M1
Bron, C.
1972-01-01
Sorting by means of a two-way merge has a reputation of requiring a clerically complicated and cumbersome program. This ALGOL 60 procedure demonstrates that, using recursion, an elegant and efficient algorithm can be designed, the correctness of which is easily proved [2]. Sorting n objects gives
Üstebay, D.; Castro, R.M.; Rabbat, M.
2009-01-01
Motivated by applications in compression and distributed transform coding, we propose a new gossip algorithm called Selective Gossip to efficiently compute sparse approximations of network data. We consider running parallel gossip algorithms on the elements of a vector of transform coefficients.
Group leaders optimization algorithm
Daskin, Anmer; Kais, Sabre
2011-03-01
We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multi-dimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N 2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for a two-qubit Grover search algorithm which is a quantum algorithm providing quadratic speedup over the classical counterpart.
Genetic algorithms for protein threading.
Yadgari, J; Amir, A; Unger, R
1998-01-01
Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).
The ATLAS Trigger Algorithms for General Purpose Graphics Processor Units
Tavares Delgado, Ademar; The ATLAS collaboration
2016-01-01
The ATLAS Trigger Algorithms for General Purpose Graphics Processor Units Type: Talk Abstract: We present the ATLAS Trigger algorithms developed to exploit General Purpose Graphics Processor Units. ATLAS is a particle physics experiment located on the LHC collider at CERN. The ATLAS Trigger system has two levels, hardware-based Level 1 and the High Level Trigger implemented in software running on a farm of commodity CPU. Performing the trigger event selection within the available farm resources presents a significant challenge that will increase future LHC upgrades. are being evaluated as a potential solution for trigger algorithms acceleration. Key factors determining the potential benefit of this new technology are the relative execution speedup, the number of GPUs required and the relative financial cost of the selected GPU. We have developed a trigger demonstrator which includes algorithms for reconstructing tracks in the Inner Detector and Muon Spectrometer and clusters of energy deposited in the Cal...
Android Malware Classification Using K-Means Clustering Algorithm
Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah
2017-08-01
Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.
Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery
Kim, Daeun; Haldar, Justin P.
2016-01-01
This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure. PMID:26973368
Innovative technology demonstration
International Nuclear Information System (INIS)
Anderson, D.B.; Luttrell, S.P.; Hartley, J.N.; Hinchee, R.
1992-04-01
The Innovative Technology Demonstration (ITD) program at Tinker Air Force Base (TAFB), Oklahoma City, Oklahoma, will demonstrate the overall utility and effectiveness of innovative technologies for site characterization, monitoring, and remediation of selected contaminated test sites. The current demonstration test sites include a CERCLA site on the NPL list, located under a building (Building 3001) that houses a large active industrial complex used for rebuilding military aircraft, and a site beneath and surrounding an abandoned underground tank vault used for storage of jet fuels and solvents. The site under Building 3001 (the NW Test Site) is contaminated with TCE and Cr +6 ; the site with the fuel storage vault (the SW Tanks Site) is contaminated with fuels, BTEX and TCE. These sites and others have been identified for cleanup under the Air Force's Installation Restoration Program (IRP). This document describes the demonstrations that have been conducted or are planned for the TAFB
An Approximate Approach to Automatic Kernel Selection.
Ding, Lizhong; Liao, Shizhong
2016-02-02
Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.
Innovative technology demonstrations
International Nuclear Information System (INIS)
Anderson, D.B.; Luttrell, S.P.; Hartley, J.N.
1992-08-01
Environmental Management Operations (EMO) is conducting an Innovative Technology Demonstration Program for Tinker Air Force Base (TAFB). Several innovative technologies are being demonstrated to address specific problems associated with remediating two contaminated test sites at the base. Cone penetrometer testing (CPT) is a form of testing that can rapidly characterize a site. This technology was selected to evaluate its applicability in the tight clay soils and consolidated sandstone sediments found at TAFB. Directionally drilled horizontal wells was selected as a method that may be effective in accessing contamination beneath Building 3001 without disrupting the mission of the building, and in enhancing the extraction of contamination both in ground water and in soil. A soil gas extraction (SGE) demonstration, also known as soil vapor extraction, will evaluate the effectiveness of SGE in remediating fuels and TCE contamination contained in the tight clay soil formations surrounding the abandoned underground fuel storage vault located at the SW Tanks Site. In situ sensors have recently received much acclaim as a technology that can be effective in remediating hazardous waste sites. Sensors can be useful for determining real-time, in situ contaminant concentrations during the remediation process for performance monitoring and in providing feedback for controlling the remediation process. Following the SGE demonstration, the SGE system and SW Tanks test site will be modified to demonstrate bioremediation as an effective means of degrading the remaining contaminants in situ. The bioremediation demonstration will evaluate a bioventing process in which the naturally occurring consortium of soil bacteria will be stimulated to aerobically degrade soil contaminants, including fuel and TCE, in situ
Innovative technology demonstrations
International Nuclear Information System (INIS)
Anderson, D.B.; Hartley, J.N.; Luttrell, S.P.
1992-04-01
Currently, several innovative technologies are being demonstrated at Tinker Air Force Base (TAFB) to address specific problems associated with remediating two contaminated test sites at the base. Cone penetrometer testing (CPT) is a form of testing that can rapidly characterize a site. This technology was selected to evaluate its applicability in the tight clay soils and consolidated sandstone sediments found at TAFB. Directionally drilled horizontal wells have been successfully installed at the US Department of Energy's (DOE) Savannah River Site to test new methods of in situ remediation of soils and ground water. This emerging technology was selected as a method that may be effective in accessing contamination beneath Building 3001 without disrupting the mission of the building, and in enhancing the extraction of contamination both in ground water and in soil. A soil gas extraction (SGE) demonstration, also known as soil vapor extraction, will evaluate the effectiveness of SGE in remediating fuels and TCE contamination contained in the tight clay soil formations surrounding the abandoned underground fuel storage vault located at the SW Tanks Site. In situ sensors have recently received much acclaim as a technology that can be effective in remediating hazardous waste sites. Sensors can be useful for determining real-time, in situ contaminant concentrations during the remediation process for performance monitoring and in providing feedback for controlling the remediation process. A demonstration of two in situ sensor systems capable of providing real-time data on contamination levels will be conducted and evaluated concurrently with the SGE demonstration activities. Following the SGE demonstration, the SGE system and SW Tanks test site will be modified to demonstrate bioremediation as an effective means of degrading the remaining contaminants in situ
Directory of Open Access Journals (Sweden)
Canyou Zhang
Full Text Available To calculate the yield and cost per diagnosed tuberculosis (TB case for three World Health Organization screening algorithms and one using the Chinese National TB program (NTP TB suspect definitions, using data from a TB prevalence survey of people aged 65 years and over in China, 2013.This was an analytic study using data from the above survey. Risk groups were defined and the prevalence of new TB cases in each group calculated. Costs of each screening component were used to give indicative costs per case detected. Yield, number needed to screen (NNS and cost per case were used to assess the algorithms.The prevalence survey identified 172 new TB cases in 34,250 participants. Prevalence varied greatly in different groups, from 131/100,000 to 4651/ 100,000. Two groups were chosen to compare the algorithms. The medium-risk group (living in a rural area: men, or previous TB case, or close contact or a BMI <18.5, or tobacco user had appreciably higher cost per case (USD 221, 298 and 963 in the three algorithms than the high-risk group (all previous TB cases, all close contacts. (USD 72, 108 and 309 but detected two to four times more TB cases in the population. Using a Chest x-ray as the initial screening tool in the medium risk group cost the most (USD 963, and detected 67% of all the new cases. Using the NTP definition of TB suspects made little difference.To "End TB", many more TB cases have to be identified. Screening only the highest risk groups identified under 14% of the undetected cases,. To "End TB", medium risk groups will need to be screened. Using a CXR for initial screening results in a much higher yield, at what should be an acceptable cost.
DEFF Research Database (Denmark)
Wessel, I; Jensen, L H; Jensen, P B
1999-01-01
in expression of the beta isoform. Sequencing of the entire topoisomerase IIalpha cDNA from NYH/187 cells demonstrated a homozygous G-->A point mutation at nucleotide 485, leading to a R162Q conversion in the Walker A consensus ATP binding site (residues 161-165 in the alpha isoform), this being the first drug......-selected mutation described at this site. Western blotting after incubation with ICRF-187 showed no depletion of the alpha isoform in NYH/187 cells in contrast to wild-type (wt) cells, whereas equal depletion of the beta isoform was observed in the two sublines. Alkaline elution assay demonstrated a lack...... of inhibition of etoposide-induced DNA single-stranded breaks in NYH/187 cells, whereas this inhibition was readily apparent in NYH cells. Site-directed mutagenesis in human topoisomerase IIalpha introduced into a yeast Saccharomyces cerevisiae strain with a temperature-conditional yeast TOP2 mutant...
DEFF Research Database (Denmark)
This book constitutes the refereed proceedings of the 10th Scandinavian Workshop on Algorithm Theory, SWAT 2006, held in Riga, Latvia, in July 2006. The 36 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 154 submissions. The papers address all...
One improved LSB steganography algorithm
Song, Bing; Zhang, Zhi-hong
2013-03-01
It is easy to be detected by X2 and RS steganalysis with high accuracy that using LSB algorithm to hide information in digital image. We started by selecting information embedded location and modifying the information embedded method, combined with sub-affine transformation and matrix coding method, improved the LSB algorithm and a new LSB algorithm was proposed. Experimental results show that the improved one can resist the X2 and RS steganalysis effectively.
Zhang, Lei; Yang, Fengbao; Ji, Linna; Lv, Sheng
2018-01-01
Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.
International Nuclear Information System (INIS)
Healy, E.C.; Maxwell, J.D.; Hinton, W.S.
1996-09-01
This report presents the results of an economic evaluation produced as part of the Innovative Clean Coal Technology project, which demonstrated selective catalytic reduction (SCR) technology for reduction of NO x emissions from utility boilers burning U.S. high-sulfur coal. The document includes a commercial-scale capital and O ampersand M cost evaluation of SCR technology applied to a new facility, coal-fired boiler utilizing high-sulfur U.S. coal. The base case presented herein determines the total capital requirement, fixed and variable operating costs, and levelized costs for a new 250-MW pulverized coal utility boiler operating with a 60-percent NO x removal. Sensitivity evaluations are included to demonstrate the variation in cost due to changes in process variables and assumptions. This report also presents the results of a study completed by SCS to determine the cost and technical feasibility of retrofitting SCR technology to selected coal-fired generating units within the Southern electric system
Ivachtchenko, Alexandre V; Ivanenkov, Yan A; Veselov, Mark S; Okun, I M
2017-01-01
In recent years, 5-hydroxytryptamine subtype 6 receptor (5-HT6 receptor, 5- HT6R) has emerged as a promising therapeutic target for the treatment of neuropathological disorders, including Alzheimer's disease (AD) and schizophrenia. 5-HT6 receptors were hypothesized to be implicated in the processes of learning, memory, and cognition with 5-HT6R antagonists being effective in animal models of cognition and memory impairment. Several selective 5-HT6R ligands are currently undergoing clinical trials for treatment of AD. We describe results of preclinical development of a novel and highly selective and potent 5- HT6R antagonist, AVN-322, as a clinical candidate for the treatment of AD to improve concurrent debilitation of memory and cognition in the AD patients, and schizophrenia as a substance with antipsychotic effect. In the manuscript, we present its in vitro and vivo efficacy, ADME, pharmacokinetics in animals and in humans, and toxicity. While having high binding affinity in medium picomolar range, the lead compound demonstrates substantially better selectivity index then the reference drug candidates currently being tested in clinical studies. AVN-322 showed high oral bioavailability and favorable blood-brain barrier (BBB) penetration. In vivo testing revealed its clear cognition enhancing effect. AVN-322 significantly restored both scopolamine- and MK-801-induced cognitive dysfunction and demonstrated antipsychotic potential. Taking into account its good safety profile and favorable pharmacokinetics, AVN-322 can be reasonably considered as a novel drug candidate for the treatment of neurological disorders such as AD and/or schizophrenia. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Where are the parallel algorithms?
Voigt, R. G.
1985-01-01
Four paradigms that can be useful in developing parallel algorithms are discussed. These include computational complexity analysis, changing the order of computation, asynchronous computation, and divide and conquer. Each is illustrated with an example from scientific computation, and it is shown that computational complexity must be used with great care or an inefficient algorithm may be selected.
Remote monitoring demonstration
International Nuclear Information System (INIS)
Caskey, Susan; Olsen, John
2006-01-01
The recently upgraded remote monitoring system at the Joyo Experimental Reactor uses a DCM-14 camera module and GEMINI software. The final data is compatible both with the IAEA-approved GARS review software and the ALIS software that was used for this demonstration. Features of the remote monitoring upgrade emphasized compatibility with IAEA practice. This presentation gives particular attention to the selection process for meeting network security considerations at the O'arai site. The Joyo system is different from the NNCA's ACPF system, in that it emphasizes use of IAEA standard camera technology and data acquisition and transmission software. In the demonstration itself, a temporary virtual private network (VPN) between the meeting room and the server at Sandia in Albuquerque allowed attendees to observe data stored from routine transmissions from the Joyo Fresh Fuel Storage to Sandia. Image files from a fuel movement earlier in the month showed Joyo workers and IAEA inspectors carrying out a transfer. (author)
Commercial incineration demonstration
International Nuclear Information System (INIS)
Borduin, L.C.; Neuls, A.S.
1981-01-01
Low-level radioactive wastes (LLW) generated by nuclear utilities presently are shipped to commercial burial grounds for disposal. Substantially increasing shipping and disposal charges have sparked renewed industry interest in incineration and other advanced volume reduction techniques as potential cost-saving measures. Repeated inquiries from industry sources regarding LLW applicability of the Los Alamos controlled-air incineration (CAI) design led DOE to initiate this commercial demonstration program in FY-1980. The selected program approach to achieving CAI demonstration at a utility site is a DOE sponsored joint effort involving Los Alamos, a nuclear utility, and a liaison subcontractor. Required development tasks and responsibilities of the particpants are described. Target date for project completion is the end of FY-1985
Directory of Open Access Journals (Sweden)
Xiang-ming Gao
2017-01-01
Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.
Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo
2017-01-01
Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.
de Casteljau's Algorithm Revisited
DEFF Research Database (Denmark)
Gravesen, Jens
1998-01-01
It is demonstrated how all the basic properties of Bezier curves can be derived swiftly and efficiently without any reference to the Bernstein polynomials and essentially with only geometric arguments. This is achieved by viewing one step in de Casteljau's algorithm as an operator (the de Casteljau...
Fast algorithm for Morphological Filters
International Nuclear Information System (INIS)
Lou Shan; Jiang Xiangqian; Scott, Paul J
2011-01-01
In surface metrology, morphological filters, which evolved from the envelope filtering system (E-system) work well for functional prediction of surface finish in the analysis of surfaces in contact. The naive algorithms are time consuming, especially for areal data, and not generally adopted in real practice. A fast algorithm is proposed based on the alpha shape. The hull obtained by rolling the alpha ball is equivalent to the morphological opening/closing in theory. The algorithm depends on Delaunay triangulation with time complexity O(nlogn). In comparison to the naive algorithms it generates the opening and closing envelope without combining dilation and erosion. Edge distortion is corrected by reflective padding for open profiles/surfaces. Spikes in the sample data are detected and points interpolated to prevent singularities. The proposed algorithm works well both for morphological profile and area filters. Examples are presented to demonstrate the validity and superiority on efficiency of this algorithm over the naive algorithm.
Goodarzi, Mohammad; Saeys, Wouter; Deeb, Omar; Pieters, Sigrid; Vander Heyden, Yvan
2013-12-01
Quantitative structure-activity relationship (QSAR) modeling was performed for imidazo[1,5-a]pyrido[3,2-e]pyrazines, which constitute a class of phosphodiesterase 10A inhibitors. Particle swarm optimization (PSO) and genetic algorithm (GA) were used as feature selection techniques to find the most reliable molecular descriptors from a large pool. Modeling of the relationship between the selected descriptors and the pIC50 activity data was achieved by linear [multiple linear regression (MLR)] and non-linear [locally weighted regression (LWR) based on both Euclidean (E) and Mahalanobis (M) distances] methods. In addition, a stepwise MLR model was built using only a limited number of quantum chemical descriptors, selected because of their correlation with the pIC50 . The model was not found interesting. It was concluded that the LWR model, based on the Euclidean distance, applied on the descriptors selected by PSO has the best prediction ability. However, some other models behaved similarly. The root-mean-squared errors of prediction (RMSEP) for the test sets obtained by PSO/MLR, GA/MLR, PSO/LWRE, PSO/LWRM, GA/LWRE, and GA/LWRM models were 0.333, 0.394, 0.313, 0.333, 0.421, and 0.424, respectively. The PSO-selected descriptors resulted in the best prediction models, both linear and non-linear. © 2013 John Wiley & Sons A/S.
Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms
Directory of Open Access Journals (Sweden)
Cheng-Yuan Shih
2010-01-01
Full Text Available This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA and quadratic discriminant analysis (QDA. It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.
Improved dynamic-programming-based algorithms for segmentation of masses in mammograms
International Nuclear Information System (INIS)
Dominguez, Alfonso Rojas; Nandi, Asoke K.
2007-01-01
In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID 2 PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID 2 PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions
International Nuclear Information System (INIS)
Creutz, M.
1987-11-01
A large variety of Monte Carlo algorithms are being used for lattice gauge simulations. For purely bosonic theories, present approaches are generally adequate; nevertheless, overrelaxation techniques promise savings by a factor of about three in computer time. For fermionic fields the situation is more difficult and less clear. Algorithms which involve an extrapolation to a vanishing step size are all quite closely related. Methods which do not require such an approximation tend to require computer time which grows as the square of the volume of the system. Recent developments combining global accept/reject stages with Langevin or microcanonical updatings promise to reduce this growth to V/sup 4/3/
Hu, T C
2002-01-01
Newly enlarged, updated second edition of a valuable text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. 153 black-and-white illus. 23 tables.Newly enlarged, updated second edition of a valuable, widely used text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discussed are binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. New to this edition: Chapter 9
US GCFR demonstration plant design
International Nuclear Information System (INIS)
Hunt, P.S.; Snyder, H.J.
1980-05-01
A general description of the US GCFR demonstration plant conceptual design is given to provide a context for more detailed papers to follow. The parameters selected for use in the design are presented and the basis for parameter selection is discussed. Nuclear steam supply system (NSSS) and balance of plant (BOP) component arrangements and systems are briefly discussed
Diversity-Guided Evolutionary Algorithms
DEFF Research Database (Denmark)
Ursem, Rasmus Kjær
2002-01-01
Population diversity is undoubtably a key issue in the performance of evolutionary algorithms. A common hypothesis is that high diversity is important to avoid premature convergence and to escape local optima. Various diversity measures have been used to analyze algorithms, but so far few...... algorithms have used a measure to guide the search. The diversity-guided evolutionary algorithm (DGEA) uses the wellknown distance-to-average-point measure to alternate between phases of exploration (mutation) and phases of exploitation (recombination and selection). The DGEA showed remarkable results...
An Improved Phase Gradient Autofocus Algorithm Used in Real-time Processing
Directory of Open Access Journals (Sweden)
Qing Ji-ming
2015-10-01
Full Text Available The Phase Gradient Autofocus (PGA algorithm can remove the high order phase error effectively, which is of great significance to get high resolution images in real-time processing. While PGA usually needs iteration, which necessitates long working hours. In addition, the performances of the algorithm are not stable in different scene applications. This severely constrains the application of PGA in real-time processing. Isolated scatter selection and windowing are two important algorithmic steps of Phase Gradient Autofocus Algorithm. Therefore, this paper presents an isolated scatter selection method based on sample mean and a windowing method based on pulse envelope. These two methods are highly adaptable to data, which would make the algorithm obtain better stability and need less iteration. The adaptability of the improved PGA is demonstrated with the experimental results of real radar data.
Online co-regularized algorithms
Ruijter, T. de; Tsivtsivadze, E.; Heskes, T.
2012-01-01
We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks
Instance-specific algorithm configuration
Malitsky, Yuri
2014-01-01
This book presents a modular and expandable technique in the rapidly emerging research area of automatic configuration and selection of the best algorithm for the instance at hand. The author presents the basic model behind ISAC and then details a number of modifications and practical applications. In particular, he addresses automated feature generation, offline algorithm configuration for portfolio generation, algorithm selection, adaptive solvers, online tuning, and parallelization. The author's related thesis was honorably mentioned (runner-up) for the ACP Dissertation Award in 2014,
Kuo, Chung-Feng Jeffrey; Chu, Yueng-Hsiang; Wang, Po-Chun; Lai, Chun-Yu; Chu, Wen-Lin; Leu, Yi-Shing; Wang, Hsing-Won
2013-12-01
The human larynx is an important organ for voice production and respiratory mechanisms. The vocal cord is approximated for voice production and open for breathing. The videolaryngoscope is widely used for vocal cord examination. At present, physicians usually diagnose vocal cord diseases by manually selecting the image of the vocal cord opening to the largest extent (abduction), thus maximally exposing the vocal cord lesion. On the other hand, the severity of diseases such as vocal palsy, atrophic vocal cord is largely dependent on the vocal cord closing to the smallest extent (adduction). Therefore, diseases can be assessed by the image of the vocal cord opening to the largest extent, and the seriousness of breathy voice is closely correlated to the gap between vocal cords when closing to the smallest extent. The aim of the study was to design an automatic vocal cord image selection system to improve the conventional selection process by physicians and enhance diagnosis efficiency. Also, due to the unwanted fuzzy images resulting from examination process caused by human factors as well as the non-vocal cord images, texture analysis is added in this study to measure image entropy to establish a screening and elimination system to effectively enhance the accuracy of selecting the image of the vocal cord closing to the smallest extent. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Vekony, M A; Holder, J E; Lee, A J; Horrocks, C; Eperon, I C; Camp, R D
1997-07-01
Several groups have investigated the role of T cells in the pathogenesis of psoriasis by determination of T-cell receptor (TCR) B-chain variable (V) region usage, both in chronic plaque (psoriasis vulgaris) and guttate forms, with various results. Because there are no data on TCR expression in early psoriasis vulgaris, when specific cellular immune events may be expected to be most pronounced, we have analyzed early lesions (less than 3 wk old) of ten patients, with highly reproducible results. We have developed a highly controlled anchored polymerase chain reaction (PCR) method in which TCR beta chain species are all amplified with the same primer pair and products are quantified by dot blot hybridization with BV family-specific oligonucleotide probes. Overexpression of certain TCR BV genes was observed in the majority of lesional biopsies, but in samples in which the expanded BV family formed more than 10% of total lesional BV (half of the samples analyzed), BV2 and BV6 predominated. The consistency of overexpression of these BV species between patients was much less than in previous studies of TCRBV usage in established chronic plaque psoriasis lesions. Complementarity-determining region 3 (CDR3) size spectratyping demonstrated evidence for selective clonal T cell accumulation in less than half of the lesional samples showing BV expansion. These results indicate that selective amplification of TCRBV species occurs in early psoriasis vulgaris but is not essential to the pathogenic process and may be more important in the maintenance or expansion of chronic lesions.
Maximum likelihood positioning algorithm for high-resolution PET scanners
International Nuclear Information System (INIS)
Gross-Weege, Nicolas; Schug, David; Hallen, Patrick; Schulz, Volkmar
2016-01-01
Purpose: In high-resolution positron emission tomography (PET), lightsharing elements are incorporated into typical detector stacks to read out scintillator arrays in which one scintillator element (crystal) is smaller than the size of the readout channel. In order to identify the hit crystal by means of the measured light distribution, a positioning algorithm is required. One commonly applied positioning algorithm uses the center of gravity (COG) of the measured light distribution. The COG algorithm is limited in spatial resolution by noise and intercrystal Compton scatter. The purpose of this work is to develop a positioning algorithm which overcomes this limitation. Methods: The authors present a maximum likelihood (ML) algorithm which compares a set of expected light distributions given by probability density functions (PDFs) with the measured light distribution. Instead of modeling the PDFs by using an analytical model, the PDFs of the proposed ML algorithm are generated assuming a single-gamma-interaction model from measured data. The algorithm was evaluated with a hot-rod phantom measurement acquired with the preclinical HYPERION II D PET scanner. In order to assess the performance with respect to sensitivity, energy resolution, and image quality, the ML algorithm was compared to a COG algorithm which calculates the COG from a restricted set of channels. The authors studied the energy resolution of the ML and the COG algorithm regarding incomplete light distributions (missing channel information caused by detector dead time). Furthermore, the authors investigated the effects of using a filter based on the likelihood values on sensitivity, energy resolution, and image quality. Results: A sensitivity gain of up to 19% was demonstrated in comparison to the COG algorithm for the selected operation parameters. Energy resolution and image quality were on a similar level for both algorithms. Additionally, the authors demonstrated that the performance of the ML
Single Directional SMO Algorithm for Least Squares Support Vector Machines
Directory of Open Access Journals (Sweden)
Xigao Shao
2013-01-01
Full Text Available Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs. In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO- type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.
Machine Learning an algorithmic perspective
Marsland, Stephen
2009-01-01
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le
Directory of Open Access Journals (Sweden)
Liliana Alvarez
2004-01-01
Full Text Available Theoretical studies suggest that mating and pair formation is not likely to be random. Computer simulations suggested that sex among genetically complex organisms requires mate choice strategies for its evolutionary maintenance, to reduce excessive genetic variance produced by out-crossing. One strategy achieving this aim efficiently in computer simulations is assortative mating modeled as “self seeking like”. Another one is selection of “good genes”. Assortative mating increases the probability of finding a genetically similar mate, without fomenting inbreeding, achieving assortative mating without hindering the working of other mate selection strategies which aim to maximize the search for “good genes”, optimizing the working of sex in evolutionary terms. Here we present indirect evidence that in a significant proportion of human reproductive couples, the partners show much higher facial resemblances than can be expected by random pair formation, or as the outcome of “matching for attractiveness” or the outcome of competition for the most attractive partner accessible, as had been previously assumed. The data presented is compatible with the hypothesis derived from computer simulations, that human mate selection strategies achieve various aims: “self seeking like” (including matching for attractiveness and mating with the best available genes.
Supplier selection based on improved MOGA and its application in nuclear power equipment procurement
International Nuclear Information System (INIS)
Yan Zhaojun; Wang Dezhong; Zhou Lei
2007-01-01
Considering the fact that there are few objective and available methods supporting the supplier selection in nuclear power equipment purchasing process, a supplier selection method based on improved multi-objective genetic algorithm (MOGA) is proposed. The simulation results demonstrate the effectiveness and efficiency of this method for the supplier selection in nuclear power equipment procurement process. (authors)
Directory of Open Access Journals (Sweden)
A. E. Karateev
2017-01-01
Full Text Available To enhance the efficacy and safety of nonsteroidal anti-inflammatory drugs (NSAIDs, a class of essential medications used to treat acute and chronic pain, is an important and urgent task. For its solution, in 2015 Russian experts provided an NSAID selection algorithm based on the assessment of risk factors (RFs for drug-induced complications and on the prescription of drugs with the least negative effect on the gastrointestinal tract and cardiovascular system. The PRINCIPLE project was implemented to test the effectiveness of this algorithm.Subjects and methods. A study group consisted of 439 patients (65% were women and 35% – men; their mean age was 51.3±14.4 years with severe musculoskeletal pain, who were prescribed NSAIDs by using the above algorithm. The majority of patients were noted to have RFs: gastrointestinal and cardiovascular ones in 62 and 88% of the patients, respectively. Given the RF, eight NSAIDs were used; these were aceclofenac, diclofenac, ibuprofen, ketoprofen, meloxicam, naproxen, nimesulide, and celecoxib, the latter being prescribed most commonly (in 57.4% of cases. NSAID was used in combination with proton pump inhibitors in 30.2% of the patients. The follow-up period was 28 days. The investigators evaluated the efficacy of therapy (pain changes on a 10-point numeric rating scale (NRS and the development of adverse events (AE. Results and discussion. Pain was completely relieved in the overwhelming majority (94.9% of patients. There were no significant differences in the efficacy of different NSAIDs according to NRS scores. The number of AE was minimal and did not differ between different NSAIDs, with the exception of a higher frequency of dyspepsia caused by diclofenac (15.7%. There were no serious complications or therapy discontinuation because of AE.Conclusion. The use of the NSAID selection algorithm allows for effective and relatively safe therapy with these drugs in real clinical practice.
DEFF Research Database (Denmark)
Markham, Annette
This paper takes an actor network theory approach to explore some of the ways that algorithms co-construct identity and relational meaning in contemporary use of social media. Based on intensive interviews with participants as well as activity logging and data tracking, the author presents a richly...... layered set of accounts to help build our understanding of how individuals relate to their devices, search systems, and social network sites. This work extends critical analyses of the power of algorithms in implicating the social self by offering narrative accounts from multiple perspectives. It also...... contributes an innovative method for blending actor network theory with symbolic interaction to grapple with the complexity of everyday sensemaking practices within networked global information flows....
Chan, Cheng Leng; Ang, Pei San; Li, Shu Chuen
2017-06-01
Most Countries have pharmacovigilance (PV) systems in place to monitor the safe use of health products. The process involves the detection and assessment of safety issues from various sources of information, communicating the risk to stakeholders and taking other relevant risk minimization measures. This study aimed to assess the PV status in Association of Southeast Asian Nation (ASEAN) countries, sources for postmarket safety monitoring, methods used for signal detection and the need for a quantitative signal detection algorithm (QSDA). Comparisons were conducted with centres outside ASEAN. A questionnaire was sent to all PV centres in ASEAN countries, as well as seven other countries, from November 2015 to June 2016. The questionnaire was designed to collect information on the status of PV, with a focus on the use of a QSDA. Data were collected from nine ASEAN countries and seven other countries. PV activities were conducted in all these countries, which were at different stages of development. In terms of adverse drug reaction (ADR) reports, the average number received per year ranged from 3 to 50,000 reports for ASEAN countries and from 7000 to 1,103,200 for non-ASEAN countries. Thirty-three percent of ASEAN countries utilized statistical methods to help detect signals from ADR reports compared with 100% in the other non-ASEAN countries. Eighty percent agreed that the development of a QSDA would help in drug signal detection. The main limitation identified was the lack of knowledge and/or lack of resources. Spontaneous ADR reports from healthcare professionals remains the most frequently used source for safety monitoring. The traditional method of case-by-case review of ADR reports prevailed for signal detection in ASEAN countries. As the reports continue to grow, the development of a QSDA would be useful in helping detect safety signals.
Wulz, Claudia
2017-01-01
With ever increasing luminosity at the LHC, optimum online data selection is becoming more and more important. While in the case of some experiments (LHCb and ALICE) this task is being completely transferred to computer farms, the others -- ATLAS and CMS -- will not be able to do this in the medium-term future for technological, detector-related reasons. Therefore, these experiments pursue the complementary approach of migrating more and more of the offline and high-level trigger intelligence into the trigger electronics. The presentation illustrates how the level-1 trigger of the CMS experiment and in particular its concluding stage, the so-called ``Global Trigger", take up this challenge.
A Parallel Butterfly Algorithm
Poulson, Jack; Demanet, Laurent; Maxwell, Nicholas; Ying, Lexing
2014-01-01
The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.
A Parallel Butterfly Algorithm
Poulson, Jack
2014-02-04
The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.
Band Subset Selection for Hyperspectral Image Classification
Directory of Open Access Journals (Sweden)
Chunyan Yu
2018-01-01
Full Text Available This paper develops a new approach to band subset selection (BSS for hyperspectral image classification (HSIC which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS, rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS, as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ and successive (SC algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.
Durmus, Tahir; Luhur, Reny; Daqqaq, Tareef; Schwenke, Carsten; Knobloch, Gesine; Huppertz, Alexander; Hamm, Bernd; Lembcke, Alexander
2016-05-01
To evaluate a software tool that claims to maintain a constant contrast-to-noise ratio (CNR) in high-pitch dual-source computed tomography coronary angiography (CTCA) by automatically selecting both X-ray tube voltage and current. A total of 302 patients (171 males; age 61±12years; body weight 82±17kg, body mass index 27.3±4.6kg/cm(2)) underwent CTCA with a topogram-based, automatic selection of both tube voltage and current using dedicated software with quality reference values of 100kV and 250mAs/rotation (i.e., standard values for an average adult weighing 75kg) and an injected iodine load of 222mg/kg. The average radiation dose was estimated to be 1.02±0.64mSv. All data sets had adequate contrast enhancement. Average CNR in the aortic root, left ventricle, and left and right coronary artery was 15.7±4.5, 8.3±2.9, 16.1±4.3 and 15.3±3.9 respectively. Individual CNR values were independent of patients' body size and radiation dose. However, individual CNR values may vary considerably between subjects as reflected by interquartile ranges of 12.6-18.6, 6.2-9.9, 12.8-18.9 and 12.5-17.9 respectively. Moreover, average CNR values were significantly lower in males than females (15.1±4.1 vs. 16.6±11.7 and 7.9±2.7 vs. 8.9±3.0, 15.5±3.9 vs. 16.9±4.6 and 14.7±3.6 vs. 16.0±4.1 respectively). A topogram-based automatic selection of X-ray tube settings in CTCA provides diagnostic image quality independent of patients' body size. Nevertheless, considerable variation of individual CNR values between patients and significant differences of CNR values between males and females occur which questions the reliability of this approach. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
International Nuclear Information System (INIS)
Mahmood, U; Erdi, Y; Wang, W
2014-01-01
Purpose: To assess the impact of General Electrics (GE) automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of an adult anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, Auto mA (180 to 380 mA), noise index (NI) = 14, adaptive iterative statistical reconstruction (ASiR) of 20%, 0.8s rotation time. Image quality was evaluated by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: The CNR for the adult male was found to decrease from CNR = 0.912 ± 0.045 for the baseline protocol without kVa to a CNR = 0.756 ± 0.049 with kVa activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.903 ± 0.023. The difference in the central liver dose with and without kVa was found to be 0.07%. Conclusion: Dose reduction was insignificant in the adult phantom. As determined by NPS analysis, ASiR of 40% produced images with similar noise texture to the baseline protocol. However, the CNR at ASiR of 40% with kVa fails to meet the current ACR CNR passing requirement of 1.0
Energy Technology Data Exchange (ETDEWEB)
Mahmood, U; Erdi, Y; Wang, W [Memorial Sloan Kettering Cancer Center, NY, NY (United States)
2014-06-01
Purpose: To assess the impact of General Electrics (GE) automated tube potential algorithm, kV assist (kVa) on radiation dose and image quality, with an emphasis on optimizing protocols based on noise texture. Methods: Radiation dose was assessed by inserting optically stimulated luminescence dosimeters (OSLs) throughout the body of an adult anthropomorphic phantom (CIRS). The baseline protocol was: 120 kVp, Auto mA (180 to 380 mA), noise index (NI) = 14, adaptive iterative statistical reconstruction (ASiR) of 20%, 0.8s rotation time. Image quality was evaluated by calculating the contrast to noise ratio (CNR) and noise power spectrum (NPS) from the ACR CT accreditation phantom. CNRs were calculated according to the steps described in ACR CT phantom testing document. NPS was determined by taking the 3D FFT of the uniformity section of the ACR phantom. NPS and CNR were evaluated with and without kVa and for all available adaptive iterative statistical reconstruction (ASiR) settings, ranging from 0 to 100%. Each NPS was also evaluated for its peak frequency difference (PFD) with respect to the baseline protocol. Results: The CNR for the adult male was found to decrease from CNR = 0.912 ± 0.045 for the baseline protocol without kVa to a CNR = 0.756 ± 0.049 with kVa activated. When compared against the baseline protocol, the PFD at ASiR of 40% yielded a decrease in noise magnitude as realized by the increase in CNR = 0.903 ± 0.023. The difference in the central liver dose with and without kVa was found to be 0.07%. Conclusion: Dose reduction was insignificant in the adult phantom. As determined by NPS analysis, ASiR of 40% produced images with similar noise texture to the baseline protocol. However, the CNR at ASiR of 40% with kVa fails to meet the current ACR CNR passing requirement of 1.0.
Energy Technology Data Exchange (ETDEWEB)
NONE
1995-11-01
The objective of this project is to demonstrate and evaluate commercially available selective catalytic reduction (SCR) catalysts from U.S., Japanese, and European catalyst suppliers on a high-sulfur U.S. Coal-fired boiler. SCR is a post-combustion nitrogen oxide (NO{sub x}) control technology that involves injecting ammonia into the flue gas generated from coal combustion in an electric utility boiler. The flue gas containing ammonia is then passed through a reactor that contains a specialized catalyst. In the presence of the catalyst, the ammonia reacts with NO{sub x} to form nitrogen and water vapor. Although SCR is widely practiced in Japan and European gas-, oil-, and low-sulfur coal-fired boilers, there are several technical uncertainties associated with applying SCR to U.S. coals. These uncertainties include: (1) potential catalyst deactivation due to poisoning by trace metal species present in U.S. coals that are not present in other fuels; (2) performance of the technology and effects on the balance-of-plant equipment in the presence of high amounts of SO{sub 2} and SO{sub 3}; performance of a wide variety of SCR catalyst compositions, geometries, and methods of manufacture under typical high-sulfur coal-fired utility operating conditions. These uncertainties are being explored by operating a series of small- scale SCR reactors and simultaneously exposing different SCR catalysts to flue gas derived from the combustion of high sulfur U.S. coal. The demonstration is being performed at Gulf Power Company`s Plant Crist Unit No. 5 (75 MW capacity) near Pensacola, Florida. The project is funded by the U.S. Department of Energy (DOE), Southern Company Services, Inc. (SCS on behalf of the entire Southern electric system), the Electric Power Research Institute (EPRI), and Ontario Hydro. SCS is the participant responsible for managing al aspects of this project. 1 ref., 69 figs., 45 tabs.
Demonstration of blind quantum computing.
Barz, Stefanie; Kashefi, Elham; Broadbent, Anne; Fitzsimons, Joseph F; Zeilinger, Anton; Walther, Philip
2012-01-20
Quantum computers, besides offering substantial computational speedups, are also expected to preserve the privacy of a computation. We present an experimental demonstration of blind quantum computing in which the input, computation, and output all remain unknown to the computer. We exploit the conceptual framework of measurement-based quantum computation that enables a client to delegate a computation to a quantum server. Various blind delegated computations, including one- and two-qubit gates and the Deutsch and Grover quantum algorithms, are demonstrated. The client only needs to be able to prepare and transmit individual photonic qubits. Our demonstration is crucial for unconditionally secure quantum cloud computing and might become a key ingredient for real-life applications, especially when considering the challenges of making powerful quantum computers widely available.
Casanova, Henri; Robert, Yves
2008-01-01
""…The authors of the present book, who have extensive credentials in both research and instruction in the area of parallelism, present a sound, principled treatment of parallel algorithms. … This book is very well written and extremely well designed from an instructional point of view. … The authors have created an instructive and fascinating text. The book will serve researchers as well as instructors who need a solid, readable text for a course on parallelism in computing. Indeed, for anyone who wants an understandable text from which to acquire a current, rigorous, and broad vi
DEFF Research Database (Denmark)
Gustavson, Fred G.; Reid, John K.; Wasniewski, Jerzy
2007-01-01
We present subroutines for the Cholesky factorization of a positive-definite symmetric matrix and for solving corresponding sets of linear equations. They exploit cache memory by using the block hybrid format proposed by the authors in a companion article. The matrix is packed into n(n + 1)/2 real...... variables, and the speed is usually better than that of the LAPACK algorithm that uses full storage (n2 variables). Included are subroutines for rearranging a matrix whose upper or lower-triangular part is packed by columns to this format and for the inverse rearrangement. Also included is a kernel...
Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.
Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo
2015-08-01
Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.
Rabideau, Gregg R.; Chien, Steve A.
2010-01-01
AVA v2 software selects goals for execution from a set of goals that oversubscribe shared resources. The term goal refers to a science or engineering request to execute a possibly complex command sequence, such as image targets or ground-station downlinks. Developed as an extension to the Virtual Machine Language (VML) execution system, the software enables onboard and remote goal triggering through the use of an embedded, dynamic goal set that can oversubscribe resources. From the set of conflicting goals, a subset must be chosen that maximizes a given quality metric, which in this case is strict priority selection. A goal can never be pre-empted by a lower priority goal, and high-level goals can be added, removed, or updated at any time, and the "best" goals will be selected for execution. The software addresses the issue of re-planning that must be performed in a short time frame by the embedded system where computational resources are constrained. In particular, the algorithm addresses problems with well-defined goal requests without temporal flexibility that oversubscribes available resources. By using a fast, incremental algorithm, goal selection can be postponed in a "just-in-time" fashion allowing requests to be changed or added at the last minute. Thereby enabling shorter response times and greater autonomy for the system under control.
Online feature selection with streaming features.
Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan
2013-05-01
We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.
An investigation of genetic algorithms
International Nuclear Information System (INIS)
Douglas, S.R.
1995-04-01
Genetic algorithms mimic biological evolution by natural selection in their search for better individuals within a changing population. they can be used as efficient optimizers. This report discusses the developing field of genetic algorithms. It gives a simple example of the search process and introduces the concept of schema. It also discusses modifications to the basic genetic algorithm that result in species and niche formation, in machine learning and artificial evolution of computer programs, and in the streamlining of human-computer interaction. (author). 3 refs., 1 tab., 2 figs
Energy Technology Data Exchange (ETDEWEB)
Gabler, H. [Fraunhofer-Institut fuer Solare Energiesysteme (ISE), Freiburg im Breisgau (Germany); Heinemann, D. [Oldenburg Univ. (Germany). Fachbereich 8 - Physik; Wiemken, E. [Fraunhofer-Institut fuer Solare Energiesysteme (ISE), Freiburg im Breisgau (Germany)
1996-02-01
From several PV-demonstration programmes, real performance data and the range in utilizing the systems are obtained. Nevertheless, for a particular system, no precise assessment and interpretation of the real system behaviour can be given: The quantification and the separation of the avoidable and of the unavoidable energy losses in the system is not to perform from the monitored data alone. Aim of this project was the interpretation of real operation data of PV-systems, the calculation of all steps of energy conversion and hence the determination of the energy losses in particular PV-systems as well as the presentation of a measure for energy production and system performance. This task includes a brief survey of the optimization potential of the energetic performance. The method choosen was an energy flow analysis, carried out on selected demonstration plants of the MuD-programme. For this reason, detailed simulation models were used. The results of the simulation calculations gives the measure to interprete the monitored system performance. In the course of the project, the analytical evaluation has shown a large ability in the assessment and interpretation of real system perforamances. Since the concept of the evaluation is transferable to many PV-systems, it represents a base for further applications in this field and in the field of the online-system control of PV-plants. (orig.) [Deutsch] In verschiedenen Demonstrationsprogrammen werden aus Langzeitmessungen Groessenordnung und Spannbreite der Nutzungsgrade photovoltaischer Anlagen ermittelt. Fuer eine spezifische Anlage ist damit jedoch keine praezise Einschaetzung bzw. Interpretation des Systemverhaltens moeglich, da die Quantifizierung der unvermeidbaren und der vermeidbaren Energieverluste im System nicht aus den Messdaten allein erfolgen kann. Ziel dieses Projektes war die Interpretation des reellen Betriebs von PV-Anlagen, die Berechnung der Zusammensetzung und der Bandbreite der Energieverluste im
Evaluation of Underwater Image Enhancement Algorithms under Different Environmental Conditions
Directory of Open Access Journals (Sweden)
Marino Mangeruga
2018-01-01
Full Text Available Underwater images usually suffer from poor visibility, lack of contrast and colour casting, mainly due to light absorption and scattering. In literature, there are many algorithms aimed to enhance the quality of underwater images through different approaches. Our purpose was to identify an algorithm that performs well in different environmental conditions. We have selected some algorithms from the state of the art and we have employed them to enhance a dataset of images produced in various underwater sites, representing different environmental and illumination conditions. These enhanced images have been evaluated through some quantitative metrics. By analysing the results of these metrics, we tried to understand which of the selected algorithms performed better than the others. Another purpose of our research was to establish if a quantitative metric was enough to judge the behaviour of an underwater image enhancement algorithm. We aim to demonstrate that, even if the metrics can provide an indicative estimation of image quality, they could lead to inconsistent or erroneous evaluations.
Kriging-based algorithm for nuclear reactor neutronic design optimization
International Nuclear Information System (INIS)
Kempf, Stephanie; Forget, Benoit; Hu, Lin-Wen
2012-01-01
Highlights: ► A Kriging-based algorithm was selected to guide research reactor optimization. ► We examined impacts of parameter values upon the algorithm. ► The best parameter values were incorporated into a set of best practices. ► Algorithm with best practices used to optimize thermal flux of concept. ► Final design produces thermal flux 30% higher than other 5 MW reactors. - Abstract: Kriging, a geospatial interpolation technique, has been used in the present work to drive a search-and-optimization algorithm which produces the optimum geometric parameters for a 5 MW research reactor design. The technique has been demonstrated to produce an optimal neutronic solution after a relatively small number of core calculations. It has additionally been successful in producing a design which significantly improves thermal neutron fluxes by 30% over existing reactors of the same power rating. Best practices for use of this algorithm in reactor design were identified and indicated the importance of selecting proper correlation functions.
Energy Technology Data Exchange (ETDEWEB)
NONE
1995-11-01
The objective of this project is to demonstrate and evaluate commercially available Selective Catalytic Reduction (SCR) catalysts from U.S., Japanese and European catalyst suppliers on a high-sulfur U.S. coal-fired boiler. SCR is a post-combustion nitrogen oxide (NO{sub x}) control technology that involve injecting ammonia into the flue gas generated from coal combustion in a boiler. The flue gas containing ammonia is then passed through a reactor that contains a specialized catalyst. In the presence of the catalyst, the ammonia reacts with NO{sub x} to form nitrogen and water vapor. Although SCR is widely practiced in Japan and Europe on gas-, oil-, and low-sulfur coal-fired boilers, there are several technical uncertainties associated with applying SCR to U.S. coals. These uncertainties include: (1) potential catalyst deactivation due to poisoning by trace metal species present in U.S. coals that are not present in other fuels; (2) performance of the technology and effects on the balance-of-plant equipment in the presence of high amounts of SO{sub 2} and SO{sub 3}; and (3) performance of a wide variety of SCR catalyst compositions, geometries and methods of manufacture under typical high-sulfur coal-fired utility operating conditions. These uncertainties are being explored by operating a series of small-scale SCR reactors and simultaneously exposing different SCR catalysts to flue gas derived from the combustion of high sulfur U.S. coal. The project is being conducted in the following three phases: permitting, environmental monitoring plan and preliminary engineering; detailed design engineering and construction; and operation, testing, disposition and final report. The project was in the operation and testing phase during this reporting period. Accomplishments for this period are described.
Kramer, Oliver
2017-01-01
This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
Realization of Deutsch-like algorithm using ensemble computing
International Nuclear Information System (INIS)
Wei Daxiu; Luo Jun; Sun Xianping; Zeng Xizhi
2003-01-01
The Deutsch-like algorithm [Phys. Rev. A. 63 (2001) 034101] distinguishes between even and odd query functions using fewer function calls than its possible classical counterpart in a two-qubit system. But the similar method cannot be applied to a multi-qubit system. We propose a new approach for solving Deutsch-like problem using ensemble computing. The proposed algorithm needs an ancillary qubit and can be easily extended to multi-qubit system with one query. Our ensemble algorithm beginning with a easily-prepared initial state has three main steps. The classifications of the functions can be obtained directly from the spectra of the ancilla qubit. We also demonstrate the new algorithm in a four-qubit molecular system using nuclear magnetic resonance (NMR). One hydrogen and three carbons are selected as the four qubits, and one of carbons is ancilla qubit. We choice two unitary transformations, corresponding to two functions (one odd function and one even function), to validate the ensemble algorithm. The results show that our experiment is successfully and our ensemble algorithm for solving the Deutsch-like problem is virtual
Liu, Xing-Cai; He, Shi-Wei; Song, Rui; Sun, Yang; Li, Hao-Dong
2014-01-01
Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.
Directory of Open Access Journals (Sweden)
Xing-cai Liu
2014-01-01
Full Text Available Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.
Energy Technology Data Exchange (ETDEWEB)
Ngonkham, S. [Khonkaen Univ., Amphur Muang (Thailand). Dept. of Electrical Engineering; Buasri, P. [Khonkaen Univ., Amphur Muang (Thailand). Embed System Research Group
2009-03-11
A harmony search (HS) algorithm was used to optimize economic dispatch (ED) in a wind energy conversion system (WECS) for power system integration. The HS algorithm was based on a stochastic random search method. System costs for the WECS system were estimated in relation to average wind speeds. The HS algorithm was implemented to optimize the ED with a simple programming procedure. The study showed that the initial parameters must be carefully selected to ensure the accuracy of the HS algorithm. The algorithm demonstrated that total costs of the WECS system were higher than costs associated with energy efficiency procedures that reduced the same amount of greenhouse gas (GHG) emissions. 7 refs,. 10 tabs., 16 figs.
Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT
Directory of Open Access Journals (Sweden)
Cunsuo Pang
2016-09-01
Full Text Available This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT’s performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated pulse radar, SAR (Synthetic aperture radar, or ISAR (Inverse synthetic aperture radar, for improving the probability of target recognition.
Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT.
Pang, Cunsuo; Han, Yan; Hou, Huiling; Liu, Shengheng; Zhang, Nan
2016-09-24
This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT) for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT's performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated) pulse radar, SAR (Synthetic aperture radar), or ISAR (Inverse synthetic aperture radar), for improving the probability of target recognition.
Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm
Desmal, Abdulla
2017-04-03
An efficient electromagnetic inversion scheme for imaging sparse 3-D domains is proposed. The scheme achieves its efficiency and accuracy by integrating two concepts. First, the nonlinear optimization problem is constrained using L₀ or L₁-norm of the solution as the penalty term to alleviate the ill-posedness of the inverse problem. The resulting Tikhonov minimization problem is solved using nonlinear Landweber iterations (NLW). Second, the efficiency of the NLW is significantly increased using a steepest descent algorithm. The algorithm uses a projection operator to enforce the sparsity constraint by thresholding the solution at every iteration. Thresholding level and iteration step are selected carefully to increase the efficiency without sacrificing the convergence of the algorithm. Numerical results demonstrate the efficiency and accuracy of the proposed imaging scheme in reconstructing sparse 3-D dielectric profiles.
Genetic algorithms: Theory and applications in the safety domain
International Nuclear Information System (INIS)
Marseguerra, M.; Zio, E.
2001-01-01
This work illustrates the fundamentals underlying optimization by genetic algorithms. All the steps of the procedure are sketched in details for both the traditional breeding algorithm as well as for more sophisticated breeding procedures. The necessity of affine transforming the fitness function, object of the optimization, is discussed in detail, together with the transformation itself. Procedures for the inducement of species and niches are also presented. The theoretical aspects of the work are corroborated by a demonstration of the potential of genetic algorithm optimization procedures on three different case studies. The first case study deals with the design of the pressure stages of a natural gas pipeline system; the second one treats a reliability allocation problem in system configuration design; the last case regards the selection of maintenance and repair strategies for the logistic management of a risky plant. (author)
Adaptive firefly algorithm: parameter analysis and its application.
Directory of Open Access Journals (Sweden)
Ngaam J Cheung
Full Text Available 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.
Demonstration of reliability centered maintenance
International Nuclear Information System (INIS)
Schwan, C.A.; Morgan, T.A.
1991-04-01
Reliability centered maintenance (RCM) is an approach to preventive maintenance planning and evaluation that has been used successfully by other industries, most notably the airlines and military. Now EPRI is demonstrating RCM in the commercial nuclear power industry. Just completed are large-scale, two-year demonstrations at Rochester Gas ampersand Electric (Ginna Nuclear Power Station) and Southern California Edison (San Onofre Nuclear Generating Station). Both demonstrations were begun in the spring of 1988. At each plant, RCM was performed on 12 to 21 major systems. Both demonstrations determined that RCM is an appropriate means to optimize a PM program and improve nuclear plant preventive maintenance on a large scale. Such favorable results had been suggested by three earlier EPRI pilot studies at Florida Power ampersand Light, Duke Power, and Southern California Edison. EPRI selected the Ginna and San Onofre sites because, together, they represent a broad range of utility and plant size, plant organization, plant age, and histories of availability and reliability. Significant steps in each demonstration included: selecting and prioritizing plant systems for RCM evaluation; performing the RCM evaluation steps on selected systems; evaluating the RCM recommendations by a multi-disciplinary task force; implementing the RCM recommendations; establishing a system to track and verify the RCM benefits; and establishing procedures to update the RCM bases and recommendations with time (a living program). 7 refs., 1 tab
A retrodictive stochastic simulation algorithm
International Nuclear Information System (INIS)
Vaughan, T.G.; Drummond, P.D.; Drummond, A.J.
2010-01-01
In this paper we describe a simple method for inferring the initial states of systems evolving stochastically according to master equations, given knowledge of the final states. This is achieved through the use of a retrodictive stochastic simulation algorithm which complements the usual predictive stochastic simulation approach. We demonstrate the utility of this new algorithm by applying it to example problems, including the derivation of likely ancestral states of a gene sequence given a Markovian model of genetic mutation.
Perchlorate Removal, Destruction, and Field Monitoring Demonstration
National Research Council Canada - National Science Library
Coppola, Edward N; Davis, Andrea
2006-01-01
The objectives of this demonstration were to evaluate and demonstrate a complete perchlorate ion exchange process for groundwater that included a unique, regenerable, perchlorate-selective ion exchange resin...
Energy Technology Data Exchange (ETDEWEB)
Fontana, W.
1990-12-13
In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.
Development and Application of a Portable Health Algorithms Test System
Melcher, Kevin J.; Fulton, Christopher E.; Maul, William A.; Sowers, T. Shane
2007-01-01
This paper describes the development and initial demonstration of a Portable Health Algorithms Test (PHALT) System that is being developed by researchers at the NASA Glenn Research Center (GRC). The PHALT System was conceived as a means of evolving the maturity and credibility of algorithms developed to assess the health of aerospace systems. Comprising an integrated hardware-software environment, the PHALT System allows systems health management algorithms to be developed in a graphical programming environment; to be tested and refined using system simulation or test data playback; and finally, to be evaluated in a real-time hardware-in-the-loop mode with a live test article. In this paper, PHALT System development is described through the presentation of a functional architecture, followed by the selection and integration of hardware and software. Also described is an initial real-time hardware-in-the-loop demonstration that used sensor data qualification algorithms to diagnose and isolate simulated sensor failures in a prototype Power Distribution Unit test-bed. Success of the initial demonstration is highlighted by the correct detection of all sensor failures and the absence of any real-time constraint violations.
Rosero-Vlasova, O.; Borini Alves, D.; Vlassova, L.; Perez-Cabello, F.; Montorio Lloveria, R.
2017-10-01
Deforestation in Amazon basin due, among other factors, to frequent wildfires demands continuous post-fire monitoring of soil and vegetation. Thus, the study posed two objectives: (1) evaluate the capacity of Visible - Near InfraRed - ShortWave InfraRed (VIS-NIR-SWIR) spectroscopy to estimate soil organic matter (SOM) in fire-affected soils, and (2) assess the feasibility of SOM mapping from satellite images. For this purpose, 30 soil samples (surface layer) were collected in 2016 in areas of grass and riparian vegetation of Campos Amazonicos National Park, Brazil, repeatedly affected by wildfires. Standard laboratory procedures were applied to determine SOM. Reflectance spectra of soils were obtained in controlled laboratory conditions using Fieldspec4 spectroradiometer (spectral range 350nm- 2500nm). Measured spectra were resampled to simulate reflectances for Landsat-8, Sentinel-2 and EnMap spectral bands, used as predictors in SOM models developed using Partial Least Squares regression and step-down variable selection algorithm (PLSR-SD). The best fit was achieved with models based on reflectances simulated for EnMap bands (R2=0.93; R2cv=0.82 and NMSE=0.07; NMSEcv=0.19). The model uses only 8 out of 244 predictors (bands) chosen by the step-down variable selection algorithm. The least reliable estimates (R2=0.55 and R2cv=0.40 and NMSE=0.43; NMSEcv=0.60) resulted from Landsat model, while Sentinel-2 model showed R2=0.68 and R2cv=0.63; NMSE=0.31 and NMSEcv=0.38. The results confirm high potential of VIS-NIR-SWIR spectroscopy for SOM estimation. Application of step-down produces sparser and better-fit models. Finally, SOM can be estimated with an acceptable accuracy (NMSE 0.35) from EnMap and Sentinel-2 data enabling mapping and analysis of impacts of repeated wildfires on soils in the study area.
Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm
Energy Technology Data Exchange (ETDEWEB)
Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Department of Mathematics, University of Trento, Trento (Italy)
2015-08-07
We address the problem of simulating biochemical reaction networks with time-dependent rates and propose a new algorithm based on our rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)]. The computation for selecting next reaction firings by our time-dependent RSSA (tRSSA) is computationally efficient. Furthermore, the generated trajectory is exact by exploiting the rejection-based mechanism. We benchmark tRSSA on different biological systems with varying forms of reaction rates to demonstrate its applicability and efficiency. We reveal that for nontrivial cases, the selection of reaction firings in existing algorithms introduces approximations because the integration of reaction rates is very computationally demanding and simplifying assumptions are introduced. The selection of the next reaction firing by our approach is easier while preserving the exactness.
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