An adaptive immune optimization algorithm with dynamic lattice searching operation for fast optimization of atomic clusters

International Nuclear Information System (INIS)

Wu, Xia; Wu, Genhua

2014-01-01

Highlights: • A high efficient method for optimization of atomic clusters is developed. • Its performance is studied by optimizing Lennard-Jones clusters and Ag clusters. • The method is proved to be quite efficient. • A new Ag 61 cluster with stacking-fault face-centered cubic motif is found. - Abstract: Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag 61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron

KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance

Directory of Open Access Journals (Sweden)

Zhiwen ZU

2018-04-01

Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.

Ant colony algorithm for clustering in portfolio optimization

Science.gov (United States)

Subekti, R.; Sari, E. R.; Kusumawati, R.

2018-03-01

This research aims to describe portfolio optimization using clustering methods with ant colony approach. Two stock portfolios of LQ45 Indonesia is proposed based on the cluster results obtained from ant colony optimization (ACO). The first portfolio consists of assets with ant colony displacement opportunities beyond the defined probability limits of the researcher, where the weight of each asset is determined by mean-variance method. The second portfolio consists of two assets with the assumption that each asset is a cluster formed from ACO. The first portfolio has a better performance compared to the second portfolio seen from the Sharpe index.

Detection and quantification of solute clusters in a nanostructured ferritic alloy

Energy Technology Data Exchange (ETDEWEB)

Miller, M.K., E-mail: millermk@ornl.gov [Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6139 (United States); Reinhard, D., E-mail: David.Reinhard@ametek.com [CAMECA Instruments, Inc., 5500 Nobel Drive, Madison, WI 53711 (United States); Larson, D.J., E-mail: David.Larson@ametek.com [CAMECA Instruments, Inc., 5500 Nobel Drive, Madison, WI 53711 (United States)

2015-07-15

Highlights: • Simulated APT data indicate that solute clusters can be resolved at 80% detection efficiency. • Solute clusters containing 2–9 atoms were detected in a prototype ∼80% detection efficiency LEAP. • High densities, 1.8 × 10{sup 24} m{sup −3}, of solute clusters were detected in as-milled flakes of 14YWT. • Lower densities, 1.2 × 10{sup 24} m{sup −3}, were detected in the stir zone of a FSW. • Vacancies stabilize the clusters, which retard diffusion and confers excellent stability. - Abstract: A series of simulated atom probe datasets were examined with a friends-of-friends method to establish the detection efficiency required to resolve solute clusters in the ferrite phase of a 14YWT nanostructured ferritic alloy. The size and number densities of solute clusters in the ferrite of the as-milled mechanically-alloyed condition and the stir zone of a friction stir weld were estimated with a prototype high-detection-efficiency (∼80%) local electrode atom probe. High number densities, 1.8 × 10{sup 24} m{sup −3} and 1.2 × 10{sup 24} m{sup −3}, respectively of solute clusters containing between 2 and 9 solute atoms of Ti, Y and O and were detected for these two conditions. These results support first principle calculations that predicted that vacancies stabilize these Ti–Y–O– clusters, which retard diffusion and contribute to the excellent high temperature stability of the microstructure and radiation tolerance of nanostructured ferritic alloys.

Optimal colour quality of LED clusters based on memory colours.

Science.gov (United States)

Smet, Kevin; Ryckaert, Wouter R; Pointer, Michael R; Deconinck, Geert; Hanselaer, Peter

2011-03-28

The spectral power distributions of tri- and tetrachromatic clusters of Light-Emitting-Diodes, composed of simulated and commercially available LEDs, were optimized with a genetic algorithm to maximize the luminous efficacy of radiation and the colour quality as assessed by the memory colour quality metric developed by the authors. The trade-off of the colour quality as assessed by the memory colour metric and the luminous efficacy of radiation was investigated by calculating the Pareto optimal front using the NSGA-II genetic algorithm. Optimal peak wavelengths and spectral widths of the LEDs were derived, and over half of them were found to be close to Thornton's prime colours. The Pareto optimal fronts of real LED clusters were always found to be smaller than those of the simulated clusters. The effect of binning on designing a real LED cluster was investigated and was found to be quite large. Finally, a real LED cluster of commercially available AlGaInP, InGaN and phosphor white LEDs was optimized to obtain a higher score on memory colour quality scale than its corresponding CIE reference illuminant.

A gluon cluster solution of effective Yang-Mills theory

CERN Document Server

Pavlovsky, O V

2001-01-01

A classical solution of the effective Yang-Mills (YM) theory with a finite energy and nonstandard Lagrangian was obtained. Influence of vacuum polarization on gluon cluster formation was discussed. Appearance of cluster solutions in the theory of non-Abelian fields can take place only if the result goes beyond the framework of pure YM theory. It is shown that account of quantum effects of polarized vacuum in the presence of a classical gluon field can also result in formation of the solutions. Solutions with the finite intrinsic energy are provided. Besides, fields of colour groups SU(2) were studied

Optimized data fusion for K-means Laplacian clustering

Science.gov (United States)

Yu, Shi; Liu, Xinhai; Tranchevent, Léon-Charles; Glänzel, Wolfgang; Suykens, Johan A. K.; De Moor, Bart; Moreau, Yves

2011-01-01

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20980271

Optimal Cluster Mill Pass Scheduling With an Accurate and Rapid New Strip Crown Model

International Nuclear Information System (INIS)

Malik, Arif S.; Grandhi, Ramana V.; Zipf, Mark E.

2007-01-01

Besides the requirement to roll coiled sheet at high levels of productivity, the optimal pass scheduling of cluster-type reversing cold mills presents the added challenge of assigning mill parameters that facilitate the best possible strip flatness. The pressures of intense global competition, and the requirements for increasingly thinner, higher quality specialty sheet products that are more difficult to roll, continue to force metal producers to commission innovative flatness-control technologies. This means that during the on-line computerized set-up of rolling mills, the mathematical model should not only determine the minimum total number of passes and maximum rolling speed, it should simultaneously optimize the pass-schedule so that desired flatness is assured, either by manual or automated means. In many cases today, however, on-line prediction of strip crown and corresponding flatness for the complex cluster-type rolling mills is typically addressed either by trial and error, by approximate deflection models for equivalent vertical roll-stacks, or by non-physical pattern recognition style models. The abundance of the aforementioned methods is largely due to the complexity of cluster-type mill configurations and the lack of deflection models with sufficient accuracy and speed for on-line use. Without adequate assignment of the pass-schedule set-up parameters, it may be difficult or impossible to achieve the required strip flatness. In this paper, we demonstrate optimization of cluster mill pass-schedules using a new accurate and rapid strip crown model. This pass-schedule optimization includes computations of the predicted strip thickness profile to validate mathematical constraints. In contrast to many of the existing methods for on-line prediction of strip crown and flatness on cluster mills, the demonstrated method requires minimal prior tuning and no extensive training with collected mill data. To rapidly and accurately solve the multi-contact problem

An optimal design of cluster spacing intervals for staged fracturing in horizontal shale gas wells based on the optimal SRVs

Directory of Open Access Journals (Sweden)

Lan Ren

2017-09-01

Full Text Available When horizontal well staged cluster fracturing is applied in shale gas reservoirs, the cluster spacing is essential to fracturing performance. If the cluster spacing is too small, the stimulated area between major fractures will be overlapped, and the efficiency of fracturing stimulation will be decreased. If the cluster spacing is too large, the area between major fractures cannot be stimulated completely and reservoir recovery extent will be adversely impacted. At present, cluster spacing design is mainly based on the static model with the potential reservoir stimulation area as the target, and there is no cluster spacing design method in accordance with the actual fracturing process and targets dynamic stimulated reservoir volume (SRV. In this paper, a dynamic SRV calculation model for cluster fracture propagation was established by analyzing the coupling mechanisms among fracture propagation, fracturing fluid loss and stress. Then, the cluster spacing was optimized to reach the target of the optimal SRVs. This model was applied for validation on site in the Jiaoshiba shale gasfield in the Fuling area of the Sichuan Basin. The key geological engineering parameters influencing the optimal cluster spacing intervals were analyzed. The reference charts for the optimal cluster spacing design were prepared based on the geological characteristics of south and north blocks in the Jiaoshiba shale gasfield. It is concluded that the cluster spacing optimal design method proposed in this paper is of great significance in overcoming the blindness in current cluster perforation design and guiding the optimal design of volume fracturing in shale gas reservoirs. Keywords: Shale gas, Horizontal well, Staged fracturing, Cluster spacing, Reservoir, Stimulated reservoir volume (SRV, Mathematical model, Optimal method, Sichuan basin, Jiaoshiba shale gasfield

Unit commitment solution using agglomerative and divisive cluster algorithm : an effective new methodology

Energy Technology Data Exchange (ETDEWEB)

Reddy, N.M.; Reddy, K.R. [G. Narayanamma Inst. of Technology and Science, Hyderabad (India). Dept. of Electrical Engineering; Ramana, N.V. [JNTU College of Engineering, Jagityala (India). Dept. of Electrical Engineering

2008-07-01

Thermal power plants consist of several generating units with different generating capacities, fuel cost per MWH generated, minimum up/down times, and start-up or shut-down costs. The Unit Commitment (UC) problem in power systems involves determining the start-up and shut-down schedules of thermal generating units to meet forecasted load over a future short term for a period of one to seven days. This paper presented a new approach for the most complex UC problem using agglomerative and divisive hierarchical clustering. Euclidean costs, which are a measure of differences in fuel cost and start-up costs of any two units, were first calculated. Then, depending on the value of Euclidean costs, similar type of units were placed in a cluster. The proposed methodology has 2 individual algorithms. An agglomerative cluster algorithm is used while the load is increasing, and a divisive cluster algorithm is used when the load is decreasing. A search was conducted for an optimal solution for a minimal number of clusters and cluster data points. A standard ten-unit thermal unit power system was used to test and evaluate the performance of the method for a period of 24 hours. The new approach proved to be quite effective and satisfactory. 15 refs., 9 tabs., 5 figs.

Optimal Sizing for Wind/PV/Battery System Using Fuzzy c-Means Clustering with Self-Adapted Cluster Number

Directory of Open Access Journals (Sweden)

Xin Liu

2017-01-01

Full Text Available Integrating wind generation, photovoltaic power, and battery storage to form hybrid power systems has been recognized to be promising in renewable energy development. However, considering the system complexity and uncertainty of renewable energies, such as wind and solar types, it is difficult to obtain practical solutions for these systems. In this paper, optimal sizing for a wind/PV/battery system is realized by trade-offs between technical and economic factors. Firstly, the fuzzy c-means clustering algorithm was modified with self-adapted parameters to extract useful information from historical data. Furthermore, the Markov model is combined to determine the chronological system states of natural resources and load. Finally, a power balance strategy is introduced to guide the optimization process with the genetic algorithm to establish the optimal configuration with minimized cost while guaranteeing reliability and environmental factors. A case of island hybrid power system is analyzed, and the simulation results are compared with the general FCM method and chronological method to validate the effectiveness of the mentioned method.

Finding Multiple Optimal Solutions to Optimal Load Distribution Problem in Hydropower Plant

Directory of Open Access Journals (Sweden)

Xinhao Jiang

2012-05-01

Full Text Available Optimal load distribution (OLD among generator units of a hydropower plant is a vital task for hydropower generation scheduling and management. Traditional optimization methods for solving this problem focus on finding a single optimal solution. However, many practical constraints on hydropower plant operation are very difficult, if not impossible, to be modeled, and the optimal solution found by those models might be of limited practical uses. This motivates us to find multiple optimal solutions to the OLD problem, which can provide more flexible choices for decision-making. Based on a special dynamic programming model, we use a modified shortest path algorithm to produce multiple solutions to the problem. It is shown that multiple optimal solutions exist for the case study of China’s Geheyan hydropower plant, and they are valuable for assessing the stability of generator units, showing the potential of reducing occurrence times of units across vibration areas.

An optimization of the ALICE XRootD storage cluster at the Tier-2 site in Czech Republic

International Nuclear Information System (INIS)

Adamova, D; Horky, J

2012-01-01

ALICE, as well as the other experiments at the CERN LHC, has been building a distributed data management infrastructure since 2002. Experience gained during years of operations with different types of storage managers deployed over this infrastructure has shown, that the most adequate storage solution for ALICE is the native XRootD manager developed within a CERN - SLAC collaboration. The XRootD storage clusters exhibit higher stability and availability in comparison with other storage solutions and demonstrate a number of other advantages, like support of high speed WAN data access or no need for maintaining complex databases. Two of the operational characteristics of XRootD data servers are a relatively high number of open sockets and a high Unix load. In this article, we would like to describe our experience with the tuning/optimization of machines hosting the XRootD servers, which are part of the ALICE storage cluster at the Tier-2 WLCG site in Prague, Czech Republic. The optimization procedure, in addition to boosting the read/write performance of the servers, also resulted in a reduction of the Unix load.

An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer

Science.gov (United States)

Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein

2018-05-01

Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

Science.gov (United States)

Wang, Zhihao; Yi, Jing

2016-01-01

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

Cluster analysis by optimal decomposition of induced fuzzy sets

Energy Technology Data Exchange (ETDEWEB)

Backer, E

1978-01-01

Nonsupervised pattern recognition is addressed and the concept of fuzzy sets is explored in order to provide the investigator (data analyst) additional information supplied by the pattern class membership values apart from the classical pattern class assignments. The basic ideas behind the pattern recognition problem, the clustering problem, and the concept of fuzzy sets in cluster analysis are discussed, and a brief review of the literature of the fuzzy cluster analysis is given. Some mathematical aspects of fuzzy set theory are briefly discussed; in particular, a measure of fuzziness is suggested. The optimization-clustering problem is characterized. Then the fundamental idea behind affinity decomposition is considered. Next, further analysis takes place with respect to the partitioning-characterization functions. The iterative optimization procedure is then addressed. The reclassification function is investigated and convergence properties are examined. Finally, several experiments in support of the method suggested are described. Four object data sets serve as appropriate test cases. 120 references, 70 figures, 11 tables. (RWR)

Approximative solutions of stochastic optimization problem

Czech Academy of Sciences Publication Activity Database

Lachout, Petr

2010-01-01

Roč. 46, č. 3 (2010), s. 513-523 ISSN 0023-5954 R&D Projects: GA ČR GA201/08/0539 Institutional research plan: CEZ:AV0Z10750506 Keywords : Stochastic optimization problem * sensitivity * approximative solution Subject RIV: BA - General Mathematics Impact factor: 0.461, year: 2010 http://library.utia.cas.cz/separaty/2010/SI/lachout-approximative solutions of stochastic optimization problem.pdf

Fast optimization of binary clusters using a novel dynamic lattice searching method

International Nuclear Information System (INIS)

Wu, Xia; Cheng, Wen

2014-01-01

Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd) 79 clusters with DFT-fit parameters of Gupta potential

Formation mechanism of solute clusters under neutron irradiation in ferritic model alloys and in a reactor pressure vessel steel: clusters of defects

International Nuclear Information System (INIS)

Meslin-Chiffon, E.

2007-11-01

The embrittlement of reactor pressure vessel (RPV) under irradiation is partly due to the formation of point defects (PD) and solute clusters. The aim of this work was to gain more insight into the formation mechanisms of solute clusters in low copper ([Cu] = 0.1 wt%) FeCu and FeCuMnNi model alloys, in a copper free FeMnNi model alloy and in a low copper French RPV steel (16MND5). These materials were neutron-irradiated around 300 C in a test reactor. Solute clusters were characterized by tomographic atom probe whereas PD clusters were simulated with a rate theory numerical code calibrated under cascade damage conditions using transmission electron microscopy analysis. The confrontation between experiments and simulation reveals that a heterogeneous irradiation-induced solute precipitation/segregation probably occurs on PD clusters. (author)

Cluster analysis for portfolio optimization

OpenAIRE

Vincenzo Tola; Fabrizio Lillo; Mauro Gallegati; Rosario N. Mantegna

2005-01-01

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio compositi...

Clustering Algorithm As A Planning Support Tool For Rural Electrification Optimization

Directory of Open Access Journals (Sweden)

Ronaldo Pornillosa Parreno Jr

2015-08-01

Full Text Available Abstract In this study clustering algorithm was developed to optimize electrification plans by screening and grouping potential customers to be supplied with electricity. The algorithm provided adifferent approach in clustering problem which combines conceptual and distance-based clustering algorithmsto analyze potential clusters using spanning tree with the shortest possible edge weight and creating final cluster trees based on the test of inconsistency for the edges. The clustering criteria consists of commonly used distance measure with the addition of household information as basis for the ability to pay ATP value. The combination of these two parameters resulted to a more significant and realistic clusters since distance measure alone could not take the effect of the household characteristics in screening the most sensible groupings of households. In addition the implications of varying geographical features were incorporated in the algorithm by using routing index across the locations of the households. This new approach of connecting the households in an area was applied in an actual case study of one village or barangay that was not yet energized. The results of clustering algorithm generated cluster trees which could becomethetheoretical basis for power utilities to plan the initial network arrangement of electrification. Scenario analysis conducted on the two strategies of clustering the households provideddifferent alternatives for the optimization of the cost of electrification. Futhermorethe benefits associated with the two strategies formulated from the two scenarios was evaluated using benefit cost ratio BC to determine which is more economically advantageous. The results of the study showed that clustering algorithm proved to be effective in solving electrification optimization problem and serves its purpose as a planning support tool which can facilitate electrification in rural areas and achieve cost-effectiveness.

Consensus of satellite cluster flight using an energy-matching optimal control method

Science.gov (United States)

Luo, Jianjun; Zhou, Liang; Zhang, Bo

2017-11-01

This paper presents an optimal control method for consensus of satellite cluster flight under a kind of energy matching condition. Firstly, the relation between energy matching and satellite periodically bounded relative motion is analyzed, and the satellite energy matching principle is applied to configure the initial conditions. Then, period-delayed errors are adopted as state variables to establish the period-delayed errors dynamics models of a single satellite and the cluster. Next a novel satellite cluster feedback control protocol with coupling gain is designed, so that the satellite cluster periodically bounded relative motion consensus problem (period-delayed errors state consensus problem) is transformed to the stability of a set of matrices with the same low dimension. Based on the consensus region theory in the research of multi-agent system consensus issues, the coupling gain can be obtained to satisfy the requirement of consensus region and decouple the satellite cluster information topology and the feedback control gain matrix, which can be determined by Linear quadratic regulator (LQR) optimal method. This method can realize the consensus of satellite cluster period-delayed errors, leading to the consistency of semi-major axes (SMA) and the energy-matching of satellite cluster. Then satellites can emerge the global coordinative cluster behavior. Finally the feasibility and effectiveness of the present energy-matching optimal consensus for satellite cluster flight is verified through numerical simulations.

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.

Science.gov (United States)

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2004-01-01

We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2006-01-01

We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA for Load Profiling Applications

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.

Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

Directory of Open Access Journals (Sweden)

Mi-Yuan Shan

2013-01-01

Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

Science.gov (United States)

Kim, Sehwi; Jung, Inkyung

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

A dynamic lattice searching method with rotation operation for optimization of large clusters

International Nuclear Information System (INIS)

Wu Xia; Cai Wensheng; Shao Xueguang

2009-01-01

Global optimization of large clusters has been a difficult task, though much effort has been paid and many efficient methods have been proposed. During our works, a rotation operation (RO) is designed to realize the structural transformation from decahedra to icosahedra for the optimization of large clusters, by rotating the atoms below the center atom with a definite degree around the fivefold axis. Based on the RO, a development of the previous dynamic lattice searching with constructed core (DLSc), named as DLSc-RO, is presented. With an investigation of the method for the optimization of Lennard-Jones (LJ) clusters, i.e., LJ 500 , LJ 561 , LJ 600 , LJ 665-667 , LJ 670 , LJ 685 , and LJ 923 , Morse clusters, silver clusters by Gupta potential, and aluminum clusters by NP-B potential, it was found that both the global minima with icosahedral and decahedral motifs can be obtained, and the method is proved to be efficient and universal.

Fine interstitial clusters as recombinators in decomposing solid solutions under irradiation

International Nuclear Information System (INIS)

Trushin, Yu.V.

1991-01-01

Behaviour of interstitial clusters and their roll in processes of radiation swelling of metals are described. It is shown that occurrence of coherent advanced precipitations during decomposition of solid solutions under irradiation leads to matrix supersaturation over interstitial atoms. This enhances recombination of unlike defects due to vacancy precipitation on fine interstitial clusters. Evaluation of cluster sizes was conducted

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Directory of Open Access Journals (Sweden)

Simon Fong

2014-01-01

Full Text Available Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Science.gov (United States)

Deb, Suash; Yang, Xin-She

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

Science.gov (United States)

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

Directory of Open Access Journals (Sweden)

Thenmozhi Srinivasan

2015-01-01

Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

On the complexity of determining tolerances for ->e--optimal solutions to min-max combinatorial optimization problems

NARCIS (Netherlands)

Ghosh, D.; Sierksma, G.

2000-01-01

Sensitivity analysis of e-optimal solutions is the problem of calculating the range within which a problem parameter may lie so that the given solution re-mains e-optimal. In this paper we study the sensitivity analysis problem for e-optimal solutions tocombinatorial optimization problems with

Magnetic clusters in ilmenite-hematite solid solutions

DEFF Research Database (Denmark)

Frandsen, Cathrine; Burton, B. P.; Rasmussen, Helge Kildahl

2010-01-01

We report the use of high-field 57Fe Mössbauer spectroscopy to resolve the magnetic ordering of ilmenite-hematite [xFeTiO3−(1−x)Fe2O3] solid solutions with x>0.5. We find that nanometer-sized hematite clusters exist within an ilmenite-like matrix. Although both phases are antiferromagnetically...

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming.

Science.gov (United States)

Wang, Haizhou; Song, Mingzhou

2011-12-01

The heuristic k -means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp . We demonstrate its advantage in optimality and runtime over the standard iterative k -means algorithm.

An Analysis of the Optimal Multiobjective Inventory Clustering Decision with Small Quantity and Great Variety Inventory by Applying a DPSO

Science.gov (United States)

Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions. PMID:25197713

MANOVA, LDA, and FA criteria in clusters parameter estimation

Directory of Open Access Journals (Sweden)

Stan Lipovetsky

2015-12-01

Full Text Available Multivariate analysis of variance (MANOVA and linear discriminant analysis (LDA apply such well-known criteria as the Wilks’ lambda, Lawley–Hotelling trace, and Pillai’s trace test for checking quality of the solutions. The current paper suggests using these criteria for building objectives for finding clusters parameters because optimizing such objectives corresponds to the best distinguishing between the clusters. Relation to Joreskog’s classification for factor analysis (FA techniques is also considered. The problem can be reduced to the multinomial parameterization, and solution can be found in a nonlinear optimization procedure which yields the estimates for the cluster centers and sizes. This approach for clustering works with data compressed into covariance matrix so can be especially useful for big data.

Hierarchical Solution of the Traveling Salesman Problem with Random Dyadic Tilings

Science.gov (United States)

Kalmár-Nagy, Tamás; Bak, Bendegúz Dezső

We propose a hierarchical heuristic approach for solving the Traveling Salesman Problem (TSP) in the unit square. The points are partitioned with a random dyadic tiling and clusters are formed by the points located in the same tile. Each cluster is represented by its geometrical barycenter and a “coarse” TSP solution is calculated for these barycenters. Midpoints are placed at the middle of each edge in the coarse solution. Near-optimal (or optimal) minimum tours are computed for each cluster. The tours are concatenated using the midpoints yielding a solution for the original TSP. The method is tested on random TSPs (independent, identically distributed points in the unit square) up to 10,000 points as well as on a popular benchmark problem (att532 — coordinates of 532 American cities). Our solutions are 8-13% longer than the optimal ones. We also present an optimization algorithm for the partitioning to improve our solutions. This algorithm further reduces the solution errors (by several percent using 1000 iteration steps). The numerical experiments demonstrate the viability of the approach.

IMRT optimization: Variability of solutions and its radiobiological impact

International Nuclear Information System (INIS)

Mattia, Maurizio; Del Giudice, Paolo; Caccia, Barbara

2004-01-01

We aim at (1) defining and measuring a 'complexity' index for the optimization process of an intensity modulated radiation therapy treatment plan (IMRT TP), (2) devising an efficient approximate optimization strategy, and (3) evaluating the impact of the complexity of the optimization process on the radiobiological quality of the treatment. In this work, for a prostate therapy case, the IMRT TP optimization problem has been formulated in terms of dose-volume constraints. The cost function has been minimized in order to achieve the optimal solution, by means of an iterative procedure, which is repeated for many initial modulation profiles, and for each of them the final optimal solution is recorded. To explore the complexity of the space of such solutions we have chosen to minimize the cost function with an algorithm that is unable to avoid local minima. The size of the (sub)optimal solutions distribution is taken as an indicator of the complexity of the optimization problem. The impact of the estimated complexity on the probability of success of the therapy is evaluated using radiobiological indicators (Poissonian TCP model [S. Webb and A. E. Nahum, Phys. Med. Biol. 38(6), 653-666 (1993)] and NTCP relative seriality model [Kallman et al., Int. J. Radiat. Biol. 62(2), 249-262 (1992)]). We find in the examined prostate case a nontrivial distribution of local minima, which has symmetry properties allowing a good estimate of near-optimal solutions with a moderate computational load. We finally demonstrate that reducing the a priori uncertainty in the optimal solution results in a significant improvement of the probability of success of the TP, based on TCP and NTCP estimates

Optimized Clustering Estimators for BAO Measurements Accounting for Significant Redshift Uncertainty

Energy Technology Data Exchange (ETDEWEB)

Ross, Ashley J. [Portsmouth U., ICG; Banik, Nilanjan [Fermilab; Avila, Santiago [Madrid, IFT; Percival, Will J. [Portsmouth U., ICG; Dodelson, Scott [Fermilab; Garcia-Bellido, Juan [Madrid, IFT; Crocce, Martin [ICE, Bellaterra; Elvin-Poole, Jack [Jodrell Bank; Giannantonio, Tommaso [Cambridge U., KICC; Manera, Marc [Cambridge U., DAMTP; Sevilla-Noarbe, Ignacio [Madrid, CIEMAT

2017-05-15

We determine an optimized clustering statistic to be used for galaxy samples with significant redshift uncertainty, such as those that rely on photometric redshifts. To do so, we study the BAO information content as a function of the orientation of galaxy clustering modes with respect to their angle to the line-of-sight (LOS). The clustering along the LOS, as observed in a redshift-space with significant redshift uncertainty, has contributions from clustering modes with a range of orientations with respect to the true LOS. For redshift uncertainty $\\sigma_z \\geq 0.02(1+z)$ we find that while the BAO information is confined to transverse clustering modes in the true space, it is spread nearly evenly in the observed space. Thus, measuring clustering in terms of the projected separation (regardless of the LOS) is an efficient and nearly lossless compression of the signal for $\\sigma_z \\geq 0.02(1+z)$. For reduced redshift uncertainty, a more careful consideration is required. We then use more than 1700 realizations of galaxy simulations mimicking the Dark Energy Survey Year 1 sample to validate our analytic results and optimized analysis procedure. We find that using the correlation function binned in projected separation, we can achieve uncertainties that are within 10 per cent of of those predicted by Fisher matrix forecasts. We predict that DES Y1 should achieve a 5 per cent distance measurement using our optimized methods. We expect the results presented here to be important for any future BAO measurements made using photometric redshift data.

Genetic algorithm optimization of atomic clusters

International Nuclear Information System (INIS)

Morris, J.R.; Deaven, D.M.; Ho, K.M.; Wang, C.Z.; Pan, B.C.; Wacker, J.G.; Turner, D.E.; Iowa State Univ., Ames, IA

1996-01-01

The authors have been using genetic algorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process

Communication: Time-dependent optimized coupled-cluster method for multielectron dynamics

Science.gov (United States)

Sato, Takeshi; Pathak, Himadri; Orimo, Yuki; Ishikawa, Kenichi L.

2018-02-01

Time-dependent coupled-cluster method with time-varying orbital functions, called time-dependent optimized coupled-cluster (TD-OCC) method, is formulated for multielectron dynamics in an intense laser field. We have successfully derived the equations of motion for CC amplitudes and orthonormal orbital functions based on the real action functional, and implemented the method including double excitations (TD-OCCD) and double and triple excitations (TD-OCCDT) within the optimized active orbitals. The present method is size extensive and gauge invariant, a polynomial cost-scaling alternative to the time-dependent multiconfiguration self-consistent-field method. The first application of the TD-OCC method of intense-laser driven correlated electron dynamics in Ar atom is reported.

Reduced-dimension power allocation over clustered channels in cognitive radios system under co-channel interference

KAUST Repository

Ben Ghorbel, Mahdi

2014-05-12

The objective of this paper is to propose a reduceddimension resource allocation scheme in the context of cognitive radio system in presence of co-channel interference between users. We assume a multicarrier transmission for both the primary and secondary systems. Instead of optimizing the powers over all sub-carriers, the sub-carriers are grouped into clusters of sub-carriers, where the power of each sub-carrier is directly related to the power of the correspondent cluster. The power optimization is done only over the set of clusters instead of all sub-carriers which can significantly reduce the complexity of the resource allocation problem. The performance loss of the reduced dimension solution with respect to the optimal solution, where the optimization is carried over all active sub-carriers, allows trading-off complexity versus performance. Numerical evaluation indeed revealed that a limited performance loss occurs by optimizing over a reduced set of clusters instead of the full optimization in the context of cognitive radio systems.

Auto-Clustering using Particle Swarm Optimization and Bacterial Foraging

DEFF Research Database (Denmark)

Rutkowski Olesen, Jakob; Cordero, Jorge; Zeng, Yifeng

2009-01-01

by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid...

Differential Evolution and Particle Swarm Optimization for Partitional Clustering

DEFF Research Database (Denmark)

Krink, Thiemo; Paterlini, Sandra

2006-01-01

for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE......Many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms...

Kernel method for clustering based on optimal target vector

International Nuclear Information System (INIS)

Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

2006-01-01

We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering

Efficient similarity-based data clustering by optimal object to cluster reallocation.

Science.gov (United States)

Rossignol, Mathias; Lagrange, Mathieu; Cont, Arshia

2018-01-01

We present an iterative flat hard clustering algorithm designed to operate on arbitrary similarity matrices, with the only constraint that these matrices be symmetrical. Although functionally very close to kernel k-means, our proposal performs a maximization of average intra-class similarity, instead of a squared distance minimization, in order to remain closer to the semantics of similarities. We show that this approach permits the relaxing of some conditions on usable affinity matrices like semi-positiveness, as well as opening possibilities for computational optimization required for large datasets. Systematic evaluation on a variety of data sets shows that compared with kernel k-means and the spectral clustering methods, the proposed approach gives equivalent or better performance, while running much faster. Most notably, it significantly reduces memory access, which makes it a good choice for large data collections. Material enabling the reproducibility of the results is made available online.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

Science.gov (United States)

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

International Nuclear Information System (INIS)

Zhou, Z; Folkert, M; Wang, J

2016-01-01

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Energy Technology Data Exchange (ETDEWEB)

Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Gulnaz Ahmed

2017-02-01

Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network

Directory of Open Access Journals (Sweden)

C. Vimalarani

2016-01-01

Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement.

Science.gov (United States)

Yuan, Wei; You, Xinge; Xu, Jing; Leung, Henry; Zhang, Tianhang; Chen, Chun Lung Philip

2016-01-01

In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

Differential Spatio-temporal Multiband Satellite Image Clustering using K-means Optimization With Reinforcement Programming

Directory of Open Access Journals (Sweden)

Irene Erlyn Wina Rachmawan

2015-06-01

Full Text Available Deforestration is one of the crucial issues in Indonesia because now Indonesia has world's highest deforestation rate. In other hand, multispectral image delivers a great source of data for studying spatial and temporal changeability of the environmental such as deforestration area. This research present differential image processing methods for detecting nature change of deforestration. Our differential image processing algorithms extract and indicating area automatically. The feature of our proposed idea produce extracted information from multiband satellite image and calculate the area of deforestration by years with calculating data using temporal dataset. Yet, multiband satellite image consists of big data size that were difficult to be handled for segmentation. Commonly, K- Means clustering is considered to be a powerfull clustering algorithm because of its ability to clustering big data. However K-Means has sensitivity of its first generated centroids, which could lead into a bad performance. In this paper we propose a new approach to optimize K-Means clustering using Reinforcement Programming in order to clustering multispectral image. We build a new mechanism for generating initial centroids by implementing exploration and exploitation knowledge from Reinforcement Programming. This optimization will lead a better result for K-means data cluster. We select multispectral image from Landsat 7 in past ten years in Medawai, Borneo, Indonesia, and apply two segmentation areas consist of deforestration land and forest field. We made series of experiments and compared the experimental results of K-means using Reinforcement Programming as optimizing initiate centroid and normal K-means without optimization process. Keywords: Deforestration, Multispectral images, landsat, automatic clustering, K-means.

Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Muhammad Aslam

2017-01-01

Full Text Available Effective utilization of energy resources in Wireless Sensor Networks (WSNs has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC protocol that executes a Hybrid Clustering Algorithm (HCA to perform optimal centralized selection of Cluster-Heads (CHs within radius of centrally located Base Station (BS and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.

Irradiation-Induced Solute Clustering in a Low Nickel FeMnNi Ferritic Alloy

International Nuclear Information System (INIS)

Meslin, E.; Barbu, A.; Radiguet, B.; Pareige, P.; Toffolon, C.

2011-01-01

Understanding the radiation embrittlement of reactor pressure vessel (RPV) steels is required to be able to operate safely a nuclear power plant or to extend its lifetime. The mechanical properties degradation is partly due to the clustering of solute under irradiation. To gain knowledge about the clustering process, a Fe-1.1 Mn-0.7 Ni (at.%) alloy was irradiated in a test reactor at two fluxes of 0.15 and 9 *10 17 n E≥1MeV . m -2 .s -1 and at increasing doses from 0.18 to 1.3 *10 24 n E≥1MeV ) . m -2 at 300 degrees C. Atom probe tomography (APT) experiments revealed that the irradiation promotes the formation in the α iron matrix of Mn/Mn and/or Ni/Ni pair correlations at low dose and Mn-Ni enriched clusters at high dose. These clusters dissolve partially after a thermal treatment at 400 degrees C. Based on a comparison with thermodynamic calculations, we show that the solute clustering under irradiation can just result from an induced mechanism. (authors)

Investigation of CdS clusters in solution for their use as building blocks in hybrid materials

International Nuclear Information System (INIS)

Bendova, M.

2010-01-01

Semiconductor CdS nanoparticles (NPs) represent a system between bulk solids and molecules with novel properties of matter, originating from 'quantum size effects'. Part of the research is aiming to prepare polymers doped with CdS NPs, so called hybrid materials, in order to introduce photoluminescent properties. Clusters are a special case of NPs with defined molecular structure. The aim of this work is related: to prepare hybrid materials with CdS clusters covalently bound in a polymeric matrix. To this end, functional CdS clusters had to be prepared first, with organic groups on the surface capable of polymerization; then they had to be covalently incorporated into a polymeric matrix. During the whole procedure, the molecular structure of the cluster has to be precisely defined. The investigation proceeded differently. In the first part, the molecular structure of the chosen model clusters was investigated in solution at conditions similar to polymerization. It was shown that solvent coordination plays an important role in the maintainance of the cluster structure. When strongly coordinating solvents were used (DMSO, DMF), the cluster (NMe 4 ) 4 [Cd 10 S 4 (SPh) 16 ] rearranged to bigger clusters and NPs, in weakly coordinating solvents (MeCN) the effect was observable only to low degrees at elevated temperatures. Simultaneously, a byproduct was formed which was characterized as [Cd(SPh) x (DMSO) y ] (2-x) (x =ca. 3) in DMSO. The second investigated compound, 'Cd 10 S 4 (SPh) 12 ', revealed the same behavior in coordinating solvents which led also to crystallization of diverse Cd54 clusters. The compound 'Cd 10 S 4 (SPh) 12 ' was investigated also for different reason: theoretically, it should have four free Cd coordination sites capable of functionalization. It turned out, that it does not have the postulated structure Cd 10 S 4 (SPh) 12 , but that it is a mixture of differently sized CdS clusters. Thus, it could not be used for controlled functionalization

Energy quality management for building clusters and districts (BCDs) through multi-objective optimization

International Nuclear Information System (INIS)

Lu, Hai; Alanne, Kari; Martinac, Ivo

2014-01-01

Highlights: • Energy quality management is applied from individual building to district. • A novel time-effective multi-objective design optimization scheme is proposed. • The scheme searches for exergy efficient and environmental solution for districts. • System reliability is considered and addressed in this paper. - Abstract: Renewable energy systems entail a significant potential to meet the energy requirements of building clusters and districts (BCDs) provided that local energy sources are exploited efficiently. Besides improving the energy efficiency by reducing energy consumption and improving the match between energy supply and demand, energy quality issues have become a key topic of interest. Energy quality management is a technique that aims at optimally utilizing the exergy content of various renewable energy sources. In addition to minimizing life-cycle CO 2 emissions related to exergy losses of an energy system, issues such as system reliability should be addressed. The present work contributes to the research by proposing a novel multi-objective design optimization scheme that minimizes the global warming potential during the life-cycle and maximizes the exergy performance, while the maximum allowable level of the loss of power supply probability (LPSP) is predefined by the user as a constraint. The optimization makes use of Genetic Algorithm (GA). Finally, a case study is presented, where the above methodology has been applied to an office BCD located in Norway. The proposed optimization scheme is proven to be efficient in finding the optimal design and can be easily enlarged to encompass more relevant objective functions

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

Science.gov (United States)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

A SURVEY ON OPTIMIZATION APPROACHES TO SEMANTIC SERVICE DISCOVERY TOWARDS AN INTEGRATED SOLUTION

Directory of Open Access Journals (Sweden)

Chellammal Surianarayanan

2012-07-01

Full Text Available The process of semantic service discovery using an ontology reasoner such as Pellet is time consuming. This restricts the usage of web services in real time applications having dynamic composition requirements. As performance of semantic service discovery is crucial in service composition, it should be optimized. Various optimization methods are being proposed to improve the performance of semantic discovery. In this work, we investigate the existing optimization methods and broadly classify optimization mechanisms into two categories, namely optimization by efficient reasoning and optimization by efficient matching. Optimization by efficient matching is further classified into subcategories such as optimization by clustering, optimization by inverted indexing, optimization by caching, optimization by hybrid methods, optimization by efficient data structures and optimization by efficient matching algorithms. With a detailed study of different methods, an integrated optimization infrastructure along with matching method has been proposed to improve the performance of semantic matching component. To achieve better optimization the proposed method integrates the effects of caching, clustering and indexing. Theoretical aspects of performance evaluation of the proposed method are discussed.

An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

Directory of Open Access Journals (Sweden)

M. Anitha

2012-04-01

Full Text Available Settings White Matter lesions (WMLs are small areas of dead cells found in parts of the brain. In general, it is difficult for medical experts to accurately quantify the WMLs due to decreased contrast between White Matter (WM and Grey Matter (GM. The aim of this paper is to
automatically detect the White Matter Lesions which is present in the brains of elderly people. WML detection process includes the following stages: 1. Image preprocessing, 2. Clustering (Fuzzy c-means clustering, Geostatistical Possibilistic clustering and Geostatistical Fuzzy clustering and 3.Optimization using Particle Swarm Optimization (PSO. The proposed system is tested on a database of 208 MRI images. GFCM yields high sensitivity of 89%, specificity of 94% and overall accuracy of 93% over FCM and GPC. The clustered brain images are then subjected to Particle Swarm Optimization (PSO. The optimized result obtained from GFCM-PSO provides sensitivity of 90%, specificity of 94% and accuracy of 95%. The detection results reveals that GFCM and GFCMPSO better localizes the large regions of lesions and gives less false positive rate when compared to GPC and GPC-PSO which captures the largest loads of WMLs only in the upper ventral horns of the brain.

The Role of Semantic Clustering in Optimal Memory Foraging

Science.gov (United States)

Montez, Priscilla; Thompson, Graham; Kello, Christopher T.

2015-01-01

Recent studies of semantic memory have investigated two theories of optimal search adopted from the animal foraging literature: Lévy flights and marginal value theorem. Each theory makes different simplifying assumptions and addresses different findings in search behaviors. In this study, an experiment is conducted to test whether clustering in…

Mobility Aware Energy Efficient Clustering for MANET: A Bio-Inspired Approach with Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Naghma Khatoon

2017-01-01

Full Text Available Mobility awareness and energy efficiency are two indispensable optimization problems in mobile ad hoc networks (MANETs where nodes move unpredictably in any direction with restricted battery life, resulting in frequent change in topology. These constraints are widely studied to increase the lifetime of such networks. This paper focuses on the problems of mobility as well as energy efficiency to develop a clustering algorithm inspired by multiagent stochastic parallel search technique of particle swarm optimization. The election of cluster heads takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster heads for longer duration of time. The cluster formation is presented by taking multiobjective fitness function using particle swarm optimization. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The results show the effectiveness of our proposed algorithm in terms of network lifetime, average number of clusters formed, average number of reclustering required, energy consumption, and packet delivery ratio.

Asymptotic Normality of the Optimal Solution in Multiresponse Surface Mathematical Programming

OpenAIRE

Díaz-García, José A.; Caro-Lopera, Francisco J.

2015-01-01

An explicit form for the perturbation effect on the matrix of regression coeffi- cients on the optimal solution in multiresponse surface methodology is obtained in this paper. Then, the sensitivity analysis of the optimal solution is studied and the critical point characterisation of the convex program, associated with the optimum of a multiresponse surface, is also analysed. Finally, the asymptotic normality of the optimal solution is derived by the standard methods.

Framework for Optimizing Cluster Selection using Geo-assisted Movement Prediction

DEFF Research Database (Denmark)

Kristensen, Thomas Sander; Madsen, Jacob Theilgaard; Pedersen, Michael Sølvkjær

2012-01-01

Due to the availability of satellite- and radio-based location systems in most new devices, it is possible to use geographical location of a node for network management and communication protocol optimization. It is a common belief that usage of location information can bring performance benefits...... movement prediction and inaccurate location estimation on its performance. The proposed algorithm is compared with two reference algorithms: when a considered node associates with either the first discovered cluster or the nearest cluster. Evaluation shows significant performance benefits in terms...

AMICO: optimized detection of galaxy clusters in photometric surveys

Science.gov (United States)

Bellagamba, Fabio; Roncarelli, Mauro; Maturi, Matteo; Moscardini, Lauro

2018-02-01

We present Adaptive Matched Identifier of Clustered Objects (AMICO), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximize the signal-to-noise ratio (S/N) of the clusters. In this work, we focus on the new iterative approach to the extraction of cluster candidates from the map produced by the filter. In particular, we provide a definition of membership probability for the galaxies close to any cluster candidate, which allows us to remove its imprint from the map, allowing the detection of smaller structures. As demonstrated in our tests, this method allows the deblending of close-by and aligned structures in more than 50 per cent of the cases for objects at radial distance equal to 0.5 × R200 or redshift distance equal to 2 × σz, being σz the typical uncertainty of photometric redshifts. Running AMICO on mocks derived from N-body simulations and semi-analytical modelling of the galaxy evolution, we obtain a consistent mass-amplitude relation through the redshift range of 0.3 slope of ∼0.55 and a logarithmic scatter of ∼0.14. The fraction of false detections is steeply decreasing with S/N and negligible at S/N > 5.

Effect of solute atom concentration on vacancy cluster formation in neutron-irradiated Ni alloys

Science.gov (United States)

Sato, Koichi; Itoh, Daiki; Yoshiie, Toshimasa; Xu, Qiu; Taniguchi, Akihiro; Toyama, Takeshi

2011-10-01

The dependence of microstructural evolution on solute atom concentration in Ni alloys was investigated by positron annihilation lifetime measurements. The positron annihilation lifetimes in pure Ni, Ni-0.05 at.%Si, Ni-0.05 at.%Sn, Ni-Cu, and Ni-Ge alloys were about 400 ps even at a low irradiation dose of 3 × 10 -4 dpa, indicating the presence of microvoids in these alloys. The size of vacancy clusters in Ni-Si and Ni-Sn alloys decreased with an increase in the solute atom concentration at irradiation doses less than 0.1 dpa; vacancy clusters started to grow at an irradiation dose of about 0.1 dpa. In Ni-2 at.%Si, irradiation-induced segregation was detected by positron annihilation coincidence Doppler broadening measurements. This segregation suppressed one-dimensional (1-D) motion of the interstitial clusters and promoted mutual annihilation of point defects. The frequency and mean free path of the 1-D motion depended on the solute atom concentration and the amount of segregation.

Particle Swarm Optimization and harmony search based clustering and routing in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Veena Anand

2017-01-01

Full Text Available Wireless Sensor Networks (WSN has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS. Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.

A two-stage approach for multi-objective decision making with applications to system reliability optimization

International Nuclear Information System (INIS)

Li Zhaojun; Liao Haitao; Coit, David W.

2009-01-01

This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.

Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

Science.gov (United States)

Tang, Haijing; Wang, Siye; Zhang, Yanjun

2013-01-01

Clustering has become a common trend in very long instruction words (VLIW) architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC) VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC) VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file. PMID:23970841

Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

Directory of Open Access Journals (Sweden)

Haijing Tang

2013-01-01

Full Text Available Clustering has become a common trend in very long instruction words (VLIW architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file.

How to cluster in parallel with neural networks

Science.gov (United States)

Kamgar-Parsi, Behzad; Gualtieri, J. A.; Devaney, Judy E.; Kamgar-Parsi, Behrooz

1988-01-01

Partitioning a set of N patterns in a d-dimensional metric space into K clusters - in a way that those in a given cluster are more similar to each other than the rest - is a problem of interest in astrophysics, image analysis and other fields. As there are approximately K(N)/K (factorial) possible ways of partitioning the patterns among K clusters, finding the best solution is beyond exhaustive search when N is large. Researchers show that this problem can be formulated as an optimization problem for which very good, but not necessarily optimal solutions can be found by using a neural network. To do this the network must start from many randomly selected initial states. The network is simulated on the MPP (a 128 x 128 SIMD array machine), where researchers use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also by starting the network from many initial states at once, thus obtaining many solutions in one run. Researchers obtain speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of speedups comensurate with human perceptual abilities.

Optimal Water-Power Flow Problem: Formulation and Distributed Optimal Solution

Energy Technology Data Exchange (ETDEWEB)

Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhao, Changhong [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zamzam, Admed S. [University of Minnesota; Sidiropoulos, Nicholas D. [University of Minnesota; Taylor, Josh A. [University of Toronto

2018-01-12

This paper formalizes an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures. Tanks and pumps are optimally managed to satisfy water demand while improving power grid operations; {for the power network, an AC optimal power flow formulation is augmented to accommodate the controllability of water pumps.} Unfortunately, the physics governing the operation of the two infrastructures and coupling constraints lead to a nonconvex (and, in fact, NP-hard) problem; however, after reformulating OWPF as a nonconvex, quadratically-constrained quadratic problem, a feasible point pursuit-successive convex approximation approach is used to identify feasible and optimal solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power operators to pursue individual objectives while respecting the couplings between the two networks. The merits of the proposed approach are demonstrated for the case of a distribution feeder coupled with a municipal water distribution network.

A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

OpenAIRE

Filatovas, Ernestas; Podkopaev, Dmitry; Kurasova, Olga

2015-01-01

Interactive methods of multiobjective optimization repetitively derive Pareto optimal solutions based on decision maker’s preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the Pareto optimal set and learning about the op...

Optimality conditions for the numerical solution of optimization problems with PDE constraints :

Energy Technology Data Exchange (ETDEWEB)

Aguilo Valentin, Miguel Alejandro; Ridzal, Denis

2014-03-01

A theoretical framework for the numerical solution of partial di erential equation (PDE) constrained optimization problems is presented in this report. This theoretical framework embodies the fundamental infrastructure required to e ciently implement and solve this class of problems. Detail derivations of the optimality conditions required to accurately solve several parameter identi cation and optimal control problems are also provided in this report. This will allow the reader to further understand how the theoretical abstraction presented in this report translates to the application.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

Science.gov (United States)

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

Directory of Open Access Journals (Sweden)

Renata De Paris

2015-01-01

Full Text Available Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

From solid solution to cluster formation of Fe and Cr in α-Zr

International Nuclear Information System (INIS)

Burr, P.A.; Wenman, M.R.; Gault, B.; Moody, M.P.; Ivermark, M.; Rushton, M.J.D.; Preuss, M.; Edwards, L.; Grimes, R.W.

2015-01-01

To understand the mechanisms by which the re-solution of Fe and Cr additions increase the corrosion rate of irradiated Zr alloys, the solubility and clustering of Fe and Cr in model binary Zr alloys was investigated using a combination of experimental and modelling techniques — atom probe tomography (APT), x-ray diffraction (XRD), thermoelectric power (TEP) and density functional theory (DFT). Cr occupies both interstitial and substitutional sites in the α-Zr lattice; Fe favours interstitial sites, and a low-symmetry site that was not previously modelled is found to be the most favourable for Fe. Lattice expansion as a function of Fe and Cr content in the α-Zr matrix deviates from Vegard's law and is strongly anisotropic for Fe additions, expanding the c-axis while contracting the a-axis. Matrix content of solutes cannot be reliably estimated from lattice parameter measurements, instead a combination of TEP and APT was employed. Defect clusters form at higher solution concentrations, which induce a smaller lattice strain compared to the dilute defects. In the presence of a Zr vacancy, all two-atom clusters are more soluble than individual point defects and as many as four Fe or three Cr atoms could be accommodated in a single Zr vacancy. The Zr vacancy is critical for the increased apparent solubility of defect clusters; the implications for irradiation induced microstructure changes in Zr alloys are discussed.

From solid solution to cluster formation of Fe and Cr in α-Zr

Energy Technology Data Exchange (ETDEWEB)

Burr, P.A., E-mail: burr.patrick@gmail.com [Centre for Nuclear Engineering and Department of Materials, Imperial College London, London, SW7 2AZ (United Kingdom); Institute of Materials Engineering, Australian Nuclear Science & Technology Organisation, Menai, New South Wales 2234 (Australia); Wenman, M.R. [Centre for Nuclear Engineering and Department of Materials, Imperial College London, London, SW7 2AZ (United Kingdom); Gault, B.; Moody, M.P. [Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH (United Kingdom); Ivermark, M. [High Temperature Materials, Sandvik Materials Technology, 734 27 Hallstahammar (Sweden); University of Manchester, School of Materials, M13 9PL (United Kingdom); Rushton, M.J.D. [Centre for Nuclear Engineering and Department of Materials, Imperial College London, London, SW7 2AZ (United Kingdom); Preuss, M. [University of Manchester, School of Materials, M13 9PL (United Kingdom); Edwards, L. [Institute of Materials Engineering, Australian Nuclear Science & Technology Organisation, Menai, New South Wales 2234 (Australia); Grimes, R.W. [Centre for Nuclear Engineering and Department of Materials, Imperial College London, London, SW7 2AZ (United Kingdom)

2015-12-15

To understand the mechanisms by which the re-solution of Fe and Cr additions increase the corrosion rate of irradiated Zr alloys, the solubility and clustering of Fe and Cr in model binary Zr alloys was investigated using a combination of experimental and modelling techniques — atom probe tomography (APT), x-ray diffraction (XRD), thermoelectric power (TEP) and density functional theory (DFT). Cr occupies both interstitial and substitutional sites in the α-Zr lattice; Fe favours interstitial sites, and a low-symmetry site that was not previously modelled is found to be the most favourable for Fe. Lattice expansion as a function of Fe and Cr content in the α-Zr matrix deviates from Vegard's law and is strongly anisotropic for Fe additions, expanding the c-axis while contracting the a-axis. Matrix content of solutes cannot be reliably estimated from lattice parameter measurements, instead a combination of TEP and APT was employed. Defect clusters form at higher solution concentrations, which induce a smaller lattice strain compared to the dilute defects. In the presence of a Zr vacancy, all two-atom clusters are more soluble than individual point defects and as many as four Fe or three Cr atoms could be accommodated in a single Zr vacancy. The Zr vacancy is critical for the increased apparent solubility of defect clusters; the implications for irradiation induced microstructure changes in Zr alloys are discussed.

Bagged K-means clustering of metabolome data

NARCIS (Netherlands)

Hageman, J. A.; van den Berg, R. A.; Westerhuis, J. A.; Hoefsloot, H. C. J.; Smilde, A. K.

2006-01-01

Clustering of metabolomics data can be hampered by noise originating from biological variation, physical sampling error and analytical error. Using data analysis methods which are not specially suited for dealing with noisy data will yield sub optimal solutions. Bootstrap aggregating (bagging) is a

Solution quality improvement in chiller loading optimization

International Nuclear Information System (INIS)

Geem, Zong Woo

2011-01-01

In order to reduce greenhouse gas emission, we can energy-efficiently operate a multiple chiller system using optimization techniques. So far, various optimization techniques have been proposed to the optimal chiller loading problem. Most of those techniques are meta-heuristic algorithms such as genetic algorithm, simulated annealing, and particle swarm optimization. However, this study applied a gradient-based method, named generalized reduced gradient, and then obtains better results when compared with other approaches. When two additional approaches (hybridization between meta-heuristic algorithm and gradient-based algorithm; and reformulation of optimization structure by adding a binary variable which denotes chiller's operating status) were introduced, generalized reduced gradient found even better solutions. - Highlights: → Chiller loading problem is optimized by generalized reduced gradient (GRG) method. → Results are compared with meta-heuristic algorithms such as genetic algorithm. → Results are even enhanced by hybridizing meta-heuristic and gradient techniques. → Results are even enhanced by modifying the optimization formulation.

Optimizing measurements of cluster velocities and temperatures for CCAT-prime and future surveys

Science.gov (United States)

Mittal, Avirukt; de Bernardis, Francesco; Niemack, Michael D.

2018-02-01

Galaxy cluster velocity correlations and mass distributions are sensitive probes of cosmology and the growth of structure. Upcoming microwave surveys will enable extraction of velocities and temperatures from many individual clusters for the first time. We forecast constraints on peculiar velocities, electron temperatures, and optical depths of galaxy clusters obtainable with upcoming multi-frequency measurements of the kinematic, thermal, and relativistic Sunyaev-Zeldovich effects. The forecasted constraints are compared for different measurement configurations with frequency bands between 90 GHz and 1 THz, and for different survey strategies for the 6-meter CCAT-prime telescope. We study methods for improving cluster constraints by removing emission from dusty star forming galaxies, and by using X-ray temperature priors from eROSITA. Cluster constraints are forecast for several model cluster masses. A sensitivity optimization for seven frequency bands is presented for a CCAT-prime first light instrument and a next generation instrument that takes advantage of the large optical throughput of CCAT-prime. We find that CCAT-prime observations are expected to enable measurement and separation of the SZ effects to characterize the velocity, temperature, and optical depth of individual massive clusters (~1015 Msolar). Submillimeter measurements are shown to play an important role in separating these components from dusty galaxy contamination. Using a modular instrument configuration with similar optical throughput for each detector array, we develop a rule of thumb for the number of detector arrays desired at each frequency to optimize extraction of these signals. Our results are relevant for a future "Stage IV" cosmic microwave background survey, which could enable galaxy cluster measurements over a larger range of masses and redshifts than will be accessible by other experiments.

Families of optimal thermodynamic solutions for combined cycle gas turbine (CCGT) power plants

International Nuclear Information System (INIS)

Godoy, E.; Scenna, N.J.; Benz, S.J.

2010-01-01

Optimal designs of a CCGT power plant characterized by maximum second law efficiency values are determined for a wide range of power demands and different values of the available heat transfer area. These thermodynamic optimal solutions are found within a feasible operation region by means of a non-linear mathematical programming (NLP) model, where decision variables (i.e. transfer areas, power production, mass flow rates, temperatures and pressures) can vary freely. Technical relationships among them are used to systematize optimal values of design and operative variables of a CCGT power plant into optimal solution sets, named here as optimal solution families. From an operative and design point of view, the families of optimal solutions let knowing in advance optimal values of the CCGT variables when facing changes of power demand or adjusting the design to an available heat transfer area.

Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

DEFF Research Database (Denmark)

Pop, Paul; Eles, Petru; Peng, Zebo

2003-01-01

We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

A parallel solution to the cutting stock problem for a cluster of workstations

Energy Technology Data Exchange (ETDEWEB)

Nicklas, L.D.; Atkins, R.W.; Setia, S.V.; Wang, P.Y. [George Mason Univ., Fairfax, VA (United States)

1996-12-31

This paper describes the design and implementation of a solution to the constrained 2-D cutting stock problem on a cluster of workstations. The constrained 2-D cutting stock problem is an irregular problem with a dynamically modified global data set and irregular amounts and patterns of communication. A replicated data structure is used for the parallel solution since the ratio of reads to writes is known to be large. Mutual exclusion and consistency are maintained using a token-based lazy consistency mechanism, and a randomized protocol for dynamically balancing the distributed work queue is employed. Speedups are reported for three benchmark problems executed on a cluster of workstations interconnected by a 10 Mbps Ethernet.

Artificial Bee Colony Algorithm Based on K-Means Clustering for Multiobjective Optimal Power Flow Problem

Directory of Open Access Journals (Sweden)

Liling Sun

2015-01-01

Full Text Available An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II, the multiobjective particle swarm optimizer (MOPSO, and the multiobjective ABC (MOABC. Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

Investigating the synthesis of ligated metal clusters in solution using a flow reactor and electrospray ionization mass spectrometry.

Science.gov (United States)

Olivares, Astrid; Laskin, Julia; Johnson, Grant E

2014-09-18

The scalable synthesis of ligated subnanometer metal clusters containing an exact number of atoms is of interest due to the highly size-dependent catalytic, electronic, and optical properties of these species. While significant research has been conducted on the batch preparation of clusters through reduction synthesis in solution, the processes of metal complex reduction as well as cluster nucleation, growth, and postreduction etching are still not well understood. Herein, we demonstrate a prototype temperature-controlled flow reactor for qualitatively studying cluster formation in solution at steady-state conditions. Employing this technique, methanol solutions of a chloro(triphenylphosphine)gold precursor, 1,4-bis(diphenylphosphino)butane capping ligand, and borane-tert-butylamine reducing agent were combined in a mixing tee and introduced into a heated capillary with a known length. In this manner, the temperature dependence of the relative abundance of different ionic reactants, intermediates, and products synthesized in real time was characterized qualitatively using online mass spectrometry. A wide distribution of doubly and triply charged cationic gold clusters was observed as well as smaller singly charged organometallic complexes. The results demonstrate that temperature plays a crucial role in determining the relative population of cationic gold clusters and, in general, that higher temperature promotes the formation of doubly charged clusters and singly charged organometallic complexes while reducing the abundance of triply charged species. Moreover, the distribution of clusters observed at elevated temperatures is found to be consistent with that obtained at longer reaction times at room temperature, thereby demonstrating that heating may be used to access cluster distributions characteristic of different stages of batch reduction synthesis in solution.

A simple and fast method to determine the parameters for fuzzy c-means cluster analysis

DEFF Research Database (Denmark)

Schwämmle, Veit; Jensen, Ole Nørregaard

2010-01-01

MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values...... of algorithm parameters. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random dataset but which detects cluster...... formation with maximum resolution on the edge of randomness. RESULTS: Estimation of the optimal parameter values is achieved by evaluation of the results of the clustering procedure applied to randomized datasets. In this case, the optimal value of the fuzzifier follows common rules that depend only...

Optimization of the annual construction program solutions

Directory of Open Access Journals (Sweden)

Oleinik Pavel

2017-01-01

Full Text Available The article considers potentially possible optimization solutions in scheduling while forming the annual production programs of the construction complex organizations. The optimization instrument is represented as a two-component system. As a fundamentally new approach in the first block of the annual program solutions, the authors propose to use a scientifically grounded methodology for determining the scope of work permissible for the transfer to a subcontractor without risk of General Contractor’s management control losing over the construction site. For this purpose, a special indicator is introduced that characterizes the activity of the general construction organization - the coefficient of construction production management. In the second block, the principal methods for the formation of calendar plans for the fulfillment of the critical work effort by the leading stream are proposed, depending on the intensity characteristic.

TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

Directory of Open Access Journals (Sweden)

C. Jehan

2016-06-01

Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

Amodified probabilistic genetic algorithm for the solution of complex constrained optimization problems

OpenAIRE

Vorozheikin, A.; Gonchar, T.; Panfilov, I.; Sopov, E.; Sopov, S.

2009-01-01

A new algorithm for the solution of complex constrained optimization problems based on the probabilistic genetic algorithm with optimal solution prediction is proposed. The efficiency investigation results in comparison with standard genetic algorithm are presented.

Optimization Route of Food Logistics Distribution Based on Genetic and Graph Cluster Scheme Algorithm

OpenAIRE

Jing Chen

2015-01-01

This study takes the concept of food logistics distribution as the breakthrough point, by means of the aim of optimization of food logistics distribution routes and analysis of the optimization model of food logistics route, as well as the interpretation of the genetic algorithm, it discusses the optimization of food logistics distribution route based on genetic and cluster scheme algorithm.

On Attainability of Optimal Solutions for Linear Elliptic Equations with Unbounded Coefficients

Directory of Open Access Journals (Sweden)

P. I. Kogut

2011-12-01

Full Text Available We study an optimal boundary control problem (OCP associated to a linear elliptic equation —div (Vj/ + A(xVy = f describing diffusion in a turbulent flow. The characteristic feature of this equation is the fact that, in applications, the stream matrix A(x = [a,ij(x]i,j=i,...,N is skew-symmetric, ац(х = —a,ji(x, measurable, and belongs to L -space (rather than L°°. An optimal solution to such problem can inherit a singular character of the original stream matrix A. We show that optimal solutions can be attainable by solutions of special optimal boundary control problems.

Quantum annealing for combinatorial clustering

Science.gov (United States)

Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

2018-02-01

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

A DFT study of the stability of SIAs and small SIA clusters in the vicinity of solute atoms in Fe

Science.gov (United States)

Becquart, C. S.; Ngayam Happy, R.; Olsson, P.; Domain, C.

2018-03-01

The energetics, defect volume and magnetic properties of single SIAs and small SIA clusters up to size 6 have been calculated by DFT for different configurations like the parallel 〈110〉 dumbbell, the non parallel 〈110〉 dumbbell and the C15 structure. The most stable configurations of each type have been further analyzed to determine the influence on their stability of various solute atoms (Ti, V, Cr, Mn, Co, Ni, Cu, Mo, W, Pd, Al, Si, P), relevant for steels used under irradiation. The results show that the presence of solute atoms does not change the relative stability order among SIA clusters. The small SIA clusters investigated can bind to both undersized and oversized solutes. Several descriptors have been considered to derive interesting trends from results. It appears that the local atomic volume available for the solute is the main physical quantity governing the binding energy evolution, whatever the solute type (undersized or oversized) and the cluster configuration (size and type).

Multiobjective optimization of urban water resources: Moving toward more practical solutions

Science.gov (United States)

Mortazavi, Mohammad; Kuczera, George; Cui, Lijie

2012-03-01

The issue of drought security is of paramount importance for cities located in regions subject to severe prolonged droughts. The prospect of "running out of water" for an extended period would threaten the very existence of the city. Managing drought security for an urban water supply is a complex task involving trade-offs between conflicting objectives. In this paper a multiobjective optimization approach for urban water resource planning and operation is developed to overcome practically significant shortcomings identified in previous work. A case study based on the headworks system for Sydney (Australia) demonstrates the approach and highlights the potentially serious shortcomings of Pareto optimal solutions conditioned on short climate records, incomplete decision spaces, and constraints to which system response is sensitive. Where high levels of drought security are required, optimal solutions conditioned on short climate records are flawed. Our approach addresses drought security explicitly by identifying approximate optimal solutions in which the system does not "run dry" in severe droughts with expected return periods up to a nominated (typically large) value. In addition, it is shown that failure to optimize the full mix of interacting operational and infrastructure decisions and to explore the trade-offs associated with sensitive constraints can lead to significantly more costly solutions.

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Directory of Open Access Journals (Sweden)

Yunfeng Dong

2017-01-01

Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

KAUST Repository

Celik, Abdulkadir

2016-06-27

In this paper, we address energy efficient (EE) cooperative spectrum sensing policies for large scale heterogeneous cognitive radio networks (CRNs) which consist of multiple primary channels and large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3) inter-cluster energy and throughput fairness. We adopt and demonstrate how to solve these using the nondominated sorting genetic algorithm-II. The micro perspective, on the other hand, operates as a sub-procedure on cluster formations decided by the macro perspective. For the micro perspectives, we first propose a procedure to select the cluster head (CH) which yields: 1) the best CH which gives the minimum total multi-hop error rate and 2) the optimal routing paths from SUs to the CH. Exploiting Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different local detection performances. Then, a convex optimization framework is established to minimize the intra-cluster energy cost by jointly obtaining the optimal sensing durations and thresholds of feature detectors for the proposed voting rule. Likewise, instead of a common fixed sample size test, we developed a weighted sample size test for quantized soft decision fusion to obtain a more EE regime under heterogeneity. We have shown that the combination of proposed CH selection and cooperation schemes gives a superior performance in terms of energy efficiency and robustness against reporting error wall.

Efficient Solutions and Cost-Optimal Analysis for Existing School Buildings

Directory of Open Access Journals (Sweden)

Paolo Maria Congedo

2016-10-01

Full Text Available The recast of the energy performance of buildings directive (EPBD describes a comparative methodological framework to promote energy efficiency and establish minimum energy performance requirements in buildings at the lowest costs. The aim of the cost-optimal methodology is to foster the achievement of nearly zero energy buildings (nZEBs, the new target for all new buildings by 2020, characterized by a high performance with a low energy requirement almost covered by renewable sources. The paper presents the results of the application of the cost-optimal methodology in two existing buildings located in the Mediterranean area. These buildings are a kindergarten and a nursery school that differ in construction period, materials and systems. Several combinations of measures have been applied to derive cost-effective efficient solutions for retrofitting. The cost-optimal level has been identified for each building and the best performing solutions have been selected considering both a financial and a macroeconomic analysis. The results illustrate the suitability of the methodology to assess cost-optimality and energy efficiency in school building refurbishment. The research shows the variants providing the most cost-effective balance between costs and energy saving. The cost-optimal solution reduces primary energy consumption by 85% and gas emissions by 82%–83% in each reference building.

Determination of atomic cluster structure with cluster fusion algorithm

DEFF Research Database (Denmark)

Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.

2005-01-01

We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....

Complicated problem solution techniques in optimal parameter searching

International Nuclear Information System (INIS)

Gergel', V.P.; Grishagin, V.A.; Rogatneva, E.A.; Strongin, R.G.; Vysotskaya, I.N.; Kukhtin, V.V.

1992-01-01

An algorithm is presented of a global search for numerical solution of multidimentional multiextremal multicriteria optimization problems with complicated constraints. A boundedness of object characteristic changes is assumed at restricted changes of its parameters (Lipschitz condition). The algorithm was realized as a computer code. The algorithm was realized as a computer code. The programme was used to solve in practice the different applied optimization problems. 10 refs.; 3 figs

The global kernel k-means algorithm for clustering in feature space.

Science.gov (United States)

Tzortzis, Grigorios F; Likas, Aristidis C

2009-07-01

Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k -means with random restarts.

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.

Science.gov (United States)

Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

2015-01-01

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.

Concentration dependence of solute atoms on vacancy cluster formation in neutron irradiated Ni alloy

International Nuclear Information System (INIS)

Sato, K.; Itoh, D.; Yoshiie, T.; Xu, Q.

2007-01-01

Full text of publication follows: One dimensional (1-D) motion of interstitial clusters is important for the microstructural evolution in metals. The movement of interstitial clusters was often observed in neutron irradiated metals by transmission electron microscopy (TEM). Alloying elements are expected to affect the motion of interstitial clusters. Yoshiie et al. have studied the effect of alloying elements in Ni. For example, in neutron irradiated pure Ni, well-developed dislocation networks and voids were observed at 573 K at a dose of 0.026 dpa by TEM. After the addition of 2at.%Si (-5.81% volume size factor to Ni) and Sn (74.08% volume size factor), no voids were detected by TEM observation and positron lifetime measurement. Alloying elements of Si and Sn were expected to prevent the 1-D motion of the interstitial clusters. In this study, the concentration dependence of alloying elements on the 1-D motion of the interstitial clusters was investigated by positron annihilation lifetime measurements, and the microstructural evolution was discussed. Specimens irradiated were 99.99 pure Ni (Johnson Matthey) and Ni based binary alloys, which contain Si, Cu, Ge and Sn as solute atoms. The concentration of solute atoms was 0.05at.%o, 0.3at.% and 2at.%. Neutron irradiation was performed with the Kyoto University Reactor (KUR) and Japan materials testing reactor (JMTR) at Japan Atomic Energy Agency. Neutron dose was 6x10 -5 -1x10 -2 dpa at KUR, and 8x10 -3 -0.3 dpa at JMTR. Irradiation temperature was 573 K at KUR and 563 K at JMTR. After the neutron irradiation, positron annihilation lifetime measurements were performed at room temperature. Microvoids were detected in pure Ni, Ni-0.05%Si, Ni-0.05%Sn, Ni-Cu and Ni-Ge alloys. In Ni-Si and Ni-Sn alloys, the size of microvoids decreased as the concentration of solute atoms increased. This is because the frequency of 1-D motion of the interstitial clusters depends on the alloy concentration. High concentration of alloying

Concentration dependence of solute atoms on vacancy cluster formation in neutron irradiated Ni alloy

Energy Technology Data Exchange (ETDEWEB)

Sato, K.; Itoh, D.; Yoshiie, T.; Xu, Q. [Kyoto Univ., Research Reactor Institute, Osaka (Japan)

2007-07-01

Full text of publication follows: One dimensional (1-D) motion of interstitial clusters is important for the microstructural evolution in metals. The movement of interstitial clusters was often observed in neutron irradiated metals by transmission electron microscopy (TEM). Alloying elements are expected to affect the motion of interstitial clusters. Yoshiie et al. have studied the effect of alloying elements in Ni. For example, in neutron irradiated pure Ni, well-developed dislocation networks and voids were observed at 573 K at a dose of 0.026 dpa by TEM. After the addition of 2at.%Si (-5.81% volume size factor to Ni) and Sn (74.08% volume size factor), no voids were detected by TEM observation and positron lifetime measurement. Alloying elements of Si and Sn were expected to prevent the 1-D motion of the interstitial clusters. In this study, the concentration dependence of alloying elements on the 1-D motion of the interstitial clusters was investigated by positron annihilation lifetime measurements, and the microstructural evolution was discussed. Specimens irradiated were 99.99 pure Ni (Johnson Matthey) and Ni based binary alloys, which contain Si, Cu, Ge and Sn as solute atoms. The concentration of solute atoms was 0.05at.%o, 0.3at.% and 2at.%. Neutron irradiation was performed with the Kyoto University Reactor (KUR) and Japan materials testing reactor (JMTR) at Japan Atomic Energy Agency. Neutron dose was 6x10{sup -5}-1x10{sup -2} dpa at KUR, and 8x10{sup -3} -0.3 dpa at JMTR. Irradiation temperature was 573 K at KUR and 563 K at JMTR. After the neutron irradiation, positron annihilation lifetime measurements were performed at room temperature. Microvoids were detected in pure Ni, Ni-0.05%Si, Ni-0.05%Sn, Ni-Cu and Ni-Ge alloys. In Ni-Si and Ni-Sn alloys, the size of microvoids decreased as the concentration of solute atoms increased. This is because the frequency of 1-D motion of the interstitial clusters depends on the alloy concentration. High

Professional cluster management by a small scientific team: challenges, solutions and perspectives

International Nuclear Information System (INIS)

Silva, Vitor V.A.; Santos, Andre A.C. dos; Cunha, Renan O.

2017-01-01

The specification, configuration and management of a professional computer cluster are specialized tasks usually hold by well trained teams, often full-time hired computer scientists. However, in many situations and for widely different reasons, these very specific technical tasks must be carried on by no other than the user itself. This is the situation at Centro de Desenvolvimento da Tecnologia Nuclear - and in many nuclear research and educational centres in developing countries - where the scientists are the users of the cluster but also the technical team responsible to keep the system running. This paper presents the process of planning and installing the whole operating system and scientific software of a professional cluster aimed to be used in the nuclear engineering eld from the point of view of its users. The drawbacks of lack of expertise and technical skills to manage such type of technology are opposed to the advantages of freedom to chose the solutions which best t to the problems to be solved. The details of selected methods or technologies chosen for addressing a specific matter are presented together with other possible options, offering a broader view of the whole process of cluster's configuration. Specificities of dealing with closed, restricted and open software, common in the nuclear engineering eld, are also put in perspective. The ideas and solutions presented in this paper can be a valuable reference to other research teams found in a similar situation: being scientists and its own technical staff at the same time. (author)

Professional cluster management by a small scientific team: challenges, solutions and perspectives

Energy Technology Data Exchange (ETDEWEB)

Silva, Vitor V.A.; Santos, Andre A.C. dos; Cunha, Renan O., E-mail: vitors@cdtn.br, E-mail: aacs@cdtn.br, E-mail: roc@cdtn.br [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

2017-11-01

The specification, configuration and management of a professional computer cluster are specialized tasks usually hold by well trained teams, often full-time hired computer scientists. However, in many situations and for widely different reasons, these very specific technical tasks must be carried on by no other than the user itself. This is the situation at Centro de Desenvolvimento da Tecnologia Nuclear - and in many nuclear research and educational centres in developing countries - where the scientists are the users of the cluster but also the technical team responsible to keep the system running. This paper presents the process of planning and installing the whole operating system and scientific software of a professional cluster aimed to be used in the nuclear engineering eld from the point of view of its users. The drawbacks of lack of expertise and technical skills to manage such type of technology are opposed to the advantages of freedom to chose the solutions which best t to the problems to be solved. The details of selected methods or technologies chosen for addressing a specific matter are presented together with other possible options, offering a broader view of the whole process of cluster's configuration. Specificities of dealing with closed, restricted and open software, common in the nuclear engineering eld, are also put in perspective. The ideas and solutions presented in this paper can be a valuable reference to other research teams found in a similar situation: being scientists and its own technical staff at the same time. (author)

Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

Directory of Open Access Journals (Sweden)

Sie Long Kek

2015-01-01

Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.

Inverse planning and optimization: a comparison of solutions

Energy Technology Data Exchange (ETDEWEB)

Ringor, Michael [School of Health Sciences, Purdue University, West Lafayette, IN (United States); Papiez, Lech [Department of Radiation Oncology, Indiana University, Indianapolis, IN (United States)

1998-09-01

The basic problem in radiation therapy treatment planning is to determine an appropriate set of treatment parameters that would induce an effective dose distribution inside a patient. One can approach this task as an inverse problem, or as an optimization problem. In this presentation, we compare both approaches. The inverse problem is presented as a dose reconstruction problem similar to tomography reconstruction. We formulate the optimization problem as linear and quadratic programs. Explicit comparisons are made between the solutions obtained by inversion and those obtained by optimization for the case in which scatter and attenuation are ignored (the NS-NA approximation)

The mechanism of solute-enriched clusters formation in neutron-irradiated pressure vessel steels: The case of Fe-Cu model alloys

Energy Technology Data Exchange (ETDEWEB)

Subbotin, A.V., E-mail: Alexey.V.Subbotin@gmail.com [Scientific and Production Complex Atomtechnoprom, Moscow 119180 (Russian Federation); Panyukov, S.V., E-mail: panyukov@lpi.ru [PN Lebedev Physics Institute, Russian Academy of Sciences, Moscow 117924 (Russian Federation)

2016-08-15

Mechanism of solute-enriched clusters formation in neutron-irradiated pressure vessel steels is proposed and developed in case of Fe-Cu model alloys. The suggested solute-drag mechanism is analogous to the well-known zone-refining process. We show that the obtained results are in good agreement with available experimental data on the parameters of clusters enriched with the alloying elements. Our model explains why the formation of solute-enriched clusters does not happen in austenitic stainless steels with fcc lattice structure. It also allows to quantify the method of evaluation of neutron irradiation dose for the process of RPV steels hardening.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

Science.gov (United States)

Zhang, Jun; Dolg, Michael

2015-10-07

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

Iterative solution to the optimal poison management problem in pressurized water reactors

International Nuclear Information System (INIS)

Colletti, J.P.; Levine, S.H.; Lewis, J.B.

1983-01-01

A new method for solving the optimal poison management problem for a multiregion pressurized water reactor has been developed. The optimization objective is to maximize the end-of-cycle core excess reactivity for any given beginning-of-cycle fuel loading. The problem is treated as an optimal control problem with the region burnup and control absorber concentrations acting as the state and control variables, respectively. Constraints are placed on the power peaking, soluble boron concentration, and control absorber concentrations. The solution method consists of successive relinearizations of the system equations resulting in a sequence of nonlinear programming problems whose solutions converge to the desired optimal control solution. Application of the method to several test problems based on a simplified three-region reactor suggests a bang-bang optimal control strategy with the peak power location switching between the inner and outer regions of the core and the critical soluble boron concentration as low as possible throughout the cycle

Smooth Solutions to Optimal Investment Models with Stochastic Volatilities and Portfolio Constraints

International Nuclear Information System (INIS)

Pham, H.

2002-01-01

This paper deals with an extension of Merton's optimal investment problem to a multidimensional model with stochastic volatility and portfolio constraints. The classical dynamic programming approach leads to a characterization of the value function as a viscosity solution of the highly nonlinear associated Bellman equation. A logarithmic transformation expresses the value function in terms of the solution to a semilinear parabolic equation with quadratic growth on the derivative term. Using a stochastic control representation and some approximations, we prove the existence of a smooth solution to this semilinear equation. An optimal portfolio is shown to exist, and is expressed in terms of the classical solution to this semilinear equation. This reduction is useful for studying numerical schemes for both the value function and the optimal portfolio. We illustrate our results with several examples of stochastic volatility models popular in the financial literature

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

Energy Technology Data Exchange (ETDEWEB)

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

International Nuclear Information System (INIS)

Lalonde, Michel; Wassenaar, Richard; Wells, R. Glenn; Birnie, David; Ruddy, Terrence D.

2014-01-01

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms

International Nuclear Information System (INIS)

Gavrielides, Marios A.; Lo, Joseph Y.; Floyd, Carey E. Jr.

2002-01-01

Our purpose in this study is to develop a parameter optimization technique for the segmentation of suspicious microcalcification clusters in digitized mammograms. In previous work, a computer-aided diagnosis (CAD) scheme was developed that used local histogram analysis of overlapping subimages and a fuzzy rule-based classifier to segment individual microcalcifications, and clustering analysis for reducing the number of false positive clusters. The performance of this previous CAD scheme depended on a large number of parameters such as the intervals used to calculate fuzzy membership values and on the combination of membership values used by each decision rule. These parameters were optimized empirically based on the performance of the algorithm on the training set. In order to overcome the limitations of manual training and rule generation, the segmentation algorithm was modified in order to incorporate automatic parameter optimization. For the segmentation of individual microcalcifications, the new algorithm used a neural network with fuzzy-scaled inputs. The fuzzy-scaled inputs were created by processing the histogram features with a family of membership functions, the parameters of which were automatically extracted from the distribution of the feature values. The neural network was trained to classify feature vectors as either positive or negative. Individual microcalcifications were segmented from positive subimages. After clustering, another neural network was trained to eliminate false positive clusters. A database of 98 images provided training and testing sets to optimize the parameters and evaluate the CAD scheme, respectively. The performance of the algorithm was evaluated with a FROC analysis. At a sensitivity rate of 93.2%, there was an average of 0.8 false positive clusters per image. The results are very comparable with those taken using our previously published rule-based method. However, the new algorithm is more suited to generalize its

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.

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Yuliang Wang

Full Text Available Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.

New numerical methods for open-loop and feedback solutions to dynamic optimization problems

Science.gov (United States)

Ghosh, Pradipto

The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development

Gas load forecasting based on optimized fuzzy c-mean clustering analysis of selecting similar days

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Qiu Jing

2017-08-01

Full Text Available Traditional fuzzy c-means (FCM clustering in short term load forecasting method is easy to fall into local optimum and is sensitive to the initial cluster center.In this paper,we propose to use global search feature of particle swarm optimization (PSO algorithm to avoid these shortcomings,and to use FCM optimization to select similar date of forecast as training sample of support vector machines.This will not only strengthen the data rule of training samples,but also ensure the consistency of data characteristics.Experimental results show that the prediction accuracy of this prediction model is better than that of BP neural network and support vector machine (SVM algorithms.

Ensemble Clustering using Semidefinite Programming with Applications.

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Singh, Vikas; Mukherjee, Lopamudra; Peng, Jiming; Xu, Jinhui

2010-05-01

In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

Solution for state constrained optimal control problems applied to power split control for hybrid vehicles

NARCIS (Netherlands)

Keulen, van T.A.C.; Gillot, J.; Jager, de A.G.; Steinbuch, M.

2014-01-01

This paper presents a numerical solution for scalar state constrained optimal control problems. The algorithm rewrites the constrained optimal control problem as a sequence of unconstrained optimal control problems which can be solved recursively as a two point boundary value problem. The solution

Energy efficiency: EDF Optimal Solutions improves the l'Oreal firm of Vichy; Efficacite energetique: EDF Optimal Solutions ameliore l'usine l'Oreal de Vichy

Energy Technology Data Exchange (ETDEWEB)

Anon.

2011-09-15

The l'Oreal CAP site has inaugurated its energetic eco-efficiency installations realized by EDF Optimal Solutions. This solution combines several techniques and makes possible to halve its yearly CO{sub 2} releases. (O.M.)

Data Clustering

Science.gov (United States)

Wagstaff, Kiri L.

2012-03-01

clustering, in which some partial information about item assignments or other components of the resulting output are already known and must be accommodated by the solution. Some algorithms seek a partition of the data set into distinct clusters, while others build a hierarchy of nested clusters that can capture taxonomic relationships. Some produce a single optimal solution, while others construct a probabilistic model of cluster membership. More formally, clustering algorithms operate on a data set X composed of items represented by one or more features (dimensions). These could include physical location, such as right ascension and declination, as well as other properties such as brightness, color, temporal change, size, texture, and so on. Let D be the number of dimensions used to represent each item, xi ∈ RD. The clustering goal is to produce an organization P of the items in X that optimizes an objective function f : P -> R, which quantifies the quality of solution P. Often f is defined so as to maximize similarity within a cluster and minimize similarity between clusters. To that end, many algorithms make use of a measure d : X x X -> R of the distance between two items. A partitioning algorithm produces a set of clusters P = {c1, . . . , ck} such that the clusters are nonoverlapping (c_i intersected with c_j = empty set, i != j) subsets of the data set (Union_i c_i=X). Hierarchical algorithms produce a series of partitions P = {p1, . . . , pn }. For a complete hierarchy, the number of partitions n’= n, the number of items in the data set; the top partition is a single cluster containing all items, and the bottom partition contains n clusters, each containing a single item. For model-based clustering, each cluster c_j is represented by a model m_j , such as the cluster center or a Gaussian distribution. The wide array of available clustering algorithms may seem bewildering, and covering all of them is beyond the scope of this chapter. Choosing among them for a

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

Energy Technology Data Exchange (ETDEWEB)

Dall' Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.

2015-07-01

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the control of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.

Multiresolution strategies for the numerical solution of optimal control problems

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Jain, Sachin

There exist many numerical techniques for solving optimal control problems but less work has been done in the field of making these algorithms run faster and more robustly. The main motivation of this work is to solve optimal control problems accurately in a fast and efficient way. Optimal control problems are often characterized by discontinuities or switchings in the control variables. One way of accurately capturing the irregularities in the solution is to use a high resolution (dense) uniform grid. This requires a large amount of computational resources both in terms of CPU time and memory. Hence, in order to accurately capture any irregularities in the solution using a few computational resources, one can refine the mesh locally in the region close to an irregularity instead of refining the mesh uniformly over the whole domain. Therefore, a novel multiresolution scheme for data compression has been designed which is shown to outperform similar data compression schemes. Specifically, we have shown that the proposed approach results in fewer grid points in the grid compared to a common multiresolution data compression scheme. The validity of the proposed mesh refinement algorithm has been verified by solving several challenging initial-boundary value problems for evolution equations in 1D. The examples have demonstrated the stability and robustness of the proposed algorithm. The algorithm adapted dynamically to any existing or emerging irregularities in the solution by automatically allocating more grid points to the region where the solution exhibited sharp features and fewer points to the region where the solution was smooth. Thereby, the computational time and memory usage has been reduced significantly, while maintaining an accuracy equivalent to the one obtained using a fine uniform mesh. Next, a direct multiresolution-based approach for solving trajectory optimization problems is developed. The original optimal control problem is transcribed into a

Application of Delaunay tessellation for the characterization of solute-rich clusters in atom probe tomography

International Nuclear Information System (INIS)

Lefebvre, W.; Philippe, T.; Vurpillot, F.

2011-01-01

This work presents an original method for cluster selection in Atom Probe Tomography designed to be applied to large datasets. It is based on the calculation of the Delaunay tessellation generated by the distribution of atoms of a selected element. It requires a single input parameter from the user. Furthermore, no prior knowledge of the material is needed. The sensitivity of the proposed Delaunay cluster selection is demonstrated by its application on simulated APT datasets. A strong advantage of the proposed methodology is that it is reinforced by the availability of an analytical model for the distribution of Delaunay cells circumspheres, which is used to control the accuracy of the cluster selection procedure. Another advantage of the Delaunay cluster selection is the direct calculation of a sharp envelope for each identified cluster or precipitate, which leads to the more appropriate morphology of the objects as they are reconstructed in the APT dataset. -- Research Highligthts: →Original method for cluster selection in Atom Probe Tomography. →Delaunay tessellation generated by the distribution of solute atoms. →Direct calculation of a sharp envelope for each identified cluster or precipitate. →Delaunay cluster selection demonstrated by its application on simulated APT datasets.

Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

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Sahraei, S.; Asadzadeh, M.

2017-12-01

Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

Ion clustering in aqueous salt solutions near the liquid/vapor interface

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J.D. Smith

2016-03-01

Full Text Available Molecular dynamics simulations of aqueous NaCl, KCl, NaI, and KI solutions are used to study the effects of salts on the properties of the liquid/vapor interface. The simulations use the models which include both charge transfer and polarization effects. Pairing and the formation of larger ion clusters occurs both in the bulk and surface region, with a decreased tendency to form larger clusters near the interface. An analysis of the roughness of the surface reveals that the chloride salts, which have less tendency to be near the surface, have a roughness that is less than pure water, while the iodide salts, which have a greater surface affinity, have a larger roughness. This suggests that ions away from the surface and ions near the surface affect the interface in opposite ways.

The Fundamental Solution and Its Role in the Optimal Control of Infinite Dimensional Neutral Systems

International Nuclear Information System (INIS)

Liu Kai

2009-01-01

In this work, we shall consider standard optimal control problems for a class of neutral functional differential equations in Banach spaces. As the basis of a systematic theory of neutral models, the fundamental solution is constructed and a variation of constants formula of mild solutions is established. We introduce a class of neutral resolvents and show that the Laplace transform of the fundamental solution is its neutral resolvent operator. Necessary conditions in terms of the solutions of neutral adjoint systems are established to deal with the fixed time integral convex cost problem of optimality. Based on optimality conditions, the maximum principle for time varying control domain is presented. Finally, the time optimal control problem to a target set is investigated

Pulse and gamma radiolytic studies of Ag, Cd and mixed clusters in aqueous solutions of carboxymethyl cellulose and gelatin

International Nuclear Information System (INIS)

Kapoor, Sudhir; Gopinathan, C.

1996-01-01

Pulse and gamma radiolytic studies in aqueous solutions of Ag, Cd and mixed clusters were carried out in carboxymethylcellulose (CMC) or gelatin. The reaction rate of e aq - with Ag + is lower in the presence of CMC or gelatin and oligomeric clusters of silver, Cd and mixed clusters get stabilized in their presence. (author). 2 refs., 2 figs

A clustering model proposal for activity optimization vs. image quality. Application in HMPAO-99mTc brain SPECT

International Nuclear Information System (INIS)

Perez, M.; Diaz, O.; Estevez, E.; Roque, R.; Hernandez, C.

2008-01-01

A method based on clustering analysis and image quality discriminant functions is tested over HMPAO- 99m Tc -SPECT images to optimize image quality vs. administered activity to patient. Signal-to-Noise ratios and Signal-to-Background ratios in each transaxial slice were measured over the images and used as image quality objective indexes. They were employed to develop image quality classification using a model of three clusters (k-mean Cluster Technique). The mathematical classification was compared with the subjective image quality evaluation, following expert observer criterion. Linear image quality discrimination was developed using the results of the cluster classification and taking as independent variables: the characteristics of the patients (sex, age and weight) and the radiopharmaceutical (labelling yield and administered activity). The dependent variables in the discriminant functions were the cluster centroids. The objective of this procedure was assigning a statistical weight to each image quality determinant variable for the optimization purpose. The method was applied in 30 brain SPECT images, acquired under different activities (400 MBq, 600 MBq, 800 MBq and 1000 MBq as reference), with a Sopha gamma camera, mean-resolution - general purpose - parallel-hole collimator, acquisition matrix 128 x 128 pixels and reconstruction following Filter - Back - Projection Algorithm. We looked for the minimum activity that guaranty most of the cases classified into the cluster with the best image quality (optimization criterion). The value of 800 MBq was the optimum obtained after application of the above methodology for the technology used. The labelling yield and the weight of the patients were the main parameters which determined image quality in clusters, as well as the processing digital filter used. The reduction in the effective dose, as a consequence of this procedure implementation, was also analysed. (author)

Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery under k-means Clustering Guidance

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WANG Huixian

2015-05-01

Full Text Available In order to adapt different scale land cover segmentation, an optimized approach under the guidance of k-means clustering for multi-scale segmentation is proposed. At first, small scale segmentation and k-means clustering are used to process the original images; then the result of k-means clustering is used to guide objects merging procedure, in which Otsu threshold method is used to automatically select the impact factor of k-means clustering; finally we obtain the segmentation results which are applicable to different scale objects. FNEA method is taken for an example and segmentation experiments are done using a simulated image and a real remote sensing image from GeoEye-1 satellite, qualitative and quantitative evaluation demonstrates that the proposed method can obtain high quality segmentation results.

Analytic solution to variance optimization with no short positions

Science.gov (United States)

Kondor, Imre; Papp, Gábor; Caccioli, Fabio

2017-12-01

We consider the variance portfolio optimization problem with a ban on short selling. We provide an analytical solution by means of the replica method for the case of a portfolio of independent, but not identically distributed, assets. We study the behavior of the solution as a function of the ratio r between the number N of assets and the length T of the time series of returns used to estimate risk. The no-short-selling constraint acts as an asymmetric \

Differential dynamic microscopy of weakly scattering and polydisperse protein-rich clusters

Science.gov (United States)

Safari, Mohammad S.; Vorontsova, Maria A.; Poling-Skutvik, Ryan; Vekilov, Peter G.; Conrad, Jacinta C.

2015-10-01

Nanoparticle dynamics impact a wide range of biological transport processes and applications in nanomedicine and natural resource engineering. Differential dynamic microscopy (DDM) was recently developed to quantify the dynamics of submicron particles in solutions from fluctuations of intensity in optical micrographs. Differential dynamic microscopy is well established for monodisperse particle populations, but has not been applied to solutions containing weakly scattering polydisperse biological nanoparticles. Here we use bright-field DDM (BDDM) to measure the dynamics of protein-rich liquid clusters, whose size ranges from tens to hundreds of nanometers and whose total volume fraction is less than 10-5. With solutions of two proteins, hemoglobin A and lysozyme, we evaluate the cluster diffusion coefficients from the dependence of the diffusive relaxation time on the scattering wave vector. We establish that for weakly scattering populations, an optimal thickness of the sample chamber exists at which the BDDM signal is maximized at the smallest sample volume. The average cluster diffusion coefficient measured using BDDM is consistently lower than that obtained from dynamic light scattering at a scattering angle of 90∘. This apparent discrepancy is due to Mie scattering from the polydisperse cluster population, in which larger clusters preferentially scatter more light in the forward direction.

A Multi-Hop Clustering Mechanism for Scalable IoT Networks.

Science.gov (United States)

Sung, Yoonyoung; Lee, Sookyoung; Lee, Meejeong

2018-03-23

It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63-87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6-89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.

Detecting and extracting clusters in atom probe data: A simple, automated method using Voronoi cells

International Nuclear Information System (INIS)

Felfer, P.; Ceguerra, A.V.; Ringer, S.P.; Cairney, J.M.

2015-01-01

The analysis of the formation of clusters in solid solutions is one of the most common uses of atom probe tomography. Here, we present a method where we use the Voronoi tessellation of the solute atoms and its geometric dual, the Delaunay triangulation to test for spatial/chemical randomness of the solid solution as well as extracting the clusters themselves. We show how the parameters necessary for cluster extraction can be determined automatically, i.e. without user interaction, making it an ideal tool for the screening of datasets and the pre-filtering of structures for other spatial analysis techniques. Since the Voronoi volumes are closely related to atomic concentrations, the parameters resulting from this analysis can also be used for other concentration based methods such as iso-surfaces. - Highlights: • Cluster analysis of atom probe data can be significantly simplified by using the Voronoi cell volumes of the atomic distribution. • Concentration fields are defined on a single atomic basis using Voronoi cells. • All parameters for the analysis are determined by optimizing the separation probability of bulk atoms vs clustered atoms

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Science.gov (United States)

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdoreza

2017-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.

Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution

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Prabha Umapathy

2010-01-01

Full Text Available This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization (PSO. The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight (CIW, a time-varying inertia weight (TVIW, and global-local best inertia weight (GLbestIW, are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.

Cooperative protein structural dynamics of homodimeric hemoglobin linked to water cluster at subunit interface revealed by time-resolved X-ray solution scattering

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Jong Goo Kim

2016-03-01

Full Text Available Homodimeric hemoglobin (HbI consisting of two subunits is a good model system for investigating the allosteric structural transition as it exhibits cooperativity in ligand binding. In this work, as an effort to extend our previous study on wild-type and F97Y mutant HbI, we investigate structural dynamics of a mutant HbI in solution to examine the role of well-organized interfacial water cluster, which has been known to mediate intersubunit communication in HbI. In the T72V mutant of HbI, the interfacial water cluster in the T state is perturbed due to the lack of Thr72, resulting in two less interfacial water molecules than in wild-type HbI. By performing picosecond time-resolved X-ray solution scattering experiment and kinetic analysis on the T72V mutant, we identify three structurally distinct intermediates (I1, I2, and I3 and show that the kinetics of the T72V mutant are well described by the same kinetic model used for wild-type and F97Y HbI, which involves biphasic kinetics, geminate recombination, and bimolecular CO recombination. The optimized kinetic model shows that the R-T transition and bimolecular CO recombination are faster in the T72V mutant than in the wild type. From structural analysis using species-associated difference scattering curves for the intermediates, we find that the T-like deoxy I3 intermediate in solution has a different structure from deoxy HbI in crystal. In addition, we extract detailed structural parameters of the intermediates such as E-F distance, intersubunit rotation angle, and heme-heme distance. By comparing the structures of protein intermediates in wild-type HbI and the T72V mutant, we reveal how the perturbation in the interfacial water cluster affects the kinetics and structures of reaction intermediates of HbI.

Optimal Solutions of Multiproduct Batch Chemical Process Using Multiobjective Genetic Algorithm with Expert Decision System

Science.gov (United States)

Mokeddem, Diab; Khellaf, Abdelhafid

2009-01-01

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples. PMID:19543537

The use of hierarchical clustering for the design of optimized monitoring networks

Science.gov (United States)

Soares, Joana; Makar, Paul Andrew; Aklilu, Yayne; Akingunola, Ayodeji

2018-05-01

Associativity analysis is a powerful tool to deal with large-scale datasets by clustering the data on the basis of (dis)similarity and can be used to assess the efficacy and design of air quality monitoring networks. We describe here our use of Kolmogorov-Zurbenko filtering and hierarchical clustering of NO2 and SO2 passive and continuous monitoring data to analyse and optimize air quality networks for these species in the province of Alberta, Canada. The methodology applied in this study assesses dissimilarity between monitoring station time series based on two metrics: 1 - R, R being the Pearson correlation coefficient, and the Euclidean distance; we find that both should be used in evaluating monitoring site similarity. We have combined the analytic power of hierarchical clustering with the spatial information provided by deterministic air quality model results, using the gridded time series of model output as potential station locations, as a proxy for assessing monitoring network design and for network optimization. We demonstrate that clustering results depend on the air contaminant analysed, reflecting the difference in the respective emission sources of SO2 and NO2 in the region under study. Our work shows that much of the signal identifying the sources of NO2 and SO2 emissions resides in shorter timescales (hourly to daily) due to short-term variation of concentrations and that longer-term averages in data collection may lose the information needed to identify local sources. However, the methodology identifies stations mainly influenced by seasonality, if larger timescales (weekly to monthly) are considered. We have performed the first dissimilarity analysis based on gridded air quality model output and have shown that the methodology is capable of generating maps of subregions within which a single station will represent the entire subregion, to a given level of dissimilarity. We have also shown that our approach is capable of identifying different

A modified teaching–learning based optimization for multi-objective optimal power flow problem

International Nuclear Information System (INIS)

Shabanpour-Haghighi, Amin; Seifi, Ali Reza; Niknam, Taher

2014-01-01

Highlights: • A new modified teaching–learning based algorithm is proposed. • A self-adaptive wavelet mutation strategy is used to enhance the performance. • To avoid reaching a large repository size, a fuzzy clustering technique is used. • An efficiently smart population selection is utilized. • Simulations show the superiority of this algorithm compared with other ones. - Abstract: In this paper, a modified teaching–learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques

A three-stage strategy for optimal price offering by a retailer based on clustering techniques

International Nuclear Information System (INIS)

Mahmoudi-Kohan, N.; Shayesteh, E.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

2010-01-01

In this paper, an innovative strategy for optimal price offering to customers for maximizing the profit of a retailer is proposed. This strategy is based on load profile clustering techniques and includes three stages. For the purpose of clustering, an improved weighted fuzzy average K-means is proposed. Also, in this paper a new acceptance function for increasing the profit of the retailer is proposed. The new method is evaluated by implementation on a group of 300 customers of a 20 kV distribution network. (author)

A three-stage strategy for optimal price offering by a retailer based on clustering techniques

Energy Technology Data Exchange (ETDEWEB)

Mahmoudi-Kohan, N.; Shayesteh, E. [Islamic Azad University (Garmsar Branch), Garmsar (Iran); Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K. [Tarbiat Modares University, Tehran (Iran)

2010-12-15

In this paper, an innovative strategy for optimal price offering to customers for maximizing the profit of a retailer is proposed. This strategy is based on load profile clustering techniques and includes three stages. For the purpose of clustering, an improved weighted fuzzy average K-means is proposed. Also, in this paper a new acceptance function for increasing the profit of the retailer is proposed. The new method is evaluated by implementation on a group of 300 customers of a 20 kV distribution network. (author)

Optimization of the recycling process of precipitation barren solution in a uranium mine

International Nuclear Information System (INIS)

Long Qing; Yu Suqin; Zhao Wucheng; Han Wei; Zhang Hui; Chen Shuangxi

2014-01-01

Alkaline leaching process was adopted to recover uranium from ores in a uranium mine, and high concentration uranium solution, which would be later used in precipitation, was obtained after ion-exchange and elution steps. The eluting agent consisted of NaCl and NaHCO 3 . Though precipitation barren solution contained as high as 80 g/L Na 2 CO 3 , it still can not be recycled due to presence of high Cl - concentration So, both elution and precipitation processes were optimized in order to control the Cl - concentration in the precipitation barren solution to the recyclable concentration range. Because the precipitation barren solution can be recycled by optimization, the agent consumption was lowered and the discharge of waste water was reduced. (authors)

Optimization of process and solution parameters in electrospinning polyethylene oxide

CSIR Research Space (South Africa)

Jacobs, V

2011-11-01

Full Text Available This paper reports the optimization of electrospinning process and solution parameters using factorial design approach to obtain uniform polyethylene oxide (PEO) nanofibers. The parameters studied were distance between nozzle and collector screen...

DMSO-Water Clustering in Solution Observed in Soft X-ray Spectra.

Science.gov (United States)

Engel, Nicholas; Atak, Kaan; Lange, Kathrin M; Gotz, Malte; Soldatov, Mikhail; Golnak, Ronny; Suljoti, Edlira; Rubensson, Jan-Erik; Aziz, Emad F

2012-12-20

The significant deviation from the ideality of dimethyl sulfoxide (DMSO)/water mixtures can be addressed based on the change of the local molecular orbitals of each solvent upon mixing. Oxygen K-edge absorption and emission spectra of DMSO/water solutions were measured using the liquid microjet technique. The spectra demonstrate that the hydrogen bond network in liquid water is already influenced at small DMSO concentrations, and at the molar fraction xDMSO = 0.43 we find strong evidence of DMSO-water clustering reflected by the influence on the occupied molecular orbitals.

An accurate approximate solution of optimal sequential age replacement policy for a finite-time horizon

International Nuclear Information System (INIS)

Jiang, R.

2009-01-01

It is difficult to find the optimal solution of the sequential age replacement policy for a finite-time horizon. This paper presents an accurate approximation to find an approximate optimal solution of the sequential replacement policy. The proposed approximation is computationally simple and suitable for any failure distribution. Their accuracy is illustrated by two examples. Based on the approximate solution, an approximate estimate for the total cost is derived.

A formal concept analysis approach to consensus clustering of multi-experiment expression data

Science.gov (United States)

2014-01-01

Background Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them. Results We propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group. These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological

Optimal Design Solutions for Permanent Magnet Synchronous Machines

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POPESCU, M.

2011-11-01

Full Text Available This paper presents optimal design solutions for reducing the cogging torque of permanent magnets synchronous machines. A first solution proposed in the paper consists in using closed stator slots that determines a nearly isotropic magnetic structure of the stator core, reducing the mutual attraction between permanent magnets and the slotted armature. To avoid complications in the windings manufacture technology the stator slots are closed using wedges made of soft magnetic composite materials. The second solution consists in properly choosing the combination of pole number and stator slots number that typically leads to a winding with fractional number of slots/pole/phase. The proposed measures for cogging torque reduction are analyzed by means of 2D/3D finite element models developed using the professional Flux software package. Numerical results are discussed and compared with experimental ones obtained by testing a PMSM prototype.

Optimizing 10-Gigabit Ethernet for Networks of Workstations, Clusters, and Grids: A Case Study

Energy Technology Data Exchange (ETDEWEB)

Feng, Wu-chun

2003-10-13

This paper presents a case study of the 10-Gigabit Ethernet (10GbE) adapter from Intel(reg sign). Specifically, with appropriate optimizations to the configurations of the 10GbE adapter and TCP, we demonstrate that the 10GbE adapter can perform well in local-area, storage-area, system-area, and wide-area networks. For local-area, storage-area, and system-area networks in support of networks of workstations, network-attached storage, and clusters, respectively, we can achieve over 7-Gb/s end-to-end throughput and 12-{micro}s end-to-end latency between applications running on Linux-based PCs. For the wide-area network in support of grids, we broke the recently-set Internet2 Land Speed Record by 2.5 times by sustaining an end-to-end TCP/IP throughput of 2.38 Gb/s between Sunnyvale, California and Geneva, Switzerland (i.e., 10,037 kilometers) to move over a terabyte of data in less than an hour. Thus, the above results indicate that 10GbE may be a cost-effective solution across a multitude of computing environments.

Application of artificial intelligence to search ground-state geometry of clusters

International Nuclear Information System (INIS)

Lemes, Mauricio Ruv; Marim, L.R.; Dal Pino, A. Jr.

2002-01-01

We introduce a global optimization procedure, the neural-assisted genetic algorithm (NAGA). It combines the power of an artificial neural network (ANN) with the versatility of the genetic algorithm. This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space. If a reasonable amount of data is available, the ANN can 'understand' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution. We tested the method in a search for the ground-state geometry of silicon clusters. We trained the ANN with information about the geometry and energetics of small silicon clusters. Next, the ANN learned how to restrict the configurational space for larger silicon clusters. For Si 10 and Si 20 , we noticed that the NAGA is at least three times faster than the 'pure' genetic algorithm. As the size of the cluster increases, it is expected that the gain in terms of time will increase as well

A New Methodology to Select the Preferred Solutions from the Pareto-optimal Set: Application to Polymer Extrusion

International Nuclear Information System (INIS)

Ferreira, Jose C.; Gaspar-Cunha, Antonio; Fonseca, Carlos M.

2007-01-01

Most of the real world optimization problems involve multiple, usually conflicting, optimization criteria. Generating Pareto optimal solutions plays an important role in multi-objective optimization, and the problem is considered to be solved when the Pareto optimal set is found, i.e., the set of non-dominated solutions. Multi-Objective Evolutionary Algorithms based on the principle of Pareto optimality are designed to produce the complete set of non-dominated solutions. However, this is not allays enough since the aim is not only to know the Pareto set but, also, to obtain one solution from this Pareto set. Thus, the definition of a methodology able to select a single solution from the set of non-dominated solutions (or a region of the Pareto frontier), and taking into account the preferences of a Decision Maker (DM), is necessary. A different method, based on a weighted stress function, is proposed. It is able to integrate the user's preferences in order to find the best region of the Pareto frontier accordingly with these preferences. This method was tested on some benchmark test problems, with two and three criteria, and on a polymer extrusion problem. This methodology is able to select efficiently the best Pareto-frontier region for the specified relative importance of the criteria

Exploitation of Clustering Techniques in Transactional Healthcare Data

Directory of Open Access Journals (Sweden)

Naeem Ahmad Mahoto

2014-03-01

Full Text Available Healthcare service centres equipped with electronic health systems have improved their resources as well as treatment processes. The dynamic nature of healthcare data of each individual makes it complex and difficult for physicians to manually mediate them; therefore, automatic techniques are essential to manage the quality and standardization of treatment procedures. Exploratory data analysis, patternanalysis and grouping of data is managed using clustering techniques, which work as an unsupervised classification. A number of healthcare applications are developed that use several data mining techniques for classification, clustering and extracting useful information from healthcare data. The challenging issue in this domain is to select adequate data mining algorithm for optimal results. This paper exploits three different clustering algorithms: DBSCAN (Density-Based Clustering, agglomerative hierarchical and k-means in real transactional healthcare data of diabetic patients (taken as case study to analyse their performance in large and dispersed healthcare data. The best solution of cluster sets among the exploited algorithms is evaluated using clustering quality indexes and is selected to identify the possible subgroups of patients having similar treatment patterns

Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

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I. Hameem Shanavas

2014-01-01

Full Text Available In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

CLUSTER ANALYSIS UKRAINIAN REGIONAL DISTRIBUTION BY LEVEL OF INNOVATION

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Roman Shchur

2016-07-01

Full Text Available   SWOT-analysis of the threats and benefits of innovation development strategy of Ivano-Frankivsk region in the context of financial support was сonducted. Methodical approach to determine of public-private partnerships potential that is tool of innovative economic development financing was identified. Cluster analysis of possibilities of forming public-private partnership in a particular region was carried out. Optimal set of problem areas that require urgent solutions and financial security is defined on the basis of cluster approach. It will help to form practical recommendations for the formation of an effective financial mechanism in the regions of Ukraine. Key words: the mechanism of innovation development financial provision, innovation development, public-private partnerships, cluster analysis, innovative development strategy.

Regulation of Renewable Energy Sources to Optimal Power Flow Solutions Using ADMM: Preprint

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yijian; Hong, Mingyi; Dall' Anese, Emiliano; Dhople, Sairaj; Xu, Zi

2017-03-03

This paper considers power distribution systems featuring renewable energy sources (RESs), and develops a distributed optimization method to steer the RES output powers to solutions of AC optimal power flow (OPF) problems. The design of the proposed method leverages suitable linear approximations of the AC-power flow equations, and is based on the Alternating Direction Method of Multipliers (ADMM). Convergence of the RES-inverter output powers to solutions of the OPF problem is established under suitable conditions on the stepsize as well as mismatches between the commanded setpoints and actual RES output powers. In a broad sense, the methods and results proposed here are also applicable to other distributed optimization problem setups with ADMM and inexact dual updates.

Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

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Jocelyn H Bolin

2014-04-01

Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

Science.gov (United States)

Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Modelling of heterogeneous clustering in aluminium

International Nuclear Information System (INIS)

Smith, A.E.; Bourgeois, L.; Nie, J.-F.; Muddle, B.C.

2003-01-01

Full text: Ab initio modelling of heterogeneous clustering in aluminium has been carried out in order to study the precipitation hardening of alloys. This process is based on the addition of small amounts of solute element to the pure metal. With increasing computational power, atomic scale effects can now be better simulated to determine the nature of the hardening mechanism. Comparisons are made between results obtained from two computational packages. These are the Linear Augmented Plane Wave WEEN2K and the plane wave pseudopotential density functional theory package fhi98md. The study of the optimal geometry of very small size clusters inside aluminium has begun with the testing of initial convergence conditions by determination of binding energies for a variety of super cell sizes of the aluminium host crystal. These are compared with total energy calculations for small size precipitates of copper and transition metals of fixed geometry. Such local optimal determinations are seen as precursors to full Monte Carlo calculations of the notional best local geometry for larger precipitates

Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

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Yun-Hao Li

2015-12-01

Full Text Available This paper presents a flexible transmission network expansion planning (TNEP approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.

Scaling behavior of ground-state energy cluster expansion for linear polyenes

Science.gov (United States)

Griffin, L. L.; Wu, Jian; Klein, D. J.; Schmalz, T. G.; Bytautas, L.

Ground-state energies for linear-chain polyenes are additively expanded in a sequence of terms for chemically relevant conjugated substructures of increasing size. The asymptotic behavior of the large-substructure limit (i.e., high-polymer limit) is investigated as a means of characterizing the rapidity of convergence and consequent utility of this energy cluster expansion. Consideration is directed to computations via: simple Hückel theory, a refined Hückel scheme with geometry optimization, restricted Hartree-Fock self-consistent field (RHF-SCF) solutions of fixed bond-length Parisier-Parr-Pople (PPP)/Hubbard models, and ab initio SCF approaches with and without geometry optimization. The cluster expansion in what might be described as the more "refined" approaches appears to lead to qualitatively more rapid convergence: exponentially fast as opposed to an inverse power at the simple Hückel or SCF-Hubbard levels. The substructural energy cluster expansion then seems to merit special attention. Its possible utility in making accurate extrapolations from finite systems to extended polymers is noted.

The gravitational attraction algorithm: a new metaheuristic applied to a nuclear reactor core design optimization problem

International Nuclear Information System (INIS)

Sacco, Wagner F.; Oliveira, Cassiano R.E. de

2005-01-01

A new metaheuristic called 'Gravitational Attraction Algorithm' (GAA) is introduced in this article. It is an analogy with the gravitational force field, where a body attracts another proportionally to both masses and inversely to their distances. The GAA is a populational algorithm where, first of all, the solutions are clustered using the Fuzzy Clustering Means (FCM) algorithm. Following that, the gravitational forces of the individuals in relation to each cluster are evaluated and this individual or solution is displaced to the cluster with the greatest attractive force. Once it is inside this cluster, the solution receives small stochastic variations, performing a local exploration. Then the solutions are crossed over and the process starts all over again. The parameters required by the GAA are the 'diversity factor', which is used to create a random diversity in a fashion similar to genetic algorithm's mutation, and the number of clusters for the FCM. GAA is applied to the reactor core design optimization problem which consists in adjusting several reactor cell parameters in order to minimize the average peak-factor in a 3-enrichment-zone reactor, considering operational restrictions. This problem was previously attacked using the canonical genetic algorithm (GA) and a Niching Genetic Algorithm (NGA). The new metaheuristic is then compared to those two algorithms. The three algorithms are submitted to the same computational effort and GAA reaches the best results, showing its potential for other applications in the nuclear engineering field as, for instance, the nuclear core reload optimization problem. (author)

An Analytical Solution for Yaw Maneuver Optimization on the International Space Station and Other Orbiting Space Vehicles

Science.gov (United States)

Dobrinskaya, Tatiana

2015-01-01

This paper suggests a new method for optimizing yaw maneuvers on the International Space Station (ISS). Yaw rotations are the most common large maneuvers on the ISS often used for docking and undocking operations, as well as for other activities. When maneuver optimization is used, large maneuvers, which were performed on thrusters, could be performed either using control moment gyroscopes (CMG), or with significantly reduced thruster firings. Maneuver optimization helps to save expensive propellant and reduce structural loads - an important factor for the ISS service life. In addition, optimized maneuvers reduce contamination of the critical elements of the vehicle structure, such as solar arrays. This paper presents an analytical solution for optimizing yaw attitude maneuvers. Equations describing pitch and roll motion needed to counteract the major torques during a yaw maneuver are obtained. A yaw rate profile is proposed. Also the paper describes the physical basis of the suggested optimization approach. In the obtained optimized case, the torques are significantly reduced. This torque reduction was compared to the existing optimization method which utilizes the computational solution. It was shown that the attitude profiles and the torque reduction have a good match for these two methods of optimization. The simulations using the ISS flight software showed similar propellant consumption for both methods. The analytical solution proposed in this paper has major benefits with respect to computational approach. In contrast to the current computational solution, which only can be calculated on the ground, the analytical solution does not require extensive computational resources, and can be implemented in the onboard software, thus, making the maneuver execution automatic. The automatic maneuver significantly simplifies the operations and, if necessary, allows to perform a maneuver without communication with the ground. It also reduces the probability of command

Jets from jets: re-clustering as a tool for large radius jet reconstruction and grooming at the LHC

Science.gov (United States)

Nachman, Benjamin; Nef, Pascal; Schwartzman, Ariel; Swiatlowski, Maximilian; Wanotayaroj, Chaowaroj

2015-02-01

Jets with a large radius R ≳ 1 and grooming algorithms are widely used to fully capture the decay products of boosted heavy particles at the Large Hadron Collider (LHC). Unlike most discriminating variables used in such studies, the jet radius is usually not optimized for specific physics scenarios. This is because every jet configuration must be calibrated, insitu, to account for detector response and other experimental effects. One solution to enhance the availability of large- R jet configurations used by the LHC experiments is jet re-clustering. Jet re-clustering introduces an intermediate scale r groomed jets. Jet re-clustering has the benefit that no additional large-R calibration is necessary, allowing the re-clustered large radius parameter to be optimized in the context of specific precision measurements or searches for new physics.

A theoretical investigation on optimal structures of ethane clusters (C2H6)n with n ≤ 25 and their building-up principle.

Science.gov (United States)

Takeuchi, Hiroshi

2011-05-01

Geometry optimization of ethane clusters (C(2)H(6))(n) in the range of n ≤ 25 is carried out with a Morse potential. A heuristic method based on perturbations of geometries is used to locate global minima of the clusters. The following perturbations are carried out: (1) the molecule or group with the highest energy is moved to the interior of a cluster, (2) it is moved to stable positions on the surface of a cluster, and (3) orientations of one and two molecules are randomly modified. The geometry obtained after each perturbation is optimized by a quasi-Newton method. The global minimum of the dimer is consistent with that previously reported. The putative global minima of the clusters with 3 ≤ n ≤ 25 are first proposed and their building-up principle is discussed. Copyright © 2010 Wiley Periodicals, Inc.

Concurrent conditional clustering of multiple networks: COCONETS.

Directory of Open Access Journals (Sweden)

Sabrina Kleessen

Full Text Available The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components. We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by

Formation mechanism of solute clusters under neutron irradiation in ferritic model alloys and in a reactor pressure vessel steel: clusters of defects; Mecanismes de fragilisation sous irradiation aux neutrons d'alliages modeles ferritiques et d'un acier de cuve: amas de defauts

Energy Technology Data Exchange (ETDEWEB)

Meslin-Chiffon, E

2007-11-15

The embrittlement of reactor pressure vessel (RPV) under irradiation is partly due to the formation of point defects (PD) and solute clusters. The aim of this work was to gain more insight into the formation mechanisms of solute clusters in low copper ([Cu] = 0.1 wt%) FeCu and FeCuMnNi model alloys, in a copper free FeMnNi model alloy and in a low copper French RPV steel (16MND5). These materials were neutron-irradiated around 300 C in a test reactor. Solute clusters were characterized by tomographic atom probe whereas PD clusters were simulated with a rate theory numerical code calibrated under cascade damage conditions using transmission electron microscopy analysis. The confrontation between experiments and simulation reveals that a heterogeneous irradiation-induced solute precipitation/segregation probably occurs on PD clusters. (author)

Mathematical classification and clustering

CERN Document Server

Mirkin, Boris

1996-01-01

I am very happy to have this opportunity to present the work of Boris Mirkin, a distinguished Russian scholar in the areas of data analysis and decision making methodologies. The monograph is devoted entirely to clustering, a discipline dispersed through many theoretical and application areas, from mathematical statistics and combina­ torial optimization to biology, sociology and organizational structures. It compiles an immense amount of research done to date, including many original Russian de­ velopments never presented to the international community before (for instance, cluster-by-cluster versions of the K-Means method in Chapter 4 or uniform par­ titioning in Chapter 5). The author's approach, approximation clustering, allows him both to systematize a great part of the discipline and to develop many in­ novative methods in the framework of optimization problems. The optimization methods considered are proved to be meaningful in the contexts of data analysis and clustering. The material presented in ...

Modified genetic algorithms to model cluster structures in medium-size silicon clusters

International Nuclear Information System (INIS)

Bazterra, Victor E.; Ona, Ofelia; Caputo, Maria C.; Ferraro, Marta B.; Fuentealba, Patricio; Facelli, Julio C.

2004-01-01

This paper presents the results obtained using a genetic algorithm (GA) to search for stable structures of medium size silicon clusters. In this work the GA uses a semiempirical energy function to find the best cluster structures, which are further optimized using density-functional theory. For small clusters our results agree well with previously reported structures, but for larger ones different structures appear. This is the case of Si 36 where we report a different structure, with significant lower energy than those previously found using limited search approaches on common structural motifs. This demonstrates the need for global optimization schemes when searching for stable structures of medium-size silicon clusters

Envri Cluster - a Community-Driven Platform of European Environmental Researcher Infrastructures for Providing Common E-Solutions for Earth Science

Science.gov (United States)

Asmi, A.; Sorvari, S.; Kutsch, W. L.; Laj, P.

2017-12-01

European long-term environmental research infrastructures (often referred as ESFRI RIs) are the core facilities for providing services for scientists in their quest for understanding and predicting the complex Earth system and its functioning that requires long-term efforts to identify environmental changes (trends, thresholds and resilience, interactions and feedbacks). Many of the research infrastructures originally have been developed to respond to the needs of their specific research communities, however, it is clear that strong collaboration among research infrastructures is needed to serve the trans-boundary research requires exploring scientific questions at the intersection of different scientific fields, conducting joint research projects and developing concepts, devices, and methods that can be used to integrate knowledge. European Environmental research infrastructures have already been successfully worked together for many years and have established a cluster - ENVRI cluster - for their collaborative work. ENVRI cluster act as a collaborative platform where the RIs can jointly agree on the common solutions for their operations, draft strategies and policies and share best practices and knowledge. Supporting project for the ENVRI cluster, ENVRIplus project, brings together 21 European research infrastructures and infrastructure networks to work on joint technical solutions, data interoperability, access management, training, strategies and dissemination efforts. ENVRI cluster act as one stop shop for multidisciplinary RI users, other collaborative initiatives, projects and programmes and coordinates and implement jointly agreed RI strategies.

[Optimal solution and analysis of muscular force during standing balance].

Science.gov (United States)

Wang, Hongrui; Zheng, Hui; Liu, Kun

2015-02-01

The present study was aimed at the optimal solution of the main muscular force distribution in the lower extremity during standing balance of human. The movement musculoskeletal system of lower extremity was simplified to a physical model with 3 joints and 9 muscles. Then on the basis of this model, an optimum mathematical model was built up to solve the problem of redundant muscle forces. Particle swarm optimization (PSO) algorithm is used to calculate the single objective and multi-objective problem respectively. The numerical results indicated that the multi-objective optimization could be more reasonable to obtain the distribution and variation of the 9 muscular forces. Finally, the coordination of each muscle group during maintaining standing balance under the passive movement was qualitatively analyzed using the simulation results obtained.

Clustering Effects on Dynamics in Ionomer Solutions: A Neutron Spin Echo Insight

Science.gov (United States)

Perahia, Dvora; Wijesinghe, Sidath; Senanayake, Manjula; Wickramasinghe, Anuradhi; Mohottalalage, Supun S.; Ohl, Michael

Ionizable blocks in ionomers associate into aggregates serving as physical cross-links and concurrently form transport pathways. The dynamics of ionomers underline their functionality. Incorporating small numbers of ionic groups into polymers significantly constraint their dynamics. Recent computational studies demonstrated a direct correlation between ionic cluster morphology and polymer dynamics. Here using neutron spin echo, we probe the segmental dynamics of polystyrene sulfonate (PSS) as the degree of sulfonation of the PSS and the solution dielectrics are varied. Specifically, 20Wt% PSS of 11,000 g/mol with polydispersity of 1.02 with 3% and 9% sulfonation were studies in toluene (dielectric constant ɛ = 2.8), a good solvent for polystyrene, and with 5Wt% of ethanol (ɛ = 24.3l) added. The dynamic structure factor S(q,t) was analyzed with a single exponential except for a limited q range where two time constants associated with constraint and mobile segments were detected. S(q,t) exhibits several distinctive time and length scales for the dynamics with a crossover appearing at the length scale of the ionic clusters. NSF DMR 1611136.

Optimal solution of full fuzzy transportation problems using total integral ranking

Science.gov (United States)

Sam’an, M.; Farikhin; Hariyanto, S.; Surarso, B.

2018-03-01

Full fuzzy transportation problem (FFTP) is a transportation problem where transport costs, demand, supply and decision variables are expressed in form of fuzzy numbers. To solve fuzzy transportation problem, fuzzy number parameter must be converted to a crisp number called defuzzyfication method. In this new total integral ranking method with fuzzy numbers from conversion of trapezoidal fuzzy numbers to hexagonal fuzzy numbers obtained result of consistency defuzzyfication on symmetrical fuzzy hexagonal and non symmetrical type 2 numbers with fuzzy triangular numbers. To calculate of optimum solution FTP used fuzzy transportation algorithm with least cost method. From this optimum solution, it is found that use of fuzzy number form total integral ranking with index of optimism gives different optimum value. In addition, total integral ranking value using hexagonal fuzzy numbers has an optimal value better than the total integral ranking value using trapezoidal fuzzy numbers.

Optimization of the solution of the problem of scheduling theory ...

African Journals Online (AJOL)

This article describes the genetic algorithm used to solve the problem related to the scheduling theory. A large number of different methods is described in the scientific literature. The main issue that faced the problem in question is that it is necessary to search the optimal solution in a large search space for the set of ...

Domainwise Web Page Optimization Based On Clustered Query Sessions Using Hybrid Of Trust And ACO For Effective Information Retrieval

Directory of Open Access Journals (Sweden)

Dr. Suruchi Chawla

2015-08-01

Full Text Available Abstract In this paper hybrid of Ant Colony OptimizationACO and trust has been used for domainwise web page optimization in clustered query sessions for effective Information retrieval. The trust of the web page identifies its degree of relevance in satisfying specific information need of the user. The trusted web pages when optimized using pheromone updates in ACO will identify the trusted colonies of web pages which will be relevant to users information need in a given domain. Hence in this paper the hybrid of Trust and ACO has been used on clustered query sessions for identifying more and more relevant number of documents in a given domain in order to better satisfy the information need of the user. Experiment was conducted on the data set of web query sessions to test the effectiveness of the proposed approach in selected three domains Academics Entertainment and Sports and the results confirm the improvement in the precision of search results.

Pareto Optimal Solutions for Network Defense Strategy Selection Simulator in Multi-Objective Reinforcement Learning

Directory of Open Access Journals (Sweden)

Yang Sun

2018-01-01

Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.

VM-based infrastructure for simulating different cluster and storage solutions in ATLAS

CERN Document Server

KUTOUSKI, M; The ATLAS collaboration; PETROSYAN, A; KADOCHNIKOV, I; BELOV, S; KORENKOV, V

2012-01-01

The current ATLAS Tier3 infrastructure consists of a variety of sites of different sizes and with a mix of local resource management systems (LRMS) and mass storage system (MSS) implementations. The Tier3 monitoring suite, having been developed in order to satisfy the needs of Tier3 site administrators and to aggregate Tier3 monitoring information on the global VO level, needs to be validated for various combinations of LRMS and MSS solutions along with the corresponding Ganglia and/or Nagios plugins. For this purpose the Testbed infrastructure, which allows simulation of various computational cluster and storage solutions, had been set up at JINR (Dubna). This infrastructure provides the ability to run testbeds with various LRMS and MSS implementations, and with the capability to quickly redeploy particular testbeds or their components. Performance of specific components is not a critical issue for development and validation, whereas easy management and deployment are crucial. Therefore virtual machines were...

Robust continuous clustering.

Science.gov (United States)

Shah, Sohil Atul; Koltun, Vladlen

2017-09-12

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank.

Relevant Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2009-01-01

Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

Distributed Algorithms for Time Optimal Reachability Analysis

DEFF Research Database (Denmark)

Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand

2016-01-01

. We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general.......Time optimal reachability analysis is a novel model based technique for solving scheduling and planning problems. After modeling them as reachability problems using timed automata, a real-time model checker can compute the fastest trace to the goal states which constitutes a time optimal schedule...

Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution

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R. Manam

2017-12-01

Full Text Available In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

The optimal solution of a non-convex state-dependent LQR problem and its applications.

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Xudan Xu

Full Text Available This paper studies a Non-convex State-dependent Linear Quadratic Regulator (NSLQR problem, in which the control penalty weighting matrix [Formula: see text] in the performance index is state-dependent. A necessary and sufficient condition for the optimal solution is established with a rigorous proof by Euler-Lagrange Equation. It is found that the optimal solution of the NSLQR problem can be obtained by solving a Pseudo-Differential-Riccati-Equation (PDRE simultaneously with the closed-loop system equation. A Comparison Theorem for the PDRE is given to facilitate solution methods for the PDRE. A linear time-variant system is employed as an example in simulation to verify the proposed optimal solution. As a non-trivial application, a goal pursuit process in psychology is modeled as a NSLQR problem and two typical goal pursuit behaviors found in human and animals are reproduced using different control weighting [Formula: see text]. It is found that these two behaviors save control energy and cause less stress over Conventional Control Behavior typified by the LQR control with a constant control weighting [Formula: see text], in situations where only the goal discrepancy at the terminal time is of concern, such as in Marathon races and target hitting missions.

Jets from jets: re-clustering as a tool for large radius jet reconstruction and grooming at the LHC

Energy Technology Data Exchange (ETDEWEB)

Nachman, Benjamin; Nef, Pascal; Schwartzman, Ariel; Swiatlowski, Maximilian [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Wanotayaroj, Chaowaroj [Center for High Energy Physics, University of Oregon,1371 E. 13th Ave, Eugene, OR 97403 (United States)

2015-02-12

Jets with a large radius R≳1 and grooming algorithms are widely used to fully capture the decay products of boosted heavy particles at the Large Hadron Collider (LHC). Unlike most discriminating variables used in such studies, the jet radius is usually not optimized for specific physics scenarios. This is because every jet configuration must be calibrated, insitu, to account for detector response and other experimental effects. One solution to enhance the availability of large-R jet configurations used by the LHC experiments is jet re-clustering. Jet re-clustering introduces an intermediate scale rclustering configurations and show that re-clustered large radius jets have essentially the same jet mass performance as large radius groomed jets. Jet re-clustering has the benefit that no additional large-R calibration is necessary, allowing the re-clustered large radius parameter to be optimized in the context of specific precision measurements or searches for new physics.

Jets from jets: re-clustering as a tool for large radius jet reconstruction and grooming at the LHC

International Nuclear Information System (INIS)

Nachman, Benjamin; Nef, Pascal; Schwartzman, Ariel; Swiatlowski, Maximilian; Wanotayaroj, Chaowaroj

2015-01-01

Jets with a large radius R≳1 and grooming algorithms are widely used to fully capture the decay products of boosted heavy particles at the Large Hadron Collider (LHC). Unlike most discriminating variables used in such studies, the jet radius is usually not optimized for specific physics scenarios. This is because every jet configuration must be calibrated, insitu, to account for detector response and other experimental effects. One solution to enhance the availability of large-R jet configurations used by the LHC experiments is jet re-clustering. Jet re-clustering introduces an intermediate scale rclustering configurations and show that re-clustered large radius jets have essentially the same jet mass performance as large radius groomed jets. Jet re-clustering has the benefit that no additional large-R calibration is necessary, allowing the re-clustered large radius parameter to be optimized in the context of specific precision measurements or searches for new physics.

Multi-objective optimization approach for air traffic flow management

Directory of Open Access Journals (Sweden)

Fadil Rabie

2017-01-01

The decision-making stage was then performed with the aid of data clustering techniques to reduce the sizeof the Pareto-optimal set and obtain a smaller representation of the multi-objective design space, there by making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

ADAPTIVE CLUSTER BASED ROUTING PROTOCOL WITH ANT COLONY OPTIMIZATION FOR MOBILE AD-HOC NETWORK IN DISASTER AREA

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Enrico Budianto

2012-07-01

Full Text Available In post-disaster rehabilitation efforts, the availability of telecommunication facilities takes important role. However, the process to improve telecommunication facilities in disaster area is risky if it is done by humans. Therefore, a network method that can work efficiently, effectively, and capable to reach the widest possible area is needed. This research introduces a cluster-based routing protocol named Adaptive Cluster Based Routing Protocol (ACBRP equipped by Ant Colony Optimization method, and its implementation in a simulator developed by author. After data analysis and statistical tests, it can be concluded that routing protocol ACBRP performs better than AODV and DSR routing protocol. Pada upaya rehabilitasi pascabencana, ketersediaan fasilitas telekomunikasi memiliki peranan yang sangat penting. Namun, proses untuk memperbaiki fasilitas telekomunikasi di daerah bencana memiliki resiko jika dilakukan oleh manusia. Oleh karena itu, metode jaringan yang dapat bekerja secara efisien, efektif, dan mampu mencapai area seluas mungkin diperlukan. Penelitian ini memperkenalkan sebuah protokol routing berbasis klaster bernama Adaptive Cluster Based Routing Protocol (ACBRP, yang dilengkapi dengan metode Ant Colony Optimization, dan diimplementasikan pada simulator yang dikembangkan penulis. Setelah data dianalisis dan dilakukan uji statistik, disimpulkan bahwa protokol routing ACBRP beroperasi lebih baik daripada protokol routing AODV maupun DSR.

Cluster analysis of stress corrosion mechanisms for steel wires used in bridge cables through acoustic emission particle swarm optimization.

Science.gov (United States)

Li, Dongsheng; Yang, Wei; Zhang, Wenyao

2017-05-01

Stress corrosion is the major failure type of bridge cable damage. The acoustic emission (AE) technique was applied to monitor the stress corrosion process of steel wires used in bridge cable structures. The damage evolution of stress corrosion in bridge cables was obtained according to the AE characteristic parameter figure. A particle swarm optimization cluster method was developed to determine the relationship between the AE signal and stress corrosion mechanisms. Results indicate that the main AE sources of stress corrosion in bridge cables included four types: passive film breakdown and detachment of the corrosion product, crack initiation, crack extension, and cable fracture. By analyzing different types of clustering data, the mean value of each damage pattern's AE characteristic parameters was determined. Different corrosion damage source AE waveforms and the peak frequency were extracted. AE particle swarm optimization cluster analysis based on principal component analysis was also proposed. This method can completely distinguish the four types of damage sources and simplifies the determination of the evolution process of corrosion damage and broken wire signals. Copyright © 2017. Published by Elsevier B.V.

Analyze the optimal solutions of optimization problems by means of fractional gradient based system using VIM

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Firat Evirgen

2016-04-01

Full Text Available In this paper, a class of Nonlinear Programming problem is modeled with gradient based system of fractional order differential equations in Caputo's sense. To see the overlap between the equilibrium point of the fractional order dynamic system and theoptimal solution of the NLP problem in a longer timespan the Multistage Variational İteration Method isapplied. The comparisons among the multistage variational iteration method, the variationaliteration method and the fourth order Runge-Kutta method in fractional and integer order showthat fractional order model and techniques can be seen as an effective and reliable tool for finding optimal solutions of Nonlinear Programming problems.

The Successor Function and Pareto Optimal Solutions of Cooperative Differential Systems with Concavity. I

DEFF Research Database (Denmark)

Andersen, Kurt Munk; Sandqvist, Allan

1997-01-01

We investigate the domain of definition and the domain of values for the successor function of a cooperative differential system x'=f(t,x), where the coordinate functions are concave in x for any fixed value of t. Moreover, we give a characterization of a weakly Pareto optimal solution.......We investigate the domain of definition and the domain of values for the successor function of a cooperative differential system x'=f(t,x), where the coordinate functions are concave in x for any fixed value of t. Moreover, we give a characterization of a weakly Pareto optimal solution....

Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

Science.gov (United States)

Niknam, Taher; Kavousifard, Abdollah; Tabatabaei, Sajad; Aghaei, Jamshid

2011-10-01

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the 'best' compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.

Subtypes of borderline personality disorder patients: a cluster-analytic approach.

Science.gov (United States)

Smits, Maaike L; Feenstra, Dine J; Bales, Dawn L; de Vos, Jasmijn; Lucas, Zwaan; Verheul, Roel; Luyten, Patrick

2017-01-01

The borderline personality disorder (BPD) population is notably heterogeneous, and this has potentially important implications for intervention. Identifying distinct subtypes of patients may represent a first step in identifying which treatments work best for which individuals. A cluster-analysis on dimensional personality disorder (PD) features, as assessed with the SCID-II, was performed on a sample of carefully screened BPD patients ( N  = 187) referred for mentalization-based treatment. The optimal cluster solution was determined using multiple indices of fit. The validity of the clusters was explored by investigating their relationship with borderline pathology, symptom severity, interpersonal problems, quality of life, personality functioning, attachment, and trauma history, in addition to demographic and clinical features. A three-cluster solution was retained, which identified three clusters of BPD patients with distinct profiles. The largest cluster ( n  = 145) consisted of patients characterized by "core BPD" features, without marked elevations on other PD dimensions. A second "Extravert/externalizing" cluster of patients ( n  = 27) was characterized by high levels of histrionic, narcissistic, and antisocial features. A third, smaller "Schizotypal/paranoid" cluster ( n  = 15) consisted of patients with marked schizotypal and paranoid features. Patients in these clusters showed theoretically meaningful differences in terms of demographic and clinical features. Three meaningful subtypes of BPD patients were identified with distinct profiles. Differences were small, even when controlling for severity of PD pathology, suggesting a strong common factor underlying BPD. These results may represent a stepping stone toward research with larger samples aimed at replicating the findings and investigating differential trajectories of change, treatment outcomes, and treatment approaches for these subtypes. The study was retrospectively registered 16 April 2010

Transformation kinetics for nucleus clusters

International Nuclear Information System (INIS)

Villa, Elena; Rios, Paulo R.

2009-01-01

A rigorous mathematical approach based on stochastic geometry concepts is presented to extend previous Johnson-Mehl, Avrami, Kolmogorov treatment of transformation kinetics to situations in which nuclei are not homogeneously located in space but are located in clusters. An exact analytical solution is presented here for the first time assuming that nucleation sites follow a Matern cluster process. The influence of Matern cluster process parameters on subsequent growth kinetics and the microstructural path are illustrated by means of numerical examples. Moreover, using the superposition principle, exact analytical solutions are also obtained when nucleation takes place by a combination of a Matern cluster process and an inhomogeneous Poisson point process. The new solutions presented here significantly increase the number of exactly solvable cases available to formal kinetics.

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Rajeev Kumar

2016-01-01

Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

Solution of relativistic quantum optics problems using clusters of graphical processing units

Energy Technology Data Exchange (ETDEWEB)

Gordon, D.F., E-mail: daviel.gordon@nrl.navy.mil; Hafizi, B.; Helle, M.H.

2014-06-15

Numerical solution of relativistic quantum optics problems requires high performance computing due to the rapid oscillations in a relativistic wavefunction. Clusters of graphical processing units are used to accelerate the computation of a time dependent relativistic wavefunction in an arbitrary external potential. The stationary states in a Coulomb potential and uniform magnetic field are determined analytically and numerically, so that they can used as initial conditions in fully time dependent calculations. Relativistic energy levels in extreme magnetic fields are recovered as a means of validation. The relativistic ionization rate is computed for an ion illuminated by a laser field near the usual barrier suppression threshold, and the ionizing wavefunction is displayed.

Hydrogen-bonded clusters of 1, 1'-ferrocenedicarboxylic acid on Au(111) are initially formed in solution

Science.gov (United States)

Quardokus, Rebecca C.; Wasio, Natalie A.; Brown, Ryan D.; Christie, John A.; Henderson, Kenneth W.; Forrest, Ryan P.; Lent, Craig S.; Corcelli, Steven A.; Alex Kandel, S.

2015-03-01

Low-temperature scanning tunneling microscopy is used to observe self-assembled structures of ferrocenedicarboxylic acid (Fc(COOH)2) on the Au(111) surface. The surface is prepared by pulse-deposition of Fc(COOH)2 dissolved in methanol, and the solvent is evaporated before imaging. While the rows of hydrogen-bonded dimers that are common for carboxylic acid species are observed, the majority of adsorbed Fc(COOH)2 is instead found in six-molecule clusters with a well-defined and chiral geometry. The coverage and distribution of these clusters are consistent with a random sequential adsorption model, showing that solution-phase species are determinative of adsorbate distribution for this system under these reaction conditions.

A New Method Based on Simulation-Optimization Approach to Find Optimal Solution in Dynamic Job-shop Scheduling Problem with Breakdown and Rework

Directory of Open Access Journals (Sweden)

Farzad Amirkhani

2017-03-01

The proposed method is implemented on classical job-shop problems with objective of makespan and results are compared with mixed integer programming model. Moreover, the appropriate dispatching priorities are achieved for dynamic job-shop problem minimizing a multi-objective criteria. The results show that simulation-based optimization are highly capable to capture the main characteristics of the shop and produce optimal/near-optimal solutions with highly credibility degree.

Tax solutions for optimal reduction of tobacco use in West Africa ...

International Development Research Centre (IDRC) Digital Library (Canada)

Tax solutions for optimal reduction of tobacco use in West Africa. During the first phase of this project, numerous decision-makers were engaged and involved in discussions with the goal of establishing a new taxation system to reduce tobacco use in West Africa. Although regional economic authorities (ECOWAS and ...

A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization

International Nuclear Information System (INIS)

Zhang, Enze; Wu, Yifei; Chen, Qingwei

2014-01-01

This paper proposes a practical approach, combining bare-bones particle swarm optimization and sensitivity-based clustering for solving multi-objective reliability redundancy allocation problems (RAPs). A two-stage process is performed to identify promising solutions. Specifically, a new bare-bones multi-objective particle swarm optimization algorithm (BBMOPSO) is developed and applied in the first stage to identify a Pareto-optimal set. This algorithm mainly differs from other multi-objective particle swarm optimization algorithms in the parameter-free particle updating strategy, which is especially suitable for handling the complexity and nonlinearity of RAPs. Moreover, by utilizing an approach based on the adaptive grid to update the global particle leaders, a mutation operator to improve the exploration ability and an effective constraint handling strategy, the integrated BBMOPSO algorithm can generate excellent approximation of the true Pareto-optimal front for RAPs. This is followed by a data clustering technique based on difference sensitivity in the second stage to prune the obtained Pareto-optimal set and obtain a small, workable sized set of promising solutions for system implementation. Two illustrative examples are presented to show the feasibility and effectiveness of the proposed approach

Linux software for large topology optimization problems

DEFF Research Database (Denmark)

evolving product, which allows a parallel solution of the PDE, it lacks the important feature that the matrix-generation part of the computations is localized to each processor. This is well-known to be critical for obtaining a useful speedup on a Linux cluster and it motivates the search for a COMSOL......-like package for large topology optimization problems. One candidate for such software is developed for Linux by Sandia Nat’l Lab in the USA being the Sundance system. Sundance also uses a symbolic representation of the PDE and a scalable numerical solution is achieved by employing the underlying Trilinos...

Robust segmentation of medical images using competitive hop field neural network as a clustering tool

International Nuclear Information System (INIS)

Golparvar Roozbahani, R.; Ghassemian, M. H.; Sharafat, A. R.

2001-01-01

This paper presents the application of competitive Hop field neural network for medical images segmentation. Our proposed approach consists of Two steps: 1) translating segmentation of the given medical image into an optimization problem, and 2) solving this problem by a version of Hop field network known as competitive Hop field neural network. Segmentation is considered as a clustering problem and its validity criterion is based on both intra set distance and inter set distance. The algorithm proposed in this paper is based on gray level features only. This leads to near optimal solutions if both intra set distance and inter set distance are considered at the same time. If only one of these distances is considered, the result of segmentation process by competitive Hop field neural network will be far from optimal solution and incorrect even for very simple cases. Furthermore, sometimes the algorithm receives at unacceptable states. Both these problems may be solved by contributing both in tera distance and inter distances in the segmentation (optimization) process. The performance of the proposed algorithm is tested on both phantom and real medical images. The promising results and the robustness of algorithm to system noises show near optimal solutions

Modeling of the (liquid + liquid) equilibrium of polydisperse hyperbranched polymer solutions by lattice-cluster theory

International Nuclear Information System (INIS)

Enders, Sabine; Browarzik, Dieter

2014-01-01

Graphical abstract: - Highlights: • Calculation of the (liquid + liquid) equilibrium of hyperbranched polymer solutions. • Description of branching effects by the lattice-cluster theory. • Consideration of self- and cross association by chemical association models. • Treatment of the molar-mass polydispersity by the use of continuous thermodynamics. • Improvement of the theoretical results by the incorporation of polydispersity. - Abstract: The (liquid + liquid) equilibrium of solutions of hyperbranched polymers of the Boltorn type is modeled in the framework of lattice-cluster theory. The association effects are described by the chemical association models CALM (for self association) and ECALM (for cross association). For the first time the molar mass polydispersity of the hyperbranched polymers is taken into account. For this purpose continuous thermodynamics is applied. Because the segment-molar excess Gibbs free energy depends on the number average of the segment number of the polymer the treatment is more general than in previous papers on continuous thermodynamics. The polydispersity is described by a generalized Schulz–Flory distribution. The calculation of the cloud-point curve reduces to two equations that have to be numerically solved. Conditions for the calculation of the spinodal curve and of the critical point are derived. The calculated results are compared to experimental data taken from the literature. For Boltorn solutions in non-polar solvents the polydispersity influence is small. In all other of the considered cases polydispersity influences the (liquid + liquid) equilibrium considerably. However, association and polydispersity influence phase equilibrium in a complex manner. Taking polydispersity into account the accuracy of the calculations is improved, especially, in the diluted region

Space-planning and structural solutions of low-rise buildings: Optimal selection methods

Science.gov (United States)

Gusakova, Natalya; Minaev, Nikolay; Filushina, Kristina; Dobrynina, Olga; Gusakov, Alexander

2017-11-01

The present study is devoted to elaboration of methodology used to select appropriately the space-planning and structural solutions in low-rise buildings. Objective of the study is working out the system of criteria influencing the selection of space-planning and structural solutions which are most suitable for low-rise buildings and structures. Application of the defined criteria in practice aim to enhance the efficiency of capital investments, energy and resource saving, create comfortable conditions for the population considering climatic zoning of the construction site. Developments of the project can be applied while implementing investment-construction projects of low-rise housing at different kinds of territories based on the local building materials. The system of criteria influencing the optimal selection of space-planning and structural solutions of low-rise buildings has been developed. Methodological basis has been also elaborated to assess optimal selection of space-planning and structural solutions of low-rise buildings satisfying the requirements of energy-efficiency, comfort and safety, and economical efficiency. Elaborated methodology enables to intensify the processes of low-rise construction development for different types of territories taking into account climatic zoning of the construction site. Stimulation of low-rise construction processes should be based on the system of approaches which are scientifically justified; thus it allows enhancing energy efficiency, comfort, safety and economical effectiveness of low-rise buildings.

K-maps: a vehicle to an optimal solution in combinational logic ...

African Journals Online (AJOL)

K-maps: a vehicle to an optimal solution in combinational logic design problems using digital multiplexers. ... Abstract. Application of Karnaugh maps (K-Maps) for the design of combinational logic circuits and sequential logic circuits is a subject that has been widely discussed. However, the use of K-Maps in the design of ...

Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

Science.gov (United States)

Giesy, D. P.

1978-01-01

A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

Maximization of the Supportable Number of Sensors in QoS-Aware Cluster-Based Underwater Acoustic Sensor Networks

Directory of Open Access Journals (Sweden)

Thi-Tham Nguyen

2014-03-01

Full Text Available This paper proposes a practical low-complexity MAC (medium access control scheme for quality of service (QoS-aware and cluster-based underwater acoustic sensor networks (UASN, in which the provision of differentiated QoS is required. In such a network, underwater sensors (U-sensor in a cluster are divided into several classes, each of which has a different QoS requirement. The major problem considered in this paper is the maximization of the number of nodes that a cluster can accommodate while still providing the required QoS for each class in terms of the PDR (packet delivery ratio. In order to address the problem, we first estimate the packet delivery probability (PDP and use it to formulate an optimization problem to determine the optimal value of the maximum packet retransmissions for each QoS class. The custom greedy and interior-point algorithms are used to find the optimal solutions, which are verified by extensive simulations. The simulation results show that, by solving the proposed optimization problem, the supportable number of underwater sensor nodes can be maximized while satisfying the QoS requirements for each class.

Energy aware swarm optimization with intercluster search for wireless sensor network.

Science.gov (United States)

Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan

2015-01-01

Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

Optimizing Energy Consumption in Vehicular Sensor Networks by Clustering Using Fuzzy C-Means and Fuzzy Subtractive Algorithms

Science.gov (United States)

Ebrahimi, A.; Pahlavani, P.; Masoumi, Z.

2017-09-01

Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.

Lattice cluster theory of associating polymers. I. Solutions of linear telechelic polymer chains.

Science.gov (United States)

Dudowicz, Jacek; Freed, Karl F

2012-02-14

The lattice cluster theory (LCT) for the thermodynamics of a wide array of polymer systems has been developed by using an analogy to Mayer's virial expansions for non-ideal gases. However, the high-temperature expansion inherent to the LCT has heretofore precluded its application to systems exhibiting strong, specific "sticky" interactions. The present paper describes a reformulation of the LCT necessary to treat systems with both weak and strong, "sticky" interactions. This initial study concerns solutions of linear telechelic chains (with stickers at the chain ends) as the self-assembling system. The main idea behind this extension of the LCT lies in the extraction of terms associated with the strong interactions from the cluster expansion. The generalized LCT for sticky systems reduces to the quasi-chemical theory of hydrogen bonding of Panyioutou and Sanchez when correlation corrections are neglected in the LCT. A diagrammatic representation is employed to facilitate the evaluation of the corrections to the zeroth-order approximation from short range correlations. © 2012 American Institute of Physics

Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm

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Ahmet Demir

2017-01-01

Full Text Available In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA and Cognitive Development Optimization Algorithm (CoDOA, have been used to solve some multidisciplinary optimization problems provided in the source book Thomas' Calculus 11th Edition and the obtained results have compared with classical optimization solutions. 

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

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Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Theoretical studies on photoelectron and IR spectral properties of Br2.-(H2O)n clusters.

Science.gov (United States)

Pathak, A K; Mukherjee, T; Maity, D K

2007-07-28

We report vertical detachment energy (VDE) and IR spectra of Br2.-.(H2O)n clusters (n=1-8) based on first principles electronic structure calculations. Cluster structures and IR spectra are calculated at Becke's half-and-half hybrid exchange-correlation functional (BHHLYP) with a triple split valence basis function, 6-311++G(d,p). VDE for the hydrated clusters is calculated based on second order Moller-Plesset perturbation (MP2) theory with the same set of basis function. On full geometry optimization, it is observed that conformers having interwater hydrogen bonding among solvent water molecules are more stable than the structures having double or single hydrogen bonded structures between the anionic solute, Br2.-, and solvent water molecules. Moreover, a conformer having cyclic interwater hydrogen bonded network is predicted to be more stable for each size hydrated cluster. It is also noticed that up to four solvent H2O units can reside around the solute in a cyclic interwater hydrogen bonded network. The excess electron in these hydrated clusters is localized over the solute atoms. Weighted average VDE is calculated for each size (n) cluster based on statistical population of the conformers at 150 K. A linear relationship is obtained for VDE versus (n+3)(-1/3) and bulk VDE of Br2.- aqueous solution is calculated as 10.01 eV at MP2 level of theory. BHHLYP density functional is seen to make a systematic overestimation in VDE values by approximately 0.5 eV compared to MP2 data in all the hydrated clusters. It is observed that hydration increases VDE of bromine dimer anion system by approximately 6.4 eV. Calculated IR spectra show that the formation of Br2.--water clusters induces large shifts from the normal O-H stretching bands of isolated water keeping bending modes rather insensitive. Hydrated clusters, Br2.-.(H2O)n, show characteristic sharp features of O-H stretching bands of water in the small size clusters.

Theoretical studies on photoelectron and IR spectral properties of Br2.-(H2O)n clusters

Science.gov (United States)

Pathak, A. K.; Mukherjee, T.; Maity, D. K.

2007-07-01

We report vertical detachment energy (VDE) and IR spectra of Br2•-•(H2O)n clusters (n=1-8) based on first principles electronic structure calculations. Cluster structures and IR spectra are calculated at Becke's half-and-half hybrid exchange-correlation functional (BHHLYP) with a triple split valence basis function, 6-311++G(d,p). VDE for the hydrated clusters is calculated based on second order Moller-Plesset perturbation (MP2) theory with the same set of basis function. On full geometry optimization, it is observed that conformers having interwater hydrogen bonding among solvent water molecules are more stable than the structures having double or single hydrogen bonded structures between the anionic solute, Br2•-, and solvent water molecules. Moreover, a conformer having cyclic interwater hydrogen bonded network is predicted to be more stable for each size hydrated cluster. It is also noticed that up to four solvent H2O units can reside around the solute in a cyclic interwater hydrogen bonded network. The excess electron in these hydrated clusters is localized over the solute atoms. Weighted average VDE is calculated for each size (n) cluster based on statistical population of the conformers at 150K. A linear relationship is obtained for VDE versus (n+3)-1/3 and bulk VDE of Br2•- aqueous solution is calculated as 10.01eV at MP2 level of theory. BHHLYP density functional is seen to make a systematic overestimation in VDE values by ˜0.5eV compared to MP2 data in all the hydrated clusters. It is observed that hydration increases VDE of bromine dimer anion system by ˜6.4eV. Calculated IR spectra show that the formation of Br2•--water clusters induces large shifts from the normal O-H stretching bands of isolated water keeping bending modes rather insensitive. Hydrated clusters, Br2•-•(H2O)n, show characteristic sharp features of O-H stretching bands of water in the small size clusters.

Node clustering for wireless sensor networks

International Nuclear Information System (INIS)

Bhatti, S.; Qureshi, I.A.; Memon, S.

2012-01-01

Recent years have witnessed considerable growth in the development and deployment of clustering methods which are not only used to maintain network resources but also increases the reliability of the WSNs (Wireless Sensor Network) and the facts manifest by the wide range of clustering solutions. Node clustering by selecting key parameters to tackle the dynamic behaviour of resource constraint WSN is a challenging issue. This paper highlights the recent progress which has been carried out pertaining to the development of clustering solutions for the WSNs. The paper presents classification of node clustering methods and their comparison based on the objectives, clustering criteria and methodology. In addition, the potential open issues which need to be considered for future work are high lighted. Keywords: Clustering, Sensor Network, Static, Dynamic

Ranked solutions to a class of combinatorial optimizations - with applications in mass spectrometry based peptide sequencing

Science.gov (United States)

Doerr, Timothy; Alves, Gelio; Yu, Yi-Kuo

2006-03-01

Typical combinatorial optimizations are NP-hard; however, for a particular class of cost functions the corresponding combinatorial optimizations can be solved in polynomial time. This suggests a way to efficiently find approximate solutions - - find a transformation that makes the cost function as similar as possible to that of the solvable class. After keeping many high-ranking solutions using the approximate cost function, one may then re-assess these solutions with the full cost function to find the best approximate solution. Under this approach, it is important to be able to assess the quality of the solutions obtained, e.g., by finding the true ranking of kth best approximate solution when all possible solutions are considered exhaustively. To tackle this statistical issue, we provide a systematic method starting with a scaling function generated from the fininte number of high- ranking solutions followed by a convergent iterative mapping. This method, useful in a variant of the directed paths in random media problem proposed here, can also provide a statistical significance assessment for one of the most important proteomic tasks - - peptide sequencing using tandem mass spectrometry data.

Simple and accurate solution for convective-radiative fin with temperature dependent thermal conductivity using double optimal linearization

International Nuclear Information System (INIS)

Bouaziz, M.N.; Aziz, Abdul

2010-01-01

A novel concept of double optimal linearization is introduced and used to obtain a simple and accurate solution for the temperature distribution in a straight rectangular convective-radiative fin with temperature dependent thermal conductivity. The solution is built from the classical solution for a pure convection fin of constant thermal conductivity which appears in terms of hyperbolic functions. When compared with the direct numerical solution, the double optimally linearized solution is found to be accurate within 4% for a range of radiation-conduction and thermal conductivity parameters that are likely to be encountered in practice. The present solution is simple and offers superior accuracy compared with the fairly complex approximate solutions based on the homotopy perturbation method, variational iteration method, and the double series regular perturbation method. The fin efficiency expression resembles the classical result for the constant thermal conductivity convecting fin. The present results are easily usable by the practicing engineers in their thermal design and analysis work involving fins.

The CMS online cluster: Setup, operation and maintenance of an evolving cluster

Energy Technology Data Exchange (ETDEWEB)

Coarasa, J.A.; et al.

2012-01-01

The CMS online cluster consists of more than 2700 computers running about 15000 application instances. These applications implement the necessary services to run the data acquisition of the CMS experiment. In this paper the IT solutions employed on the cluster are reviewed. Details are given on the adopted solutions which include the following topics: implementation of reduction and load balanced network and core IT services; deployment and configuration management infrastructure and its customization; a new monitoring infrastructure. Special emphasis will be put on the scalable approach allowing to increase the size of the cluster with no administration overhead. Finally, the lessons learnt from the two years of running will be presented.

Colloidal solutions of luminescent porous silicon clusters with different cluster sizes

Czech Academy of Sciences Publication Activity Database

Herynková, Kateřina; Podkorytov, E.; Šlechta, Miroslav; Cibulka, Ondřej; Leitner, J.; Pelant, Ivan

2014-01-01

Roč. 9, č. 1 (2014), 1-5 ISSN 1931-7573 Institutional support: RVO:68378271 Keywords : nanocrystalline silicon * porous silicon * cluster size * luminescent markers Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 2.524, year: 2012

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

Science.gov (United States)

Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

2018-05-03

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Directory of Open Access Journals (Sweden)

Farhan Aadil

2018-05-01

Full Text Available Flying ad-hoc networks (FANETs are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

The applications of small-angle X-ray scattering in studying nano-scaled polyoxometalate clusters in solutions

Science.gov (United States)

Li, Mu; Zhang, Mingxin; Wang, Weiyu; Cheng, Stephen Z. D.; Yin, Panchao

2018-05-01

Nano-scaled polyoxometalates (POMs) clusters with sizes ranging from 1 to 10 nm attract tremendous attention and have been extensively studied due to POMs' fascinating structural characteristics and prospects for wide-ranging applications. As a unique class of nanoparticles with well-defined structural topologies and monodispersed masses, the structures and properties of POMs in both bulk state and solutions have been explored with several well-developed protocols. Small-angle X-ray scattering (SAXS) technique, as a powerful tool for studying polymers and nanoparticles, has been recently extended to the investigating of solution behaviors of POMs. In this mini-review, the general principle and typical experimental procedures of SAXS are illustrated first. The applications of SAXS in characterizing POMs' morphology, counterion distribution around POMs, and short-range interactions among POMs in solutions are highlighted. [Figure not available: see fulltext.

OPTIMIZING ENERGY CONSUMPTION IN VEHICULAR SENSOR NETWORKS BY CLUSTERING USING FUZZY C-MEANS AND FUZZY SUBTRACTIVE ALGORITHMS

Directory of Open Access Journals (Sweden)

A. Ebrahimi

2017-09-01

Full Text Available Traffic monitoring and managing in urban intelligent transportation systems (ITS can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs; moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH, and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.

Data Clustering on Breast Cancer Data Using Firefly Algorithm with Golden Ratio Method

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DEMIR, M.

2015-05-01

Full Text Available Heuristic methods are problem solving methods. In general, they obtain near-optimal solutions, and they do not take the care of provability of this case. The heuristic methods do not guarantee to obtain the optimal results; however, they guarantee to obtain near-optimal solutions in considerable time. In this paper, an application was performed by using firefly algorithm - one of the heuristic methods. The golden ratio was applied to different steps of firefly algorithm and different parameters of firefly algorithm to develop a new algorithm - called Firefly Algorithm with Golden Ratio (FAGR. It was shown that the golden ratio made firefly algorithm be superior to the firefly algorithm without golden ratio. At this aim, the developed algorithm was applied to WBCD database (breast cancer database to cluster data obtained from breast cancer patients. The highest obtained success rate among all executions is 96% and the highest obtained average success rate in all executions is 94.5%.

Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders

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Simona-Vasilica Oprea

2018-01-01

Full Text Available Although in 2012 the European Union (EU has promoted energy efficiency in order to ensure a gradual 20% reduction of energy consumption by 2020, its targets related to energy efficiency have increased and extended to new time horizons. Therefore, in 2016, a new proposal for 2030 of energy efficiency target of 30% has been agreed. However, during the last years, even if the electricity consumption by households decreased in the EU-28, the largest expansion was recorded in Romania. Taking into account that the projected consumption peak is increasing and energy consumption management for residential activities is an important measure for energy efficiency improvement since its ratio from total consumption can be around 25–30%, in this paper, we propose an informatics solution that assists both electricity suppliers/grid operators and consumers. It includes three models for electricity consumption optimization, profiles, clustering and forecast. By this solution, the daily operation of appliances can be optimized and scheduled to minimize the consumption peak and reduce the stress on the grid. For optimization purpose, we propose three algorithms for shifting the operation of the programmable appliances from peak to off-peak hours. This approach enables the supplier to apply attractive time-of-use tariffs due to the fact that by flattening the consumption peak, it becomes more predictable, and thus improves the strategies on the electricity markets. According to the results of the optimization process, we compare the proposed algorithms emphasizing the benefits. For building consumption profiles, we develop a clustering algorithm based on self-organizing maps. By running the algorithm for three scenarios, well-delimited profiles are obtained. As for the consumption forecast, highly accurate feedforward artificial neural networks algorithm with backpropagation is implemented. Finally, we test these algorithms using several datasets showing their

Egocentric daily activity recognition via multitask clustering.

Science.gov (United States)

Yan, Yan; Ricci, Elisa; Liu, Gaowen; Sebe, Nicu

2015-10-01

Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human behavior from data collected with wearable cameras. First-person cameras continuously record several hours of their wearers' life. To cope with this vast amount of unlabeled and heterogeneous data, novel algorithmic solutions are required. In this paper, we propose a multitask clustering framework for activity of daily living analysis from visual data gathered from wearable cameras. Our intuition is that, even if the data are not annotated, it is possible to exploit the fact that the tasks of recognizing everyday activities of multiple individuals are related, since typically people perform the same actions in similar environments, e.g., people working in an office often read and write documents). In our framework, rather than clustering data from different users separately, we propose to look for clustering partitions which are coherent among related tasks. In particular, two novel multitask clustering algorithms, derived from a common optimization problem, are introduced. Our experimental evaluation, conducted both on synthetic data and on publicly available first-person vision data sets, shows that the proposed approach outperforms several single-task and multitask learning methods.

Optimal wavelength band clustering for multispectral iris recognition.

Science.gov (United States)

Gong, Yazhuo; Zhang, David; Shi, Pengfei; Yan, Jingqi

2012-07-01

This work explores the possibility of clustering spectral wavelengths based on the maximum dissimilarity of iris textures. The eventual goal is to determine how many bands of spectral wavelengths will be enough for iris multispectral fusion and to find these bands that will provide higher performance of iris multispectral recognition. A multispectral acquisition system was first designed for imaging the iris at narrow spectral bands in the range of 420 to 940 nm. Next, a set of 60 human iris images that correspond to the right and left eyes of 30 different subjects were acquired for an analysis. Finally, we determined that 3 clusters were enough to represent the 10 feature bands of spectral wavelengths using the agglomerative clustering based on two-dimensional principal component analysis. The experimental results suggest (1) the number, center, and composition of clusters of spectral wavelengths and (2) the higher performance of iris multispectral recognition based on a three wavelengths-bands fusion.

Optimally Stopped Optimization

Science.gov (United States)

Vinci, Walter; Lidar, Daniel

We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known, and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time, optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark the performance of a D-Wave 2X quantum annealer and the HFS solver, a specialized classical heuristic algorithm designed for low tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N = 1098 variables, the D-Wave device is between one to two orders of magnitude faster than the HFS solver.

Energy Aware Swarm Optimization with Intercluster Search for Wireless Sensor Network

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Shanmugasundaram Thilagavathi

2015-01-01

Full Text Available Wireless sensor networks (WSNs are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO algorithm with modified connected dominating set (CDS based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH. Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

Improving Spectral Image Classification through Band-Ratio Optimization and Pixel Clustering

Science.gov (United States)

O'Neill, M.; Burt, C.; McKenna, I.; Kimblin, C.

2017-12-01

The Underground Nuclear Explosion Signatures Experiment (UNESE) seeks to characterize non-prompt observables from underground nuclear explosions (UNE). As part of this effort, we evaluated the ability of DigitalGlobe's WorldView-3 (WV3) to detect and map UNE signatures. WV3 is the current state-of-the-art, commercial, multispectral imaging satellite; however, it has relatively limited spectral and spatial resolutions. These limitations impede image classifiers from detecting targets that are spatially small and lack distinct spectral features. In order to improve classification results, we developed custom algorithms to reduce false positive rates while increasing true positive rates via a band-ratio optimization and pixel clustering front-end. The clusters resulting from these algorithms were processed with standard spectral image classifiers such as Mixture-Tuned Matched Filter (MTMF) and Adaptive Coherence Estimator (ACE). WV3 and AVIRIS data of Cuprite, Nevada, were used as a validation data set. These data were processed with a standard classification approach using MTMF and ACE algorithms. They were also processed using the custom front-end prior to the standard approach. A comparison of the results shows that the custom front-end significantly increases the true positive rate and decreases the false positive rate.This work was done by National Security Technologies, LLC, under Contract No. DE-AC52-06NA25946 with the U.S. Department of Energy. DOE/NV/25946-3283.

Improved Solutions for the Optimal Coordination of DOCRs Using Firefly Algorithm

Directory of Open Access Journals (Sweden)

Muhammad Sulaiman

2018-01-01

Full Text Available Nature-inspired optimization techniques are useful tools in electrical engineering problems to minimize or maximize an objective function. In this paper, we use the firefly algorithm to improve the optimal solution for the problem of directional overcurrent relays (DOCRs. It is a complex and highly nonlinear constrained optimization problem. In this problem, we have two types of design variables, which are variables for plug settings (PSs and the time dial settings (TDSs for each relay in the circuit. The objective function is to minimize the total operating time of all the basic relays to avoid unnecessary delays. We have considered four models in this paper which are IEEE (3-bus, 4-bus, 6-bus, and 8-bus models. From the numerical results, it is obvious that the firefly algorithm with certain parameter settings performs better than the other state-of-the-art algorithms.

Clustering of resting state networks.

Directory of Open Access Journals (Sweden)

Megan H Lee

Full Text Available The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

Computation cluster for Monte Carlo calculations

Energy Technology Data Exchange (ETDEWEB)

Petriska, M.; Vitazek, K.; Farkas, G.; Stacho, M.; Michalek, S. [Dep. Of Nuclear Physics and Technology, Faculty of Electrical Engineering and Information, Technology, Slovak Technical University, Ilkovicova 3, 81219 Bratislava (Slovakia)

2010-07-01

Two computation clusters based on Rocks Clusters 5.1 Linux distribution with Intel Core Duo and Intel Core Quad based computers were made at the Department of the Nuclear Physics and Technology. Clusters were used for Monte Carlo calculations, specifically for MCNP calculations applied in Nuclear reactor core simulations. Optimization for computation speed was made on hardware and software basis. Hardware cluster parameters, such as size of the memory, network speed, CPU speed, number of processors per computation, number of processors in one computer were tested for shortening the calculation time. For software optimization, different Fortran compilers, MPI implementations and CPU multi-core libraries were tested. Finally computer cluster was used in finding the weighting functions of neutron ex-core detectors of VVER-440. (authors)

Computation cluster for Monte Carlo calculations

International Nuclear Information System (INIS)

Petriska, M.; Vitazek, K.; Farkas, G.; Stacho, M.; Michalek, S.

2010-01-01

Two computation clusters based on Rocks Clusters 5.1 Linux distribution with Intel Core Duo and Intel Core Quad based computers were made at the Department of the Nuclear Physics and Technology. Clusters were used for Monte Carlo calculations, specifically for MCNP calculations applied in Nuclear reactor core simulations. Optimization for computation speed was made on hardware and software basis. Hardware cluster parameters, such as size of the memory, network speed, CPU speed, number of processors per computation, number of processors in one computer were tested for shortening the calculation time. For software optimization, different Fortran compilers, MPI implementations and CPU multi-core libraries were tested. Finally computer cluster was used in finding the weighting functions of neutron ex-core detectors of VVER-440. (authors)

Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique

Science.gov (United States)

Bandaru, Sunith; Deb, Kalyanmoy

2011-09-01

In this article, a methodology is proposed for automatically extracting innovative design principles which make a system or process (subject to conflicting objectives) optimal using its Pareto-optimal dataset. Such 'higher knowledge' would not only help designers to execute the system better, but also enable them to predict how changes in one variable would affect other variables if the system has to retain its optimal behaviour. This in turn would help solve other similar systems with different parameter settings easily without the need to perform a fresh optimization task. The proposed methodology uses a clustering-based optimization technique and is capable of discovering hidden functional relationships between the variables, objective and constraint functions and any other function that the designer wishes to include as a 'basis function'. A number of engineering design problems are considered for which the mathematical structure of these explicit relationships exists and has been revealed by a previous study. A comparison with the multivariate adaptive regression splines (MARS) approach reveals the practicality of the proposed approach due to its ability to find meaningful design principles. The success of this procedure for automated innovization is highly encouraging and indicates its suitability for further development in tackling more complex design scenarios.

Viscosity Solutions for a System of Integro-PDEs and Connections to Optimal Switching and Control of Jump-Diffusion Processes

International Nuclear Information System (INIS)

Biswas, Imran H.; Jakobsen, Espen R.; Karlsen, Kenneth H.

2010-01-01

We develop a viscosity solution theory for a system of nonlinear degenerate parabolic integro-partial differential equations (IPDEs) related to stochastic optimal switching and control problems or stochastic games. In the case of stochastic optimal switching and control, we prove via dynamic programming methods that the value function is a viscosity solution of the IPDEs. In our setting the value functions or the solutions of the IPDEs are not smooth, so classical verification theorems do not apply.

Bayesian Decision Theoretical Framework for Clustering

Science.gov (United States)

Chen, Mo

2011-01-01

In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…

A time-domain decomposition iterative method for the solution of distributed linear quadratic optimal control problems

Science.gov (United States)

Heinkenschloss, Matthias

2005-01-01

We study a class of time-domain decomposition-based methods for the numerical solution of large-scale linear quadratic optimal control problems. Our methods are based on a multiple shooting reformulation of the linear quadratic optimal control problem as a discrete-time optimal control (DTOC) problem. The optimality conditions for this DTOC problem lead to a linear block tridiagonal system. The diagonal blocks are invertible and are related to the original linear quadratic optimal control problem restricted to smaller time-subintervals. This motivates the application of block Gauss-Seidel (GS)-type methods for the solution of the block tridiagonal systems. Numerical experiments show that the spectral radii of the block GS iteration matrices are larger than one for typical applications, but that the eigenvalues of the iteration matrices decay to zero fast. Hence, while the GS method is not expected to convergence for typical applications, it can be effective as a preconditioner for Krylov-subspace methods. This is confirmed by our numerical tests.A byproduct of this research is the insight that certain instantaneous control techniques can be viewed as the application of one step of the forward block GS method applied to the DTOC optimality system.

Optimality Measures for Monotone Equivariant Cluster Techniques.

Science.gov (United States)

1980-09-01

complete linkage, u-clustering (u - .3, .5, .7), uv-clustering (uv = (.2,.4), (.2,.6), (.4,.6)) as well as the UPGMA algorithm. The idea will be to...Table 15. Notice that these measure-- do indeed pioduce difftxent verdicts. OPI rates UPGMA as best with uv = (.2,.4) R € second. By OP2, UPGMA is best...By OPI, UPGQA and uv = (.4,.6) are tied for first place, while by OP2, UPGMA is best with uv = (.2,.6), uv = (.2,.4) and uv = (.4,.6) close behind

OPTIMAL MODEL OF FUNCTIONING OF OLERICULTURE: VERTICAL INTEGRATION, AGRICULTURAL FILIERES, CLUSTERS

Directory of Open Access Journals (Sweden)

Y. B. Mindlin

2016-01-01

Full Text Available The goal of the present paper is to identify the optimal strategy of development of the Russian olericulture in order to substitute imported products and to build up logistic and transport infrastructure. Existing problems of the Russian olericulture are described. It is demonstrated that these problems can be solved on the basis of big integrated structures. Formation of these structures can be based on hierarchical (vertical  integration or networking (agricultural filieres or clusters models. A comparative analysis of these models of development of olericulture is made. Advantages and inconveniences of each model are described. It is demonstrated that sustainable development of the Russian olericulture can be insured only by a combination of hierarchical and networking tools. Vertical integration will help to reach quick increase of production, while networking models are necessary for inclusion of small producers into production chains, development of product range and development of supporting industries. Networking models are also necessary for social tasks. It means that the optimal strategy of development of the  Russian olericulture should be based on a combination of networking and hierarchical tools. This combination is necessary for agricultural corporation as well as for the Russian olericulture in general.

Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids

International Nuclear Information System (INIS)

El-Sharafy, M. Zaki; Farag, Hany E.Z.

2017-01-01

Highlights: •Introducing three types of energy transfer between neighboring microgrids. •Incorporating droop-based power flow model of islanded microgrids in restoration. •Optimizing droop settings of DGs in islanded mode to maximize restored load. •Decomposing restoration into two distributed constraint optimization problems. •Using OPTAPO to solve the formulated problems in a multiagent environment. -- Abstract: In this paper, an optimization problem is formulated for the automatic back-feed service restoration in smart distribution grids. The formulated problem relies on the structure of smart distribution grids, clustered into multi-microgrids, capable of operating in both grid-connected and islanded modes of operation. To that end, three types of power transfer between the neighboring microgrids, during the restoration processes are introduced: load transfer, distributed generation (DG) transfer, and combined load–DG transfer. The formulated optimization problem takes into account the ability of forming new, not predefined islanded microgrids, in the post-restoration configuration, to maximize service restoration. To obviate the need for a central unit, the optimization problem is reformulated, in this work, as a distributed constraint optimization problem, in which the variables and constraints are distributed among automated agents. To reduce the problem complexity, the restoration problem is decomposed into two sequential and interdependent distributed sub-problems: supply adequacy, and optimal reconfiguration. The proposed algorithm adopts the Optimal Asynchronous Partial Overlay (OPTAPO) technique, which is based on the distributed constraint agent search to solve distributed sub-problems in a multi-agent environment. Several case studies have been carried out to evaluate the effectiveness and robustness of the proposed algorithm.

Optimization of the spatial mesh for numerical solution of the neutron transport equation in a cluster-type lattice cell

International Nuclear Information System (INIS)

Davis, R.S.

2012-01-01

For programs that solve the neutron transport equation with an approximation that the neutron flux is constant in each space in a user-defined mesh, optimization of that mesh yields benefits in computing time and attainable precision. The previous best practice does not optimize the mesh thoroughly, because a large number of test runs of the solving software would be necessary. The method presented here optimizes the mesh for a flux that is based on conventional approximations but is more informative, so that a minimal number of parameters, one per type of material, must be adjusted by test runs to achieve thorough optimization. For a 37 element, natural-uranium, CANDU lattice cell, the present optimization yields 7 to 12 times (depending on the criterion) better precision than the previous best practice in 37% less computing time. (author)

A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering

Directory of Open Access Journals (Sweden)

Li Ma

2015-01-01

Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA. The proposed algorithm combines artificial fish swarm algorithm (AFSA with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM.

Optimal resource allocation solutions for heterogeneous cognitive radio networks

Directory of Open Access Journals (Sweden)

Babatunde Awoyemi

2017-05-01

Full Text Available Cognitive radio networks (CRN are currently gaining immense recognition as the most-likely next-generation wireless communication paradigm, because of their enticing promise of mitigating the spectrum scarcity and/or underutilisation challenge. Indisputably, for this promise to ever materialise, CRN must of necessity devise appropriate mechanisms to judiciously allocate their rather scarce or limited resources (spectrum and others among their numerous users. ‘Resource allocation (RA in CRN', which essentially describes mechanisms that can effectively and optimally carry out such allocation, so as to achieve the utmost for the network, has therefore recently become an important research focus. However, in most research works on RA in CRN, a highly significant factor that describes a more realistic and practical consideration of CRN has been ignored (or only partially explored, i.e., the aspect of the heterogeneity of CRN. To address this important aspect, in this paper, RA models that incorporate the most essential concepts of heterogeneity, as applicable to CRN, are developed and the imports of such inclusion in the overall networking are investigated. Furthermore, to fully explore the relevance and implications of the various heterogeneous classifications to the RA formulations, weights are attached to the different classes and their effects on the network performance are studied. In solving the developed complex RA problems for heterogeneous CRN, a solution approach that examines and exploits the structure of the problem in achieving a less-complex reformulation, is extensively employed. This approach, as the results presented show, makes it possible to obtain optimal solutions to the rather difficult RA problems of heterogeneous CRN.

Spike-layer solutions to nonlinear fractional Schrodinger equations with almost optimal nonlinearities

Directory of Open Access Journals (Sweden)

Jinmyoung Seok

2015-07-01

Full Text Available In this article, we are interested in singularly perturbed nonlinear elliptic problems involving a fractional Laplacian. Under a class of nonlinearity which is believed to be almost optimal, we construct a positive solution which exhibits multiple spikes near any given local minimum components of an exterior potential of the problem.

Closed-form solutions for linear regulator design of mechanical systems including optimal weighting matrix selection

Science.gov (United States)

Hanks, Brantley R.; Skelton, Robert E.

1991-01-01

Vibration in modern structural and mechanical systems can be reduced in amplitude by increasing stiffness, redistributing stiffness and mass, and/or adding damping if design techniques are available to do so. Linear Quadratic Regulator (LQR) theory in modern multivariable control design, attacks the general dissipative elastic system design problem in a global formulation. The optimal design, however, allows electronic connections and phase relations which are not physically practical or possible in passive structural-mechanical devices. The restriction of LQR solutions (to the Algebraic Riccati Equation) to design spaces which can be implemented as passive structural members and/or dampers is addressed. A general closed-form solution to the optimal free-decay control problem is presented which is tailored for structural-mechanical system. The solution includes, as subsets, special cases such as the Rayleigh Dissipation Function and total energy. Weighting matrix selection is a constrained choice among several parameters to obtain desired physical relationships. The closed-form solution is also applicable to active control design for systems where perfect, collocated actuator-sensor pairs exist.

Time Optimal Reachability Analysis Using Swarm Verification

DEFF Research Database (Denmark)

Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand

2016-01-01

Time optimal reachability analysis employs model-checking to compute goal states that can be reached from an initial state with a minimal accumulated time duration. The model-checker may produce a corresponding diagnostic trace which can be interpreted as a feasible schedule for many scheduling...... and planning problems, response time optimization etc. We propose swarm verification to accelerate time optimal reachability using the real-time model-checker Uppaal. In swarm verification, a large number of model checker instances execute in parallel on a computer cluster using different, typically randomized...... search strategies. We develop four swarm algorithms and evaluate them with four models in terms scalability, and time- and memory consumption. Three of these cooperate by exchanging costs of intermediate solutions to prune the search using a branch-and-bound approach. Our results show that swarm...

Finding reproducible cluster partitions for the k-means algorithm.

Science.gov (United States)

Lisboa, Paulo J G; Etchells, Terence A; Jarman, Ian H; Chambers, Simon J

2013-01-01

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

MAS-based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

DEFF Research Database (Denmark)

Han, Yang; Zhang, Ke; Hong, Li

2018-01-01

The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance...... the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized...... configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since...

Optimization and analysis of large chemical kinetic mechanisms using the solution mapping method - Combustion of methane

Science.gov (United States)

Frenklach, Michael; Wang, Hai; Rabinowitz, Martin J.

1992-01-01

A method of systematic optimization, solution mapping, as applied to a large-scale dynamic model is presented. The basis of the technique is parameterization of model responses in terms of model parameters by simple algebraic expressions. These expressions are obtained by computer experiments arranged in a factorial design. The developed parameterized responses are then used in a joint multiparameter multidata-set optimization. A brief review of the mathematical background of the technique is given. The concept of active parameters is discussed. The technique is applied to determine an optimum set of parameters for a methane combustion mechanism. Five independent responses - comprising ignition delay times, pre-ignition methyl radical concentration profiles, and laminar premixed flame velocities - were optimized with respect to thirteen reaction rate parameters. The numerical predictions of the optimized model are compared to those computed with several recent literature mechanisms. The utility of the solution mapping technique in situations where the optimum is not unique is also demonstrated.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

International Nuclear Information System (INIS)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong; Choi, Jae Ho

2009-01-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of); Choi, Jae Ho [Samsung Techwin Co., Ltd., Changwon (Korea, Republic of)

2009-07-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with {epsilon}-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

[Autism Spectrum Disorder and DSM-5: Spectrum or Cluster?].

Science.gov (United States)

Kienle, Xaver; Freiberger, Verena; Greulich, Heide; Blank, Rainer

2015-01-01

Within the new DSM-5, the currently differentiated subgroups of "Autistic Disorder" (299.0), "Asperger's Disorder" (299.80) and "Pervasive Developmental Disorder" (299.80) are replaced by the more general "Autism Spectrum Disorder". With regard to a patient-oriented and expedient advising therapy planning, however, the issue of an empirically reproducible and clinically feasible differentiation into subgroups must still be raised. Based on two Autism-rating-scales (ASDS and FSK), an exploratory two-step cluster analysis was conducted with N=103 children (age: 5-18) seen in our social-pediatric health care centre to examine potentially autistic symptoms. In the two-cluster solution of both rating scales, mainly the problems in social communication grouped the children into a cluster "with communication problems" (51 % and 41 %), and a cluster "without communication problems". Within the three-cluster solution of the ASDS, sensory hypersensitivity, cleaving to routines and social-communicative problems generated an "autistic" subgroup (22%). The children of the second cluster ("communication problems", 35%) were only described by social-communicative problems, and the third group did not show any problems (38%). In the three-cluster solution of the FSK, the "autistic cluster" of the two-cluster solution differentiated in a subgroup with mainly social-communicative problems (cluster 1) and a second subgroup described by restrictive, repetitive behavior. The different cluster solutions will be discussed with a view to the new DSM-5 diagnostic criteria, for following studies a further specification of some of the ASDS and FSK items could be helpful.

Evaluation of gene-expression clustering via mutual information distance measure

Directory of Open Access Journals (Sweden)

Maimon Oded

2007-03-01

Full Text Available Abstract Background The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compare different clustering solutions when using the Mutual Information (MI measure versus the use of the well known Euclidean distance and Pearson correlation coefficient. Results Relying on several public gene expression datasets, we evaluate the homogeneity and separation scores of different clustering solutions. It was found that the use of the MI measure yields a more significant differentiation among erroneous clustering solutions. The proposed measure was also used to analyze the performance of several known clustering algorithms. A comparative study of these algorithms reveals that their "best solutions" are ranked almost oppositely when using different distance measures, despite the found correspondence between these measures when analysing the averaged scores of groups of solutions. Conclusion In view of the results, further attention should be paid to the selection of a proper distance measure for analyzing the clustering of gene expression data.

Evolutionary design optimization of traffic signals applied to Quito city.

Science.gov (United States)

Armas, Rolando; Aguirre, Hernán; Daolio, Fabio; Tanaka, Kiyoshi

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.

A WEB-BASED SOLUTION TO VISUALIZE OPERATIONAL MONITORING LINUX CLUSTER FOR THE PROTODUNE DATA QUALITY MONITORING CLUSTER

CERN Document Server

Mosesane, Badisa

2017-01-01

The Neutrino computing cluster made of 300 Dell PowerEdge 1950 U1 nodes serves an integral role to the CERN Neutrino Platform (CENF). It represents an effort to foster fundamental research in the field of Neutrino physics as it provides data processing facility. We cannot begin to over emphasize the need for data quality monitoring coupled with automating system configurations and remote monitoring of the cluster. To achieve these, a software stack has been chosen to implement automatic propagation of configurations across all the nodes in the cluster. The bulk of these discusses and delves more into the automated configuration management system on this cluster to enable the fast online data processing and Data Quality (DQM) process for the Neutrino Platform cluster (npcmp.cern.ch).

Modeling of the branching influence on liquid–liquid equilibrium of binary and ternary polymer solutions by lattice–cluster theory

International Nuclear Information System (INIS)

Browarzik, Dieter; Langenbach, Kai; Enders, Sabine; Browarzik, Christina

2013-01-01

Highlights: ► Liquid–liquid equilibrium (LLE) is calculated with the lattice–cluster theory (LCT). ► Equations of the LCT are reduced to only three geometrical parameters. ► Branching influence on the LLE is modeled for binary and ternary polymer solutions. ► Branched and linear solvents and polymers are compared in their influence on LLE. ► Solutions of branched polymers in branched solvents show the best miscibility. -- Abstract: The liquid–liquid equilibrium (LLE) of ternary model systems of the type solvent A + polymer B + solvent C is treated in the framework of lattice–cluster theory (LCT). There are a linear and a branched type of A-molecules as well as a linear and two types of strongly branched polymer molecules. The C-molecules are assumed to occupy only one lattice site. For nine binary and six ternary polymer solutions the branching influence on LLE is discussed. Currently, the LCT is the most useful model to take the architecture of the molecules into account. However, particularly for ternary systems the model is not comfortable because of the very numerous terms of the Gibbs energy. Using some relationships between the geometrical parameters of the model a considerable simplification is possible. In this paper the new and simpler equations of the LCT are presented. For comparison with experimental data critical temperatures of solutions of linear and branched polyethylene samples in diphenyl ether are calculated

Oil Reservoir Production Optimization using Optimal Control

DEFF Research Database (Denmark)

Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan

2011-01-01

Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%.......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...

clusters

Indian Academy of Sciences (India)

2017-09-27

Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Directory of Open Access Journals (Sweden)

Ion LUNGU

2012-01-01

Full Text Available In this paper, we research, analyze and develop optimization solutions for the parallel reduction function using graphics processing units (GPUs that implement the Compute Unified Device Architecture (CUDA, a modern and novel approach for improving the software performance of data processing applications and algorithms. Many of these applications and algorithms make use of the reduction function in their computational steps. After having designed the function and its algorithmic steps in CUDA, we have progressively developed and implemented optimization solutions for the reduction function. In order to confirm, test and evaluate the solutions' efficiency, we have developed a custom tailored benchmark suite. We have analyzed the obtained experimental results regarding: the comparison of the execution time and bandwidth when using graphic processing units covering the main CUDA architectures (Tesla GT200, Fermi GF100, Kepler GK104 and a central processing unit; the data type influence; the binary operator's influence.

Towards Development of Clustering Applications for Large-Scale Comparative Genotyping and Kinship Analysis Using Y-Short Tandem Repeats.

Science.gov (United States)

Seman, Ali; Sapawi, Azizian Mohd; Salleh, Mohd Zaki

2015-06-01

Y-chromosome short tandem repeats (Y-STRs) are genetic markers with practical applications in human identification. However, where mass identification is required (e.g., in the aftermath of disasters with significant fatalities), the efficiency of the process could be improved with new statistical approaches. Clustering applications are relatively new tools for large-scale comparative genotyping, and the k-Approximate Modal Haplotype (k-AMH), an efficient algorithm for clustering large-scale Y-STR data, represents a promising method for developing these tools. In this study we improved the k-AMH and produced three new algorithms: the Nk-AMH I (including a new initial cluster center selection), the Nk-AMH II (including a new dominant weighting value), and the Nk-AMH III (combining I and II). The Nk-AMH III was the superior algorithm, with mean clustering accuracy that increased in four out of six datasets and remained at 100% in the other two. Additionally, the Nk-AMH III achieved a 2% higher overall mean clustering accuracy score than the k-AMH, as well as optimal accuracy for all datasets (0.84-1.00). With inclusion of the two new methods, the Nk-AMH III produced an optimal solution for clustering Y-STR data; thus, the algorithm has potential for further development towards fully automatic clustering of any large-scale genotypic data.

A Clustering Based Approach for Observability and Controllability Analysis for Optimal Placement of PMU

Science.gov (United States)

Murthy, Ch; MIEEE; Mohanta, D. K.; SMIEE; Meher, Mahendra

2017-08-01

Continuous monitoring and control of the power system is essential for its healthy operation. This can be achieved by making the system observable as well as controllable. Many efforts have been made by several researchers to make the system observable by placing the Phasor Measurement Units (PMUs) at the optimal locations. But so far the idea of controllability with PMUs is not considered. This paper contributes how to check whether the system is controllable or not, if not then how make it controllable using a clustering approach. IEEE 14 bus system is considered to illustrate the concept of controllability.

Interplay between experiments and calculations for organometallic clusters and caged clusters

International Nuclear Information System (INIS)

Nakajima, Atsushi

2015-01-01

Clusters consisting of 10-1000 atoms exhibit size-dependent electronic and geometric properties. In particular, composite clusters consisting of several elements and/or components provide a promising way for a bottom-up approach for designing functional advanced materials, because the functionality of the composite clusters can be optimized not only by the cluster size but also by their compositions. In the formation of composite clusters, their geometric symmetry and dimensionality are emphasized to control the physical and chemical properties, because selective and anisotropic enhancements for optical, chemical, and magnetic properties can be expected. Organometallic clusters and caged clusters are demonstrated as a representative example of designing the functionality of the composite clusters. Organometallic vanadium-benzene forms a one dimensional sandwich structure showing ferromagnetic behaviors and anomalously large HOMO-LUMO gap differences of two spin orbitals, which can be regarded as spin-filter components for cluster-based spintronic devices. Caged clusters of aluminum (Al) are well stabilized both geometrically and electronically at Al 12 X, behaving as a “superatom”

The Development Of Red Chili Agribusiness Cluster With Soft System Methodology (Ssm Approach In Garut, West Java

Directory of Open Access Journals (Sweden)

Sri Ayu Andayani

2016-12-01

Full Text Available Red chili is one of the commodities with high price fluctuation and gives influence to inflation. It happens due to the unsustainable supply of red chili from the central production centers to the market. Bank Indonesia (central bank initiates a cluster system to support price controlling and regional economic growth. In this regard, the study is conducted  in Garut regency, which is one of the centers of red chili plantation in West Java and uses as cluster development, and yet there are still many obstacles along the way. This paper has the objective to describe the problem which causes unsustainable production and affects industrial supplies systemically and also to analyze the existing partnerships in order to maintain the continuity of supply as an alternative solution.This study was designed qualitatively with case study method through a system approach namely soft system methodology (SSM. The results shows that the problems in the cluster of red chili are ranging from production planning to the delay of sales payment process which systemically interlinked and the collaboration of executors that have not been optimally implemented. This study offers solution for those problems accordance with change formulation of SSM and industrial emphasis on fairness, transparency and integrated optimization with the principle of production sustainability from all stakeholders through participative collaboration to maintain continuity of production.

Shape optimization in 2D contact problems with given friction and a solution-dependent coefficient of friction

Czech Academy of Sciences Publication Activity Database

Haslinger, J.; Outrata, Jiří; Pathó, R.

2012-01-01

Roč. 20, č. 1 (2012), s. 31-59 ISSN 1877-0533 R&D Projects: GA AV ČR IAA100750802 Institutional research plan: CEZ:AV0Z10750506 Institutional support: RVO:67985556 Keywords : shape optimization * Signorini problem * model with given frinction * solution-dependent coefficient of friction * mathematical probrams with equilibrium constraints Subject RIV: BA - General Mathematics Impact factor: 1.036, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/outrata-shape optimization in 2d contact problems with given friction and a solution-dependent coefficient of friction .pdf

Cluster growth kinetics

International Nuclear Information System (INIS)

Dubovik, V.M.; Gal'perin, A.G.; Rikhvitskij, V.S.; Lushnikov, A.A.

2000-01-01

Processes of some traffic blocking coming into existence are considered as probabilistic ones. We study analytic solutions for models for the dynamics of both cluster growth and cluster growth with fragmentation in the systems of finite number of objects. Assuming rates constancy of both coalescence and fragmentation, the models under consideration are linear on the probability functions

Diskless Linux Cluster How-To

National Research Council Canada - National Science Library

Shumaker, Justin L

2005-01-01

Diskless linux clustering is not yet a turn-key solution. The process of configuring a cluster of diskless linux machines requires many modifications to the stock linux operating system before they can boot cleanly...

Do protein crystals nucleate within dense liquid clusters?

International Nuclear Information System (INIS)

Maes, Dominique; Vorontsova, Maria A.; Potenza, Marco A. C.; Sanvito, Tiziano; Sleutel, Mike; Giglio, Marzio; Vekilov, Peter G.

2015-01-01

The evolution of protein-rich clusters and nucleating crystals were characterized by dynamic light scattering (DLS), confocal depolarized dynamic light scattering (cDDLS) and depolarized oblique illumination dark-field microscopy. Newly nucleated crystals within protein-rich clusters were detected directly. These observations indicate that the protein-rich clusters are locations for crystal nucleation. Protein-dense liquid clusters are regions of high protein concentration that have been observed in solutions of several proteins. The typical cluster size varies from several tens to several hundreds of nanometres and their volume fraction remains below 10 −3 of the solution. According to the two-step mechanism of nucleation, the protein-rich clusters serve as locations for and precursors to the nucleation of protein crystals. While the two-step mechanism explained several unusual features of protein crystal nucleation kinetics, a direct observation of its validity for protein crystals has been lacking. Here, two independent observations of crystal nucleation with the proteins lysozyme and glucose isomerase are discussed. Firstly, the evolutions of the protein-rich clusters and nucleating crystals were characterized simultaneously by dynamic light scattering (DLS) and confocal depolarized dynamic light scattering (cDDLS), respectively. It is demonstrated that protein crystals appear following a significant delay after cluster formation. The cDDLS correlation functions follow a Gaussian decay, indicative of nondiffusive motion. A possible explanation is that the crystals are contained inside large clusters and are driven by the elasticity of the cluster surface. Secondly, depolarized oblique illumination dark-field microscopy reveals the evolution from liquid clusters without crystals to newly nucleated crystals contained in the clusters to grown crystals freely diffusing in the solution. Collectively, the observations indicate that the protein-rich clusters in

A Holistic Concept to Design Optimal Water Supply Infrastructures for Informal Settlements Using Remote Sensing Data

Directory of Open Access Journals (Sweden)

Lea Rausch

2018-02-01

Full Text Available Ensuring access to water and sanitation for all is Goal No. 6 of the 17 UN Sustainability Development Goals to transform our world. As one step towards this goal, we present an approach that leverages remote sensing data to plan optimal water supply networks for informal urban settlements. The concept focuses on slums within large urban areas, which are often characterized by a lack of an appropriate water supply. We apply methods of mathematical optimization aiming to find a network describing the optimal supply infrastructure. Hereby, we choose between different decentral and central approaches combining supply by motorized vehicles with supply by pipe systems. For the purposes of illustration, we apply the approach to two small slum clusters in Dhaka and Dar es Salaam. We show our optimization results, which represent the lowest cost water supply systems possible. Additionally, we compare the optimal solutions of the two clusters (also for varying input parameters, such as population densities and slum size development over time and describe how the result of the optimization depends on the entered remote sensing data.

The Primary Experiments of an Analysis of Pareto Solutions for Conceptual Design Optimization Problem of Hybrid Rocket Engine

Science.gov (United States)

Kudo, Fumiya; Yoshikawa, Tomohiro; Furuhashi, Takeshi

Recentry, Multi-objective Genetic Algorithm, which is the application of Genetic Algorithm to Multi-objective Optimization Problems is focused on in the engineering design field. In this field, the analysis of design variables in the acquired Pareto solutions, which gives the designers useful knowledge in the applied problem, is important as well as the acquisition of advanced solutions. This paper proposes a new visualization method using Isomap which visualizes the geometric distances of solutions in the design variable space considering their distances in the objective space. The proposed method enables a user to analyze the design variables of the acquired solutions considering their relationship in the objective space. This paper applies the proposed method to the conceptual design optimization problem of hybrid rocket engine and studies the effectiveness of the proposed method.

Micro-scale NMR Experiments for Monitoring the Optimization of Membrane Protein Solutions for Structural Biology.

Science.gov (United States)

Horst, Reto; Wüthrich, Kurt

2015-07-20

Reconstitution of integral membrane proteins (IMP) in aqueous solutions of detergent micelles has been extensively used in structural biology, using either X-ray crystallography or NMR in solution. Further progress could be achieved by establishing a rational basis for the selection of detergent and buffer conditions, since the stringent bottleneck that slows down the structural biology of IMPs is the preparation of diffracting crystals or concentrated solutions of stable isotope labeled IMPs. Here, we describe procedures to monitor the quality of aqueous solutions of [ 2 H, 15 N]-labeled IMPs reconstituted in detergent micelles. This approach has been developed for studies of β-barrel IMPs, where it was successfully applied for numerous NMR structure determinations, and it has also been adapted for use with α-helical IMPs, in particular GPCRs, in guiding crystallization trials and optimizing samples for NMR studies (Horst et al ., 2013). 2D [ 15 N, 1 H]-correlation maps are used as "fingerprints" to assess the foldedness of the IMP in solution. For promising samples, these "inexpensive" data are then supplemented with measurements of the translational and rotational diffusion coefficients, which give information on the shape and size of the IMP/detergent mixed micelles. Using microcoil equipment for these NMR experiments enables data collection with only micrograms of protein and detergent. This makes serial screens of variable solution conditions viable, enabling the optimization of parameters such as the detergent concentration, sample temperature, pH and the composition of the buffer.

K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system.

Science.gov (United States)

Zhang, Junfeng; Chen, Wei; Gao, Mingyi; Shen, Gangxiang

2017-10-30

In this work, we proposed two k-means-clustering-based algorithms to mitigate the fiber nonlinearity for 64-quadrature amplitude modulation (64-QAM) signal, the training-sequence assisted k-means algorithm and the blind k-means algorithm. We experimentally demonstrated the proposed k-means-clustering-based fiber nonlinearity mitigation techniques in 75-Gb/s 64-QAM coherent optical communication system. The proposed algorithms have reduced clustering complexity and low data redundancy and they are able to quickly find appropriate initial centroids and select correctly the centroids of the clusters to obtain the global optimal solutions for large k value. We measured the bit-error-ratio (BER) performance of 64-QAM signal with different launched powers into the 50-km single mode fiber and the proposed techniques can greatly mitigate the signal impairments caused by the amplified spontaneous emission noise and the fiber Kerr nonlinearity and improve the BER performance.

Optimizing an Investment Solution in Conditions of Uncertainty and Risk as a Multicriterial Task

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Kotsyuba Oleksiy S.

2017-10-01

Full Text Available The article is concerned with the methodology for optimizing investment decisions in conditions of uncertainty and risk. The subject area of the study relates, first of all, to real investment. The problem of modeling an optimal investment solution is considered to be a multicriterial task. Also, the constructive part of the publication is based on the position that the multicriteriality of objectives of investment projecting is the result, first, of the complex nature of the category of economic attractiveness (efficiency of real investment, and secondly, of the need to take into account the risk factor, which is a vector measure, in the preparation of an investment solution. An attempt has been made to develop an instrumentarium to optimize investment decisions in a situation of uncertainty and the risk it engenders, based on the use of roll-up of the local criteria. As a result of its implementation, a model has been proposed, which has the advantage that it takes into account, to a greater extent than is the case for standardized roll-up options, the contensive and formal features of the local (detailed criteria.

An Ad-Hoc Initial Solution Heuristic for Metaheuristic Optimization of Energy Market Participation Portfolios

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Ricardo Faia

2017-06-01

Full Text Available The deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character. The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial. Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations.

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

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Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Studying the varied shapes of gold clusters by an elegant optimization algorithm that hybridizes the density functional tight-binding theory and the density functional theory

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Yen, Tsung-Wen; Lim, Thong-Leng; Yoon, Tiem-Leong; Lai, S. K.

2017-11-01

We combined a new parametrized density functional tight-binding (DFTB) theory (Fihey et al. 2015) with an unbiased modified basin hopping (MBH) optimization algorithm (Yen and Lai 2015) and applied it to calculate the lowest energy structures of Au clusters. From the calculated topologies and their conformational changes, we find that this DFTB/MBH method is a necessary procedure for a systematic study of the structural development of Au clusters but is somewhat insufficient for a quantitative study. As a result, we propose an extended hybridized algorithm. This improved algorithm proceeds in two steps. In the first step, the DFTB theory is employed to calculate the total energy of the cluster and this step (through running DFTB/MBH optimization for given Monte-Carlo steps) is meant to efficiently bring the Au cluster near to the region of the lowest energy minimum since the cluster as a whole has explicitly considered the interactions of valence electrons with ions, albeit semi-quantitatively. Then, in the second succeeding step, the energy-minimum searching process will continue with a skilledly replacement of the energy function calculated by the DFTB theory in the first step by one calculated in the full density functional theory (DFT). In these subsequent calculations, we couple the DFT energy also with the MBH strategy and proceed with the DFT/MBH optimization until the lowest energy value is found. We checked that this extended hybridized algorithm successfully predicts the twisted pyramidal structure for the Au40 cluster and correctly confirms also the linear shape of C8 which our previous DFTB/MBH method failed to do so. Perhaps more remarkable is the topological growth of Aun: it changes from a planar (n =3-11) → an oblate-like cage (n =12-15) → a hollow-shape cage (n =16-18) and finally a pyramidal-like cage (n =19, 20). These varied forms of the cluster's shapes are consistent with those reported in the literature.

Cluster models of aqueous Na{sup +} and Cl{sup -} in sea water/ice

Energy Technology Data Exchange (ETDEWEB)

Michelsen, R.; Walker, R. [Randolph-Macon College, Department of Chemistry (United States); Shillady, D., E-mail: quantummechanicsllc@msn.com [Virginia Commonwealth University, Department of Chemistry (United States)

2012-10-15

In this article, we present finite cluster models of aqueous solutes [NaCl(H{sub 2}O){sub 10}, NaCl(H{sub 2}O){sub 5}, and (H{sub 2}O){sub 6}] in terms of molecular geometry and vibrational spectra for interpretation of experimental infrared spectra of NaCl brine solutions. The quantum chemistry program GAMESS is used to optimize the model clusters to a local minimum energy gradient of less than 5.0d-6 hartrees/bohr with B3LYP in a gaussian basis of 6-31G(d,p). Harmonic frequencies are computed for comparison with the infrared spectra measured by attenuated total reflection of a temperature-controlled Ge plate under a layer of cold brine solution. The motivation for this research is to understand the mechanism by which freezing seawater excludes halide ions (mainly Cl{sup -}) and why the O-H stretching region of the spectra changes with temperature. Frost flowers, sea ice, and snow in marine environments contain concentrated halides in liquid brine at their surfaces which lead to catalytic destruction of low-altitude ozone in the polar regions of the Earth.

Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

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M. Amiri

2012-10-01

Full Text Available Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on Nasdaq from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods.

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

Optimization of soymilk, mango nectar and sucrose solution mixes for better quality of soymilk based beverage.

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Getu, Rahel; Tola, Yetenayet B; Neela, Satheesh

2017-01-01

Soy milk-based beverages play an important role as a healthy food alternative for human consumption. However, the ‘beany’ flavor and chalky mouth feel of soy milk often makes it unpalatable to consumers. The objective of the present study is to optimize a blend of soy milk, mango nectar and sucrose solution for the best quality soy milk-based beverage. This study was designed to develop a soy milk blended beverage, with mango nectar and sucrose solutions, with the best physicochemical and sensory properties. Fourteen combinations of formulations were determined by D-optimal mixture simplex lattice design, by using Design expert. The blended beverages were prepared by mixing the three basic ingredients with the range of 60−100% soy milk, 0–25% mango nectar and 0–15% sucrose solution. The prepared blended beverage was analyzed for selected physicochemical and sensory properties. The statistical significance of the terms in the regression equations were examined by Analysis of Variance (ANOVA) for each response and the significance test level was set at 5% (p nectar and sucrose solution increased, total color change, total soluble solid, gross energy, titratable acidity, and beta-carotene contents increased but with a decrease in moisture , ash, protein, ether extract, minerals and phytic acid contents was observed. Fi- nally, numerical optimization determined that 81% soy milk, 16% Mango nectar and 3% sugar solution will give by a soy milk blended beverage with the best physicochemical and sensory properties, with a desirability of 0.564. Blending soy milk with fruit juice such as mango is beneficial, as it improves sensory as well as selected nutritional parameters.

A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

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Lizhi Cui

2014-01-01

Full Text Available This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO, for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1 the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2 the GRCM-PSO method is able to handle the real HPLC-DAD data set.

MATHEMATICAL SOLUTIONS FOR OPTIMAL DIMENSIONING OF NUMBER AND HEIGHTS OF SOME HYDROTECHNIQUE ON TORRENTIAL FORMATION

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Nicolae Petrescu

2010-01-01

Full Text Available This paper is intended to achieve a mathematical model for the optimal dimensioning of number and heights of somedams/thresholds during a downpour, a decrease of water flow rate being obtained and by the solid material depositionsbehind the constructions a new smaller slope of the valley that changes the torrential nature that had before theconstruction is obtained.The choice of the dam and its characteristic dimensions may be an optimization issue and the location of dams on thetorrential (rainfall aspect is dictated by the capabilities of the foundation and restraint so that the chosen solutions willhave to comply with these sites. Finally, the choice of optimal solution to limit torrential (rainfall aspect will be basedon a calculation where the number of thresholds / dams can be a variable related to this, their height properly varying.The calculation method presented is an attempt to demonstrate the multiple opportunities available to implement atechnical issue solving conditions offered by the mathematics against soil erosion, which now is currently very topicalon the environmental protection.

Optimal solutions for a bio mathematical model for the evolution of smoking habit

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Sikander, Waseem; Khan, Umar; Ahmed, Naveed; Mohyud-Din, Syed Tauseef

In this study, we apply Variation of Parameter Method (VPM) coupled with an auxiliary parameter to obtain the approximate solutions for the epidemic model for the evolution of smoking habit in a constant population. Convergence of the developed algorithm, namely VPM with an auxiliary parameter is studied. Furthermore, a simple way is considered for obtaining an optimal value of auxiliary parameter via minimizing the total residual error over the domain of problem. Comparison of the obtained results with standard VPM shows that an auxiliary parameter is very feasible and reliable in controlling the convergence of approximate solutions.

General solution of undersampling frequency conversion and its optimization for parallel photodisplacement imaging.

Science.gov (United States)

Nakata, Toshihiko; Ninomiya, Takanori

2006-10-10

A general solution of undersampling frequency conversion and its optimization for parallel photodisplacement imaging is presented. Phase-modulated heterodyne interference light generated by a linear region of periodic displacement is captured by a charge-coupled device image sensor, in which the interference light is sampled at a sampling rate lower than the Nyquist frequency. The frequencies of the components of the light, such as the sideband and carrier (which include photodisplacement and topography information, respectively), are downconverted and sampled simultaneously based on the integration and sampling effects of the sensor. A general solution of frequency and amplitude in this downconversion is derived by Fourier analysis of the sampling procedure. The optimal frequency condition for the heterodyne beat signal, modulation signal, and sensor gate pulse is derived such that undesirable components are eliminated and each information component is converted into an orthogonal function, allowing each to be discretely reproduced from the Fourier coefficients. The optimal frequency parameters that maximize the sideband-to-carrier amplitude ratio are determined, theoretically demonstrating its high selectivity over 80 dB. Preliminary experiments demonstrate that this technique is capable of simultaneous imaging of reflectivity, topography, and photodisplacement for the detection of subsurface lattice defects at a speed corresponding to an acquisition time of only 0.26 s per 256 x 256 pixel area.

Reload pattern optimization by application of multiple cyclic interchange algorithms

Energy Technology Data Exchange (ETDEWEB)

Geemert, R. van; Quist, A.J.; Hoogenboom, J.E. [Technische Univ. Delft (Netherlands)

1996-09-01

Reload pattern optimization procedures are proposed which are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the `elite` cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic tests for the number of fuel bundles involved in a cyclic interchange. Equilibrium cycle optimization results are reported for a test PWR reactor core of modest size. (author)

Reload pattern optimization by application of multiple cyclic interchange algorithms

International Nuclear Information System (INIS)

Geemert, R. van; Quist, A.J.; Hoogenboom, J.E.

1996-01-01

Reload pattern optimization procedures are proposed which are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the 'elite' cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic tests for the number of fuel bundles involved in a cyclic interchange. Equilibrium cycle optimization results are reported for a test PWR reactor core of modest size. (author)

Localization and orientation of heavy-atom cluster compounds in protein crystals using molecular replacement.

Science.gov (United States)

Dahms, Sven O; Kuester, Miriam; Streb, Carsten; Roth, Christian; Sträter, Norbert; Than, Manuel E

2013-02-01

Heavy-atom clusters (HA clusters) containing a large number of specifically arranged electron-dense scatterers are especially useful for experimental phase determination of large complex structures, weakly diffracting crystals or structures with large unit cells. Often, the determination of the exact orientation of the HA cluster and hence of the individual heavy-atom positions proves to be the critical step in successful phasing and subsequent structure solution. Here, it is demonstrated that molecular replacement (MR) with either anomalous or isomorphous differences is a useful strategy for the correct placement of HA cluster compounds. The polyoxometallate cluster hexasodium α-metatungstate (HMT) was applied in phasing the structure of death receptor 6. Even though the HA cluster is bound in alternate partially occupied orientations and is located at a special position, its correct localization and orientation could be determined at resolutions as low as 4.9 Å. The broad applicability of this approach was demonstrated for five different derivative crystals that included the compounds tantalum tetradecabromide and trisodium phosphotungstate in addition to HMT. The correct placement of the HA cluster depends on the length of the intramolecular vectors chosen for MR, such that both a larger cluster size and the optimal choice of the wavelength used for anomalous data collection strongly affect the outcome.

Formation, spectroscopic characterization, and solution stability of an [Fe4S4]2+ cluster derived from β-cyclodextrin dithiolate.

Science.gov (United States)

Lo, Wayne; Zhang, Ping; Ling, Chang-Chun; Huang, Shaw; Holm, R H

2012-09-17

The formation and solution properties, including stability in mixed aqueous-Me(2)SO media, have been investigated for an [Fe(4)S(4)](2+) cluster derived from β-cyclodextrin (CD) dithiolate. Clusters of the type [Fe(4)S(4)(SAr)(4)](2-) (Ar = Ph, C(6)H(4)-3-F) are generated in Me(2)SO by redox reactions of [Fe(4)S(4)(SEt)(4)](2-) with 2 equiv of ArSSAr. An analogous reaction with the intramolecular disulfide of 6(A),6(D)-(3-NHCOC(6)H(4)-1-SH)(2)-6(A),6(D)-dideoxy-β-cyclodextrin (14), whose synthesis is described, affords a completely substituted cluster formulated as [Fe(4)S(4){β-CD-(1,3-NHCOC(6)H(4)S)(2)}(2)](2-) (15). Ligand binding is indicated by a circular dichroism spectrum and also by UV-visible and isotropically shifted (1)H NMR spectra and redox behavior convincingly similar to [Fe(4)S(4)(SPh)(4)](2-). One formulation of 15 is a single cluster to which two dithiolates are bound, each in bidentate coordination. With there being no proven precedent for this binding mode, we show that the cluster [Fe(4)S(4)(S(2)-m-xyl)(2)](2-) is a single cubane whose m-xylyldithiolate ligands are bound in a bidentate arrangement. This same structure type was proposed for a cluster formulated as [Fe(4)S(4){β-CD-(1,3-SC(6)H(4)S)(2)}(2)](2-) (16; Kuroda et al. J. Am. Chem. Soc.1988, 110, 4049-4050) and reported to be water-stable. Clusters 15 and 16 are derived from similar ligands differing only in the spacer group between the thiolate binding site and the CD platform. In our search for clusters stable in aqueous or organic-aqueous mixed solvents that are potential candidates for the reconstitution of scaffold proteins implicated in cluster biogenesis, 15 is the most stable cluster that we have thus far encountered under anaerobic conditions in the absence of added ligand.

Methanol Synthesis: Optimal Solution for a Better Efficiency of the Process

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Grazia Leonzio

2018-02-01

Full Text Available In this research, an ANOVA analysis and a response surface methodology are applied to analyze the equilibrium of methanol reaction from pure carbon dioxide and hydrogen. In the ANOVA analysis, carbon monoxide composition in the feed, reaction temperature, recycle and water removal through a zeolite membrane are the analyzed factors. Carbon conversion, methanol yield, methanol productivity and methanol selectivity are the analyzed responses. Results show that main factors have the same effect on responses and a common significant interaction is not present. Carbon monoxide composition and water removal have a positive effect, while temperature and recycle have a negative effect on the system. From central composite design, an optimal solution is found in order to overcome thermodynamic limit: the reactor works with a membrane at lower temperature with carbon monoxide composition in the feed equal to 10 mol % and without recycle. In these conditions, carbon conversion, methanol yield, methanol selectivity, and methanol production are, respectively, higher than 60%, higher than 60%, between 90% and 95% and higher than 0.15 mol/h when considering a feed flow rate of 1 mol/h. A comparison with a traditional reactor is also developed: the membrane reactor ensures to have a carbon conversion higher of the 29% and a methanol yield higher of the 34%. Future researches should evaluate an economic analysis about the optimal solution.

clues: An R Package for Nonparametric Clustering Based on Local Shrinking

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Fang Chang

2010-02-01

Full Text Available Determining the optimal number of clusters appears to be a persistent and controversial issue in cluster analysis. Most existing R packages targeting clustering require the user to specify the number of clusters in advance. However, if this subjectively chosen number is far from optimal, clustering may produce seriously misleading results. In order to address this vexing problem, we develop the R package clues to automate and evaluate the selection of an optimal number of clusters, which is widely applicable in the field of clustering analysis. Package clues uses two main procedures, shrinking and partitioning, to estimate an optimal number of clusters by maximizing an index function, either the CH index or the Silhouette index, rather than relying on guessing a pre-specified number. Five agreement indices (Rand index, Hubert and Arabie’s adjusted Rand index, Morey and Agresti’s adjusted Rand index, Fowlkes and Mallows index and Jaccard index, which measure the degree of agreement between any two partitions, are also provided in clues. In addition to numerical evidence, clues also supplies a deeper insight into the partitioning process with trajectory plots.

On hierarchical solutions to the BBGKY hierarchy

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Hamilton, A. J. S.

1988-01-01

It is thought that the gravitational clustering of galaxies in the universe may approach a scale-invariant, hierarchical form in the small separation, large-clustering regime. Past attempts to solve the Born-Bogoliubov-Green-Kirkwood-Yvon (BBGKY) hierarchy in this regime have assumed a certain separable hierarchical form for the higher order correlation functions of galaxies in phase space. It is shown here that such separable solutions to the BBGKY equations must satisfy the condition that the clustered component of the solution has cluster-cluster correlations equal to galaxy-galaxy correlations to all orders. The solutions also admit the presence of an arbitrary unclustered component, which plays no dyamical role in the large-clustering regime. These results are a particular property of the specific separable model assumed for the correlation functions in phase space, not an intrinsic property of spatially hierarchical solutions to the BBGKY hierarchy. The observed distribution of galaxies does not satisfy the required conditions. The disagreement between theory and observation may be traced, at least in part, to initial conditions which, if Gaussian, already have cluster correlations greater than galaxy correlations.

Anti-predatory particle swarm optimization: Solution to nonconvex economic dispatch problems

Energy Technology Data Exchange (ETDEWEB)

Selvakumar, A. Immanuel [Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamilnadu (India); Thanushkodi, K. [Department of Electronics and Instrumentation Engineering, Government College of Technology, Coimbatore 641013, Tamilnadu (India)

2008-01-15

This paper proposes a new particle swarm optimization (PSO) strategy namely, anti-predatory particle swarm optimization (APSO) to solve nonconvex economic dispatch problems. In the classical PSO, the movement of a particle (bird) is governed by three behaviors: inertial, cognitive and social. The cognitive and social behaviors are the components of the foraging activity, which help the swarm of birds to locate food. Another activity that is observed in birds is the anti-predatory nature, which helps the swarm to escape from the predators. In this work, the anti-predatory activity is modeled and embedded in the classical PSO to form APSO. This inclusion enhances the exploration capability of the swarm. To validate the proposed APSO model, it is applied to two test systems having nonconvex solution spaces. Satisfactory results are obtained when compared with previous approaches. (author)

Closed-form solutions for linear regulator-design of mechanical systems including optimal weighting matrix selection

Science.gov (United States)

Hanks, Brantley R.; Skelton, Robert E.

1991-01-01

This paper addresses the restriction of Linear Quadratic Regulator (LQR) solutions to the algebraic Riccati Equation to design spaces which can be implemented as passive structural members and/or dampers. A general closed-form solution to the optimal free-decay control problem is presented which is tailored for structural-mechanical systems. The solution includes, as subsets, special cases such as the Rayleigh Dissipation Function and total energy. Weighting matrix selection is a constrained choice among several parameters to obtain desired physical relationships. The closed-form solution is also applicable to active control design for systems where perfect, collocated actuator-sensor pairs exist. Some examples of simple spring mass systems are shown to illustrate key points.

Optimal Analytical Solution for a Capacitive Wireless Power Transfer System with One Transmitter and Two Receivers

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Ben Minnaert

2017-09-01

Full Text Available Wireless power transfer from one transmitter to multiple receivers through inductive coupling is slowly entering the market. However, for certain applications, capacitive wireless power transfer (CWPT using electric coupling might be preferable. In this work, we determine closed-form expressions for a CWPT system with one transmitter and two receivers. We determine the optimal solution for two design requirements: (i maximum power transfer, and (ii maximum system efficiency. We derive the optimal loads and provide the analytical expressions for the efficiency and power. We show that the optimal load conductances for the maximum power configuration are always larger than for the maximum efficiency configuration. Furthermore, it is demonstrated that if the receivers are coupled, this can be compensated for by introducing susceptances that have the same value for both configurations. Finally, we numerically verify our results. We illustrate the similarities to the inductive wireless power transfer (IWPT solution and find that the same, but dual, expressions apply.

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

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Sealfon Stuart C

2010-10-01

Full Text Available Abstract Background There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments. Results To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 106 data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment. Conclusions

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

Science.gov (United States)

Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

2017-07-01

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

Cluster Optimization and Parallelization of Simulations with Dynamically Adaptive Grids

KAUST Repository

Schreiber, Martin; Weinzierl, Tobias; Bungartz, Hans-Joachim

2013-01-01

The present paper studies solvers for partial differential equations that work on dynamically adaptive grids stemming from spacetrees. Due to the underlying tree formalism, such grids efficiently can be decomposed into connected grid regions (clusters) on-the-fly. A graph on those clusters classified according to their grid invariancy, workload, multi-core affinity, and further meta data represents the inter-cluster communication. While stationary clusters already can be handled more efficiently than their dynamic counterparts, we propose to treat them as atomic grid entities and introduce a skip mechanism that allows the grid traversal to omit those regions completely. The communication graph ensures that the cluster data nevertheless are kept consistent, and several shared memory parallelization strategies are feasible. A hyperbolic benchmark that has to remesh selected mesh regions iteratively to preserve conforming tessellations acts as benchmark for the present work. We discuss runtime improvements resulting from the skip mechanism and the implications on shared memory performance and load balancing. © 2013 Springer-Verlag.

Approximate analytical solution of diffusion equation with fractional time derivative using optimal homotopy analysis method

Directory of Open Access Journals (Sweden)

S. Das

2013-12-01

Full Text Available In this article, optimal homotopy-analysis method is used to obtain approximate analytic solution of the time-fractional diffusion equation with a given initial condition. The fractional derivatives are considered in the Caputo sense. Unlike usual Homotopy analysis method, this method contains at the most three convergence control parameters which describe the faster convergence of the solution. Effects of parameters on the convergence of the approximate series solution by minimizing the averaged residual error with the proper choices of parameters are calculated numerically and presented through graphs and tables for different particular cases.

Radiation-induced segregation on defect clusters in single-phase concentrated solid-solution alloys

International Nuclear Information System (INIS)

Lu, Chenyang; Yang, Taini; Jin, Ke; Gao, Ning; Xiu, Pengyuan; Zhang, Yanwen; Gao, Fei; Bei, Hongbin; Weber, William J.; Sun, Kai; Dong, Yan; Wang, Lumin

2017-01-01

A group of single-phase concentrated solid-solution alloys (SP-CSAs), including NiFe, NiCoFe, NiCoFeCr, as well as a high entropy alloy NiCoFeCrMn, was irradiated with 3 MeV Ni"2"+ ions at 773 K to a fluence of 5 × 10"1"6 ions/cm"2 for the study of radiation response with increasing compositional complexity. Advanced transmission electron microscopy (TEM) with electron energy loss spectroscopy (EELS) was used to characterize the dislocation loop distribution and radiation-induced segregation (RIS) on defect clusters in the SP-CSAs. The results show that a higher fraction of faulted loops exists in the more compositionally complex alloys, which indicate that increasing compositional complexity can extend the incubation period and delay loop growth. The RIS behaviors of each element in the SP-CSAs were observed as follows: Ni and Co tend to enrich, but Cr, Fe and Mn prefer to deplete near the defect clusters. RIS level can be significantly suppressed by increasing compositional complexity due to the sluggish atom diffusion. According to molecular static (MS) simulations, “disk” like segregations may form near the faulted dislocation loops in the SP-CSAs. Segregated elements tend to distribute around the whole faulted loop as a disk rather than only around the edge of the loop.

Design of Distributed Controllers Seeking Optimal Power Flow Solutions Under Communication Constraints

Energy Technology Data Exchange (ETDEWEB)

Dall' Anese, Emiliano; Simonetto, Andrea; Dhople, Sairaj

2016-12-29

This paper focuses on power distribution networks featuring inverter-interfaced distributed energy resources (DERs), and develops feedback controllers that drive the DER output powers to solutions of time-varying AC optimal power flow (OPF) problems. Control synthesis is grounded on primal-dual-type methods for regularized Lagrangian functions, as well as linear approximations of the AC power-flow equations. Convergence and OPF-solution-tracking capabilities are established while acknowledging: i) communication-packet losses, and ii) partial updates of control signals. The latter case is particularly relevant since it enables asynchronous operation of the controllers where DER setpoints are updated at a fast time scale based on local voltage measurements, and information on the network state is utilized if and when available, based on communication constraints. As an application, the paper considers distribution systems with high photovoltaic integration, and demonstrates that the proposed framework provides fast voltage-regulation capabilities, while enabling the near real-time pursuit of solutions of AC OPF problems.

Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

Science.gov (United States)

Hoomod, Haider K.; Kareem Jebur, Tuka

2018-05-01

Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

Optimized high energy resolution in γ-ray spectroscopy with AGATA triple cluster detectors

Energy Technology Data Exchange (ETDEWEB)

Wiens, Andreas

2011-06-20

The AGATA demonstrator consists of five AGATA Triple Cluster (ATC) detectors. Each triple cluster detector contains three asymmetric, 36-fold segmented, encapsulated high purity germanium detectors. The purpose of the demonstrator is to show the feasibility of position-dependent γ-ray detection by means of γ-ray tracking, which is based on pulse shape analysis. The thesis describes the first optimization procedure of the first triple cluster detectors. Here, a high signal quality is mandatory for the energy resolution and the pulse shape analysis. The signal quality was optimized and the energy resolution was improved through the modification of the electronic properties, of the grounding scheme of the detector in particular. The first part of the work was the successful installation of the first four triple cluster detectors at INFN (National Institute of Nuclear Physics) in Legnaro, Italy, in the demonstrator frame prior to the AGATA commissioning experiments and the first physics campaign. The four ATC detectors combine 444 high resolution spectroscopy channels. This number combined with a high density were achieved for the first time for in-beam γ-ray spectroscopy experiments. The high quality of the ATC detectors is characterized by the average energy resolutions achieved for the segments of each crystal in the range of 1.943 and 2.131 keV at a γ-ray energy of 1.33 MeV for the first 12 crystals. The crosstalk level between individual detectors in the ATC is negligible. The crosstalk within one crystal is at a level of 10{sup -3}. In the second part of the work new methods for enhanced energy resolution in highly segmented and position sensitive detectors were developed. The signal-to-noise ratio was improved through averaging of the core and the segment signals, which led to an improvement of the energy resolution of 21% for γ-energies of 60 keV to a FWHM of 870 eV. In combination with crosstalk correction, a clearly improved energy resolution was

Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm

OpenAIRE

Ahmet Demir; Utku kose

2017-01-01

In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an...

Personalized PageRank Clustering: A graph clustering algorithm based on random walks

Science.gov (United States)

A. Tabrizi, Shayan; Shakery, Azadeh; Asadpour, Masoud; Abbasi, Maziar; Tavallaie, Mohammad Ali

2013-11-01

Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.

A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Jing Yang

2010-05-01

Full Text Available For monitoring burst events in a kind of reactive wireless sensor networks (WSNs, a multipath routing protocol (MRP based on dynamic clustering and ant colony optimization (ACO is proposed.. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

Study of Research and Development Processes through Fuzzy Super FRM Model and Optimization Solutions

Directory of Open Access Journals (Sweden)

Flavius Aurelian Sârbu

2015-01-01

Full Text Available The aim of this study is to measure resources for R&D (research and development at the regional level in Romania and also obtain primary data that will be important in making the right decisions to increase competitiveness and development based on an economic knowledge. As our motivation, we would like to emphasize that by the use of Super Fuzzy FRM model we want to determine the state of R&D processes at regional level using a mean different from the statistical survey, while by the two optimization methods we mean to provide optimization solutions for the R&D actions of the enterprises. Therefore to fulfill the above mentioned aim in this application-oriented paper we decided to use a questionnaire and for the interpretation of the results the Super Fuzzy FRM model, representing the main novelty of our paper, as this theory provides a formalism based on matrix calculus, which allows processing of large volumes of information and also delivers results difficult or impossible to see, through statistical processing. Furthermore another novelty of the paper represents the optimization solutions submitted in this work, given for the situation when the sales price is variable, and the quantity sold is constant in time and for the reverse situation.

An Efficient Optimization Method for Solving Unsupervised Data Classification Problems

Directory of Open Access Journals (Sweden)

Parvaneh Shabanzadeh

2015-01-01

Full Text Available Unsupervised data classification (or clustering analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

Parallel optimization algorithm for drone inspection in the building industry

Science.gov (United States)

Walczyński, Maciej; BoŻejko, Wojciech; Skorupka, Dariusz

2017-07-01

In this paper we present an approach for Vehicle Routing Problem with Drones (VRPD) in case of building inspection from the air. In autonomic inspection process there is a need to determine of the optimal route for inspection drone. This is especially important issue because of the very limited flight time of modern multicopters. The method of determining solutions for Traveling Salesman Problem(TSP), described in this paper bases on Parallel Evolutionary Algorithm (ParEA)with cooperative and independent approach for communication between threads. This method described first by Bożejko and Wodecki [1] bases on the observation that if exists some number of elements on certain positions in a number of permutations which are local minima, then those elements will be in the same position in the optimal solution for TSP problem. Numerical experiments were made on BEM computational cluster with using MPI library.

Sensitivity of Optimal Solutions to Control Problems for Second Order Evolution Subdifferential Inclusions.

Science.gov (United States)

Bartosz, Krzysztof; Denkowski, Zdzisław; Kalita, Piotr

In this paper the sensitivity of optimal solutions to control problems described by second order evolution subdifferential inclusions under perturbations of state relations and of cost functionals is investigated. First we establish a new existence result for a class of such inclusions. Then, based on the theory of sequential [Formula: see text]-convergence we recall the abstract scheme concerning convergence of minimal values and minimizers. The abstract scheme works provided we can establish two properties: the Kuratowski convergence of solution sets for the state relations and some complementary [Formula: see text]-convergence of the cost functionals. Then these two properties are implemented in the considered case.

Asymptotic Method of Solution for a Problem of Construction of Optimal Gas-Lift Process Modes

Directory of Open Access Journals (Sweden)

Fikrat A. Aliev

2010-01-01

Full Text Available Mathematical model in oil extraction by gas-lift method for the case when the reciprocal value of well's depth represents a small parameter is considered. Problem of optimal mode construction (i.e., construction of optimal program trajectories and controls is reduced to the linear-quadratic optimal control problem with a small parameter. Analytic formulae for determining the solutions at the first-order approximation with respect to the small parameter are obtained. Comparison of the obtained results with known ones on a specific example is provided, which makes it, in particular, possible to use obtained results in realizations of oil extraction problems by gas-lift method.

Microgrids Real-Time Pricing Based on Clustering Techniques

Directory of Open Access Journals (Sweden)

Hao Liu

2018-05-01

Full Text Available Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.

Fermion cluster algorithms

International Nuclear Information System (INIS)

Chandrasekharan, Shailesh

2000-01-01

Cluster algorithms have been recently used to eliminate sign problems that plague Monte-Carlo methods in a variety of systems. In particular such algorithms can also be used to solve sign problems associated with the permutation of fermion world lines. This solution leads to the possibility of designing fermion cluster algorithms in certain cases. Using the example of free non-relativistic fermions we discuss the ideas underlying the algorithm

Local entropy as a measure for sampling solutions in constraint satisfaction problems

International Nuclear Information System (INIS)

Baldassi, Carlo; Ingrosso, Alessandro; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo

2016-01-01

We introduce a novel entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random constraint satisfaction problems (CSPs). First, we extend a recent result that, using a large-deviation analysis, shows that the geometry of the space of solutions of the binary perceptron learning problem (a prototypical CSP), contains regions of very high-density of solutions. Despite being sub-dominant, these regions can be found by optimizing a local entropy measure. Building on these results, we construct a fast solver that relies exclusively on a local entropy estimate, and can be applied to general CSPs. We describe its performance not only for the perceptron learning problem but also for the random K-satisfiabilty problem (another prototypical CSP with a radically different structure), and show numerically that a simple zero-temperature Metropolis search in the smooth local entropy landscape can reach sub-dominant clusters of optimal solutions in a small number of steps, while standard Simulated Annealing either requires extremely long cooling procedures or just fails. We also discuss how the EdMC can heuristically be made even more efficient for the cases we studied. (paper: disordered systems, classical and quantum)

RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks

Energy Technology Data Exchange (ETDEWEB)

Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)

2013-12-25

The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need

Cluster implementation for parallel computation within MATLAB software environment

International Nuclear Information System (INIS)

Santana, Antonio O. de; Dantas, Carlos C.; Charamba, Luiz G. da R.; Souza Neto, Wilson F. de; Melo, Silvio B. Melo; Lima, Emerson A. de O.

2013-01-01

A cluster for parallel computation with MATLAB software the COCGT - Cluster for Optimizing Computing in Gamma ray Transmission methods, is implemented. The implementation correspond to creation of a local net of computers, facilities and configurations of software, as well as the accomplishment of cluster tests for determine and optimizing of performance in the data processing. The COCGT implementation was required by data computation from gamma transmission measurements applied to fluid dynamic and tomography reconstruction in a FCC-Fluid Catalytic Cracking cold pilot unity, and simulation data as well. As an initial test the determination of SVD - Singular Values Decomposition - of random matrix with dimension (n , n), n=1000, using the Girco's law modified, revealed that COCGT was faster in comparison to the literature [1] cluster, which is similar and operates at the same conditions. Solution of a system of linear equations provided a new test for the COCGT performance by processing a square matrix with n=10000, computing time was 27 s and for square matrix with n=12000, computation time was 45 s. For determination of the cluster behavior in relation to 'parfor' (parallel for-loop) and 'spmd' (single program multiple data), two codes were used containing those two commands and the same problem: determination of SVD of a square matrix with n= 1000. The execution of codes by means of COCGT proved: 1) for the code with 'parfor', the performance improved with the labs number from 1 to 8 labs; 2) for the code 'spmd', just 1 lab (core) was enough to process and give results in less than 1 s. In similar situation, with the difference that now the SVD will be determined from square matrix with n1500, for code with 'parfor', and n=7000, for code with 'spmd'. That results take to conclusions: 1) for the code with 'parfor', the behavior was the same already described above; 2) for code with 'spmd', the same besides having produced a larger performance, it supports a

Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization

Directory of Open Access Journals (Sweden)

R. Mukesh

2012-01-01

Full Text Available The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA, simulated annealing (SA, and particle swarm optimization (PSO, are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

Metode RCE-Kmeans untuk Clustering Data

Directory of Open Access Journals (Sweden)

Izmy Alwiah Musdar

2015-07-01

Abstract  There have been many methods developed to solve the clustering problem. One of them is method in swarm intelligence field such as Particle Swarm Optimization (PSO. Rapid Centroid Estimation (RCE is a method of clustering based Particle Swarm Optimization. RCE, like other variants of PSO clustering, does not depend on initial cluster centers. Moreover, RCE has faster computational time than the previous method like PSC and mPSC. However, RCE has higher standar deviation value than PSC and mPSC in which has impact in the variance of clustering result. It is happaned because of improper equilibrium state, a condition in which the position of the particle does not change anymore, when  the stopping criteria is reached. This study proposes RCE-Kmeans which is a  method applying K-means after the equilibrium state of RCE  reached to update the particle's position which is generated from the RCE method. The results showed that RCE-Kmeans has better quality of the clustering scheme in 7 of 10 datasets compared to K-means and better in 8 of 10 dataset then RCE method. The use of K-means clustering on the RCE method is also able to reduce the standard deviation from RCE method.   Keywords—Data Clustering, Particle Swarm, K-means, Rapid Centroid Estimation.

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

Science.gov (United States)

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

Science.gov (United States)

Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

2018-01-01

In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

Structural properties of small rhodium clusters

Energy Technology Data Exchange (ETDEWEB)

Soon, Yee Yeen; Yoon, Tiem Leong [School of Physics, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia); Lim, Thong Leng [Faculty of Engineering and Technology, Multimedia University, Melaka Campus, 75450 Melaka (Malaysia)

2015-04-24

We report a systematic study of the structural properties of rhodium clusters at the atomistic level. A novel global-minimum search algorithm, known as parallel tempering multicanonical basin hopping plus genetic algorithm (PTMBHGA), is used to obtain the geometrical structures with lowest minima at the semi-empirical level where Gupta potential is used to describe the atomic interaction among the rhodium atoms. These structures are then re-optimized at the density functional theory (DFT) level with exchange-correlation energy approximated by Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA). The structures are optimized for different spin multiplicities. The ones with lowest energies will be taken as ground-state structures. In most cases, we observe only minor changes in the geometry and bond length of the clusters as a result of DFT-level re-optimization. Only in some limited cases, the initial geometries obtained from the PTMBHGA are modified by the re-optimization. The variation of structural properties, such as ground-state geometry, symmetry and binding energy, with respect to the cluster size is studied and agreed well with other results available in the literature.

Ions, solutes and solvents, oh my!

Energy Technology Data Exchange (ETDEWEB)

Kemp, Daniel David [Iowa State Univ., Ames, IA (United States)

2009-08-01

Modern methods in ab initio quantum mechanics have become efficient and accurate enough to study many gas-phase systems. However, chemists often work in the solution phase. The presence of solvent molecules has been shown to affect reaction mechanisms1, lower reaction energy barriers2, participate in energy transfer with the solute3 and change the physical properties of the solute4. These effects would be overlooked in simple gas phase calculations. Careful study of specific solvents and solutes must be done in order to fully understand the chemistry of the solution phase. Water is a key solvent in chemical and biological applications. The properties of an individual water molecule (a monomer) and the behavior of thousands of molecules (bulk solution) are well known for many solvents. Much is also understood about aqueous microsolvation (small clusters containing ten water molecules or fewer) and the solvation characteristics when bulk water is chosen to solvate a solute. However, much less is known about how these properties behave as the cluster size transitions from the microsolvated cluster size to the bulk. This thesis will focus on species solvated with water clusters that are large enough to exhibit the properties of the bulk but small enough to consist of fewer than one hundred solvent molecules. New methods to study such systems will also be presented.

Orbit Clustering Based on Transfer Cost

Science.gov (United States)

Gustafson, Eric D.; Arrieta-Camacho, Juan J.; Petropoulos, Anastassios E.

2013-01-01

We propose using cluster analysis to perform quick screening for combinatorial global optimization problems. The key missing component currently preventing cluster analysis from use in this context is the lack of a useable metric function that defines the cost to transfer between two orbits. We study several proposed metrics and clustering algorithms, including k-means and the expectation maximization algorithm. We also show that proven heuristic methods such as the Q-law can be modified to work with cluster analysis.

Analytical Solution for Stellar Density in Globular Clusters MA Sharaf

Indian Academy of Sciences (India)

Introduction. A globular cluster is a spherical collection of stars that orbits a galactic core as a satellite. They are generally composed of hundreds of thousands of low-metal, old stars. The types of stars found in a globular cluster are similar to those in the bulge of a spiral galaxy but confined to a volume of only a few cubic ...

Personalized Metaheuristic Clustering Onto Web Documents

Institute of Scientific and Technical Information of China (English)

Wookey Lee

2004-01-01

Optimal clustering for the web documents is known to complicated cornbinatorial Optimization problem and it is hard to develop a generally applicable oplimal algorithm. An accelerated simuIated arlneaIing aIgorithm is developed for automatic web document classification. The web document classification problem is addressed as the problem of best describing a match between a web query and a hypothesized web object. The normalized term frequency and inverse document frequency coefficient is used as a measure of the match. Test beds are generated on - line during the search by transforming model web sites. As a result, web sites can be clustered optimally in terms of keyword vectofs of corresponding web documents.

Searching for optimal integer solutions to set partitioning problems using column generation

OpenAIRE

Bredström, David; Jörnsten, Kurt; Rönnqvist, Mikael

2007-01-01

We describe a new approach to produce integer feasible columns to a set partitioning problem directly in solving the linear programming (LP) relaxation using column generation. Traditionally, column generation is aimed to solve the LP relaxation as quick as possible without any concern of the integer properties of the columns formed. In our approach we aim to generate the columns forming the optimal integer solution while simultaneously solving the LP relaxation. By this we can re...

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

Science.gov (United States)

Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy

Directory of Open Access Journals (Sweden)

Ning Li

2016-01-01

Full Text Available A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.

Moving target tracking through distributed clustering in directional sensor networks.

Science.gov (United States)

Enayet, Asma; Razzaque, Md Abdur; Hassan, Mohammad Mehedi; Almogren, Ahmad; Alamri, Atif

2014-12-18

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

Moving Target Tracking through Distributed Clustering in Directional Sensor Networks

Directory of Open Access Journals (Sweden)

Asma Enayet

2014-12-01

Full Text Available The problem of moving target tracking in directional sensor networks (DSNs introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target’s location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture

Science.gov (United States)

Kreitler, Jason R.; Stoms, David M.; Davis, Frank W.

2014-01-01

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.

SETTING OF TASK OF OPTIMIZATION OF THE ACTIVITY OF A MACHINE-BUILDING CLUSTER COMPANY

Directory of Open Access Journals (Sweden)

A. V. Romanenko

2014-01-01

Full Text Available The work is dedicated to the development of methodological approaches to the management of machine-building enterprise on the basis of cost reduction, optimization of the portfolio of orders and capacity utilization in the process of operational management. Evaluation of economic efficiency of such economic entities of the real sector of the economy is determined, including the timing of orders, which depend on the issues of building a production facility, maintenance of fixed assets and maintain them at a given level. Formulated key components of economic-mathematical model of industrial activity and is defined as the optimization criterion. As proposed formula accumulating profits due to production capacity and technology to produce products current direct variable costs, the amount of property tax and expenses appearing as a result of manifestations of variance when performing replacement of production tasks for a single period of time. The main component of the optimization of the production activity of the enterprise on the basis of this criterion is the vector of direct variable costs. It depends on the number of types of products in the current portfolio of orders, production schedules production, the normative time for the release of a particular product available Fund time efficient production positions, the current valuation for certain groups of technological operations and the current priority of operations for the degree of readiness performed internal orders. Modeling of industrial activity based on the proposed provisions would allow the enterprises of machine-building cluster, active innovation, improve the efficient use of available production resources by optimizing current operations at the high uncertainty of the magnitude of the demand planning and carrying out maintenance and routine repairs.

BRAIN Journal - Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm

OpenAIRE

Ahmet Demir; Utku Kose

2016-01-01

ABSTRACT In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Sc...

Cluster structures influenced by interaction with a surface.

Science.gov (United States)

Witt, Christopher; Dieterich, Johannes M; Hartke, Bernd

2018-05-30

Clusters on surfaces are vitally important for nanotechnological applications. Clearly, cluster-surface interactions heavily influence the preferred cluster structures, compared to clusters in vacuum. Nevertheless, systematic explorations and an in-depth understanding of these interactions and how they determine the cluster structures are still lacking. Here we present an extension of our well-established non-deterministic global optimization package OGOLEM from isolated clusters to clusters on surfaces. Applying this approach to intentionally simple Lennard-Jones test systems, we produce a first systematic exploration that relates changes in cluster-surface interactions to resulting changes in adsorbed cluster structures.

A solution to the optimal power flow using multi-verse optimizer

Directory of Open Access Journals (Sweden)

Bachir Bentouati

2016-12-01

Full Text Available In this work, the most common problem of the modern power system named optimal power flow (OPF is optimized using the novel meta-heuristic optimization Multi-verse Optimizer(MVO algorithm. In order to solve the optimal power flow problem, the IEEE 30-bus and IEEE 57-bus systems are used. MVO is applied to solve the proposed problem. The problems considered in the OPF problem are fuel cost reduction, voltage profile improvement, voltage stability enhancement. The obtained results are compared with recently published meta-heuristics. Simulation results clearly reveal the effectiveness and the rapidity of the proposed algorithm for solving the OPF problem.

Impact of discretization of the decision variables in the search of optimized solutions for history matching and injection rate optimization; Impacto do uso de variaveis discretas na busca de solucoes otimizadas para o ajuste de historico e distribuicao de vazoes de injecao

Energy Technology Data Exchange (ETDEWEB)

Sousa, Sergio H.G. de; Madeira, Marcelo G. [Halliburton Servicos Ltda., Rio de Janeiro, RJ (Brazil)

2008-07-01

In the classical operations research arena, there is the notion that the search for optimized solutions in continuous solution spaces is easier than on discrete solution spaces, even when the latter is a subset of the first. On the upstream oil industry, there is an additional complexity in the optimization problems because there usually are no analytical expressions for the objective function, which require some form of simulation in order to be evaluated. Thus, the use of meta heuristic optimizers like scatter search, tabu search and genetic algorithms is common. In this meta heuristic context, there are advantages in transforming continuous solution spaces in equivalent discrete ones; the goal to do so usually is to speed up the search for optimized solutions. However, these advantages can be masked when the problem has restrictions formed by linear combinations of its decision variables. In order to study these aspects of meta heuristic optimization, two optimization problems are proposed and solved with both continuous and discrete solution spaces: assisted history matching and injection rates optimization. Both cases operate on a model of the Wytch Farm onshore oil filed located in England. (author)

Methods for providing decision makers with optimal solutions for multiple objectives that change over time

CSIR Research Space (South Africa)

Greeff, M

2010-09-01

Full Text Available Decision making - with the goal of finding the optimal solution - is an important part of modern life. For example: In the control room of an airport, the goals or objectives are to minimise the risk of airplanes colliding, minimise the time that a...

Optimization of the Coupled Cluster Implementation in NWChem on Petascale Parallel Architectures

Energy Technology Data Exchange (ETDEWEB)

Anisimov, Victor; Bauer, Gregory H.; Chadalavada, Kalyana; Olson, Ryan M.; Glenski, Joseph W.; Kramer, William T.; Apra, Edoardo; Kowalski, Karol

2014-09-04

Coupled cluster singles and doubles (CCSD) algorithm has been optimized in NWChem software package. This modification alleviated the communication bottleneck and provided from 2- to 5-fold speedup in the CCSD iteration time depending on the problem size and available memory. Sustained 0.60 petaflop/sec performance on CCSD(T) calculation has been obtained on NCSA Blue Waters. This number included all stages of the calculation from initialization till termination, iterative computation of single and double excitations, and perturbative accounting for triple excitations. In the section of perturbative triples alone, the computation maintained 1.18 petaflop/sec performance level. CCSD computations have been performed on Guanine-Cytosine deoxydinucleotide monophosphate (GC-dDMP) to probe the conformational energy difference in DNA single strand in A- and B-conformations. The computation revealed significant discrepancy between CCSD and classical force fields in prediction of relative energy of A- and B-conformations of GC-dDMP.

The Shortlist Method for fast computation of the Earth Mover's Distance and finding optimal solutions to transportation problems.

Science.gov (United States)

Gottschlich, Carsten; Schuhmacher, Dominic

2014-01-01

Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method.

Design of Distributed Controllers Seeking Optimal Power Flow Solutions under Communication Constraints: Preprint

Energy Technology Data Exchange (ETDEWEB)

Dall' Anese, Emiliano; Simonetto, Andrea; Dhople, Sairaj

2016-12-01

This paper focuses on power distribution networks featuring inverter-interfaced distributed energy resources (DERs), and develops feedback controllers that drive the DER output powers to solutions of time-varying AC optimal power flow (OPF) problems. Control synthesis is grounded on primal-dual-type methods for regularized Lagrangian functions, as well as linear approximations of the AC power-flow equations. Convergence and OPF-solution-tracking capabilities are established while acknowledging: i) communication-packet losses, and ii) partial updates of control signals. The latter case is particularly relevant since it enables asynchronous operation of the controllers where DER setpoints are updated at a fast time scale based on local voltage measurements, and information on the network state is utilized if and when available, based on communication constraints. As an application, the paper considers distribution systems with high photovoltaic integration, and demonstrates that the proposed framework provides fast voltage-regulation capabilities, while enabling the near real-time pursuit of solutions of AC OPF problems.

Optimization Strategies for Hardware-Based Cofactorization

Science.gov (United States)

Loebenberger, Daniel; Putzka, Jens

We use the specific structure of the inputs to the cofactorization step in the general number field sieve (GNFS) in order to optimize the runtime for the cofactorization step on a hardware cluster. An optimal distribution of bitlength-specific ECM modules is proposed and compared to existing ones. With our optimizations we obtain a speedup between 17% and 33% of the cofactorization step of the GNFS when compared to the runtime of an unoptimized cluster.

Research reactor in-core fuel management optimization by application of multiple cyclic interchange algorithms

Energy Technology Data Exchange (ETDEWEB)

van Geemert, R.; Hoogenboom, J.E.; Gibcus, H.P.M. [Technische Univ. Delft (Netherlands). Interfacultair Reactor Inst.; Quist, A.J. [Delft University of Technology, Faculty of Applied Mathematics and Informatics Mekelweg 4, 2628 JB, Delft (Netherlands)

1998-12-01

Fuel shuffling optimization procedures are proposed for the Hoger Onderwijs Reactor (HOR) in Delft, The Netherlands, a 2MWth swimming-pool type research reactor. These procedures are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the `elite` cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic acceptance tests. The possible objectives and the safety and operation constraints, as well as the optimization procedure, are discussed, followed by some optimization results for the HOR. (orig.) 4 refs.

Research reactor in-core fuel management optimization by application of multiple cyclic interchange algorithms

International Nuclear Information System (INIS)

Geemert, R. van; Hoogenboom, J.E.; Gibcus, H.P.M.

1998-01-01

Fuel shuffling optimization procedures are proposed for the Hoger Onderwijs Reactor (HOR) in Delft, The Netherlands, a 2MWth swimming-pool type research reactor. These procedures are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the 'elite' cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic acceptance tests. The possible objectives and the safety and operation constraints, as well as the optimization procedure, are discussed, followed by some optimization results for the HOR. (orig.)

Numerical solution of the state-delayed optimal control problems by a fast and accurate finite difference θ-method

Science.gov (United States)

Hajipour, Mojtaba; Jajarmi, Amin

2018-02-01

Using the Pontryagin's maximum principle for a time-delayed optimal control problem results in a system of coupled two-point boundary-value problems (BVPs) involving both time-advance and time-delay arguments. The analytical solution of this advance-delay two-point BVP is extremely difficult, if not impossible. This paper provides a discrete general form of the numerical solution for the derived advance-delay system by applying a finite difference θ-method. This method is also implemented for the infinite-time horizon time-delayed optimal control problems by using a piecewise version of the θ-method. A matrix formulation and the error analysis of the suggested technique are provided. The new scheme is accurate, fast and very effective for the optimal control of linear and nonlinear time-delay systems. Various types of finite- and infinite-time horizon problems are included to demonstrate the accuracy, validity and applicability of the new technique.

Deposition of size-selected atomic clusters on surfaces

International Nuclear Information System (INIS)

Carroll, S.J.

1999-06-01

This dissertation presents technical developments and experimental and computational investigations concerned with the deposition of atomic clusters onto surfaces. It consists of a collection of papers, in which the main body of results are contained, and four chapters presenting a subject review, computational and experimental techniques and a summary of the results presented in full within the papers. Technical work includes the optimization of an existing gas condensation cluster source based on evaporation, and the design, construction and optimization of a new gas condensation cluster source based on RF magnetron sputtering (detailed in Paper 1). The result of cluster deposition onto surfaces is found to depend on the cluster deposition energy; three impact energy regimes are explored in this work. (1) Low energy: n clusters create a defect in the surface, which pins the cluster in place, inhibiting cluster diffusion at room temperature (Paper V). (3) High energy: > 50 eV/atom. The clusters implant into the surface. For Ag 20 -Ag 200 clusters, the implantation depth is found to scale linearly with the impact energy and inversely with the cross-sectional area of the cluster, with an offset due to energy lost to the elastic compression of the surface (Paper VI). For smaller (Ag 3 ) clusters the orientation of the cluster with respect to the surface and the precise impact site play an important role; the impact energy has to be 'focused' in order for cluster implantation to occur (Paper VII). The application of deposited clusters for the creation of Si nanostructures by plasma etching is explored in Paper VIII. (author)

Quadratically convergent algorithm for orbital optimization in the orbital-optimized coupled-cluster doubles method and in orbital-optimized second-order Møller-Plesset perturbation theory

Science.gov (United States)

Bozkaya, Uǧur; Turney, Justin M.; Yamaguchi, Yukio; Schaefer, Henry F.; Sherrill, C. David

2011-09-01

Using a Lagrangian-based approach, we present a more elegant derivation of the equations necessary for the variational optimization of the molecular orbitals (MOs) for the coupled-cluster doubles (CCD) method and second-order Møller-Plesset perturbation theory (MP2). These orbital-optimized theories are referred to as OO-CCD and OO-MP2 (or simply "OD" and "OMP2" for short), respectively. We also present an improved algorithm for orbital optimization in these methods. Explicit equations for response density matrices, the MO gradient, and the MO Hessian are reported both in spin-orbital and closed-shell spin-adapted forms. The Newton-Raphson algorithm is used for the optimization procedure using the MO gradient and Hessian. Further, orbital stability analyses are also carried out at correlated levels. The OD and OMP2 approaches are compared with the standard MP2, CCD, CCSD, and CCSD(T) methods. All these methods are applied to H2O, three diatomics, and the O_4^+ molecule. Results demonstrate that the CCSD and OD methods give nearly identical results for H2O and diatomics; however, in symmetry-breaking problems as exemplified by O_4^+, the OD method provides better results for vibrational frequencies. The OD method has further advantages over CCSD: its analytic gradients are easier to compute since there is no need to solve the coupled-perturbed equations for the orbital response, the computation of one-electron properties are easier because there is no response contribution to the particle density matrices, the variational optimized orbitals can be readily extended to allow inactive orbitals, it avoids spurious second-order poles in its response function, and its transition dipole moments are gauge invariant. The OMP2 has these same advantages over canonical MP2, making it promising for excited state properties via linear response theory. The quadratically convergent orbital-optimization procedure converges quickly for OMP2, and provides molecular properties that

Stochastic network interdiction optimization via capacitated network reliability modeling and probabilistic solution discovery

International Nuclear Information System (INIS)

Ramirez-Marquez, Jose Emmanuel; Rocco S, Claudio M.

2009-01-01

This paper introduces an evolutionary optimization approach that can be readily applied to solve stochastic network interdiction problems (SNIP). The network interdiction problem solved considers the minimization of the cost associated with an interdiction strategy such that the maximum flow that can be transmitted between a source node and a sink node for a fixed network design is greater than or equal to a given reliability requirement. Furthermore, the model assumes that the nominal capacity of each network link and the cost associated with their interdiction can change from link to link and that such interdiction has a probability of being successful. This version of the SNIP is for the first time modeled as a capacitated network reliability problem allowing for the implementation of computation and solution techniques previously unavailable. The solution process is based on an evolutionary algorithm that implements: (1) Monte-Carlo simulation, to generate potential network interdiction strategies, (2) capacitated network reliability techniques to analyze strategies' source-sink flow reliability and, (3) an evolutionary optimization technique to define, in probabilistic terms, how likely a link is to appear in the final interdiction strategy. Examples for different sizes of networks are used throughout the paper to illustrate the approach

Optimization of Multipurpose Reservoir Operation with Application Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Elahe Fallah Mehdipour

2012-12-01

Full Text Available Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research, application of multi-objective particle swarm optimization (MOPSO in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture, recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher; Meymand, Hamed Zeinoddini; Mojarrad, Hasan Doagou [Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, P.O. 71555-313 (Iran, Islamic Republic of)

2011-05-15

In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem. (author)

A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Wenping Zou

2010-01-01

Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

Characterisation, degradation and regeneration of luminescent Ag29 clusters in solution

NARCIS (Netherlands)

van der Linden, Marte; Barendregt, Arjan; van Bunningen, Arnoldus J; Chin, Patrick T K; Thies-Weesie, Dominique; de Groot, Frank M F; Meijerink, A

2016-01-01

Luminescent Ag clusters are prepared with lipoic acid (LA) as the ligand. Using a combination of mass spectrometry, optical spectroscopy and analytical ultracentrifugation, the clusters are found to be highly monodisperse with mass 5.6 kDa. We assign the chemical composition [Ag29(LA)12](3-) to the

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

Science.gov (United States)

Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

2017-01-01

Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

Electrical Discharge Platinum Machining Optimization Using Stefan Problem Solutions

Directory of Open Access Journals (Sweden)

I. B. Stavitskiy

2015-01-01

Full Text Available The article presents the theoretical study results of platinum workability by electrical discharge machining (EDM, based on the solution of the thermal problem of moving the boundary of material change phase, i.e. Stefan problem. The problem solution enables defining the surface melt penetration of the material under the heat flow proceeding from the time of its action and the physical properties of the processed material. To determine the rational EDM operating conditions of platinum the article suggests relating its workability with machinability of materials, for which the rational EDM operating conditions are, currently, defined. It is shown that at low densities of the heat flow corresponding to the finishing EDM operating conditions, the processing conditions used for steel 45 are appropriate for platinum machining; with EDM at higher heat flow densities (e.g. 50 GW / m2 for this purpose copper processing conditions are used; at the high heat flow densities corresponding to heavy roughing EDM it is reasonable to use tungsten processing conditions. The article also represents how the minimum width of the current pulses, at which platinum starts melting and, accordingly, the EDM process becomes possible, depends on the heat flow density. It is shown that the processing of platinum is expedient at a pulse width corresponding to the values, called the effective pulse width. Exceeding these values does not lead to a substantial increase in removal of material per pulse, but considerably reduces the maximum repetition rate and therefore, the EDM capacity. The paper shows the effective pulse width versus the heat flow density. It also presents the dependences of the maximum platinum surface melt penetration and the corresponding pulse width on the heat flow density. Results obtained using solutions of the Stephen heat problem can be used to optimize EDM operating conditions of platinum machining.

Moment-tensor solutions estimated using optimal filter theory: Global seismicity, 2001

Science.gov (United States)

Sipkin, S.A.; Bufe, C.G.; Zirbes, M.D.

2003-01-01

This paper is the 12th in a series published yearly containing moment-tensor solutions computed at the US Geological Survey using an algorithm based on the theory of optimal filter design (Sipkin, 1982 and Sipkin, 1986b). An inversion has been attempted for all earthquakes with a magnitude, mb or MS, of 5.5 or greater. Previous listings include solutions for earthquakes that occurred from 1981 to 2000 (Sipkin, 1986b; Sipkin and Needham, 1989, Sipkin and Needham, 1991, Sipkin and Needham, 1992, Sipkin and Needham, 1993, Sipkin and Needham, 1994a and Sipkin and Needham, 1994b; Sipkin and Zirbes, 1996 and Sipkin and Zirbes, 1997; Sipkin et al., 1998, Sipkin et al., 1999, Sipkin et al., 2000a, Sipkin et al., 2000b and Sipkin et al., 2002).The entire USGS moment-tensor catalog can be obtained via anonymous FTP at ftp://ghtftp.cr.usgs.gov. After logging on, change directory to “momten”. This directory contains two compressed ASCII files that contain the finalized solutions, “mt.lis.Z” and “fmech.lis.Z”. “mt.lis.Z” contains the elements of the moment tensors along with detailed event information; “fmech.lis.Z” contains the decompositions into the principal axes and best double-couples. The fast moment-tensor solutions for more recent events that have not yet been finalized and added to the catalog, are gathered by month in the files “jan01.lis.Z”, etc. “fmech.doc.Z” describes the various fields.

Positron lifetime in vacancy clusters. Application to the study of vacancy-impurity interactions

International Nuclear Information System (INIS)

Corbel, C.

1986-02-01

Positron lifetime measurements are used to study the vacancy recovery (77-650 K) in 20 K electron irradiated dilute gold or iron alloys in stainless steels. Positron lifetimes in clusters of various shapes and structures are calculated to precise the information obtained by measuring the positron lifetime in a vacancy cluster of unknown size and configuration. From the calculations, we have drawn the following conclusions: - the minimal size of an unknown cluster can be deduced from the measurement of the positron lifetime in the cluster; - a decrease of the positron lifetime when the structure of the cluster evolves, means either a decrease of the size of the cluster, or, the appearance of a relaxed configuration. - The positron lifetime is very useful to discriminate between a spatial planar or relaxed configuration and a tri-dimensional one. In AuGe, AuSb, AuTn alloys, vacancy clusters decorated by solute atoms appear at 250 K. Their configurations are different from those in pure Au. Mobile vacancy-solutes complexes are involved in the clustering process in AuGe, AuSb. The clusters are probably decorated by several solute atoms in AuGe and AuSb where the resistivity evidences a clustering of solute atoms. In AuFe, vacancy-single or multi-complexes stable up to 670 K prevent cluster formation. In FeTi, FeSb, FeAu, vacancy migration is hindered by the formation of vacancy-solute complexes up to 315 K (Ti), up to 670 K (Sb), up to 700 K (Au). In FeSi, FeCu, FeAg, tri-dimensional clusters grow less easily than Fe. This is likely due to the formation of several kinds of small decorated clusters with relaxed or planar configurations. They are peculiarly stable, surviving up to 700 K at least. In Si (resp. Ti) doped 59Fe25Ni16Cr, solute atoms retain the vacancies up to 300 K (resp. 320 K) [fr

BCS superconductivity for weakly coupled clusters

International Nuclear Information System (INIS)

Friedel, J.

1992-01-01

BCS superconductivity is expected to have fairly high critical temperatures when clusters of moderate sizes are weakly coupled to form a crystal. This remark extends to quasi zerodimensional cases, a remark initially made by Labbe for quasi one-dimensional ones and by Hirsch, Bok and Labbe for quasi twodimensional ones. Possible applications are envisaged for twodimensional clusters (fullerene) or threedimensional ones (metal clusters, Chevrel phases). Conditions for optimal applicability of the scheme are somewhat restricted. (orig.)

Re-weighted Discriminatively Embedded K-Means for Multi-view Clustering.

Science.gov (United States)

Xu, Jinglin; Han, Junwei; Nie, Feiping; Li, Xuelong

2017-02-08

Recent years, more and more multi-view data are widely used in many real world applications. This kind of data (such as image data) are high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called Re-weighted Discriminatively Embedded KMeans (RDEKM), for this task. The proposed method is a multiview least-absolute residual model which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi-view clustering. Specifically, the proposed model is an unsupervised optimization scheme which utilizes Iterative Re-weighted Least Squares to solve leastabsolute residual and adaptively controls the distribution of multiple weights in a re-weighted manner only based on its own low-dimensional subspaces and a common clustering indicator matrix. Furthermore, theoretical analysis (including optimality and convergence analysis) and the optimization algorithm are also presented. Compared to several state-of-the-art multi-view clustering methods, the proposed method substantially improves the accuracy of the clustering results on widely used benchmark datasets, which demonstrates the superiority of the proposed work.

Energy Aware Clustering Algorithms for Wireless Sensor Networks

Science.gov (United States)

Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian

2011-09-01

The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.

A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles.

Science.gov (United States)

Zhang, Lin; Meng, Jia; Liu, Hui; Huang, Yufei

2012-01-01

DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters. The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.

Alpha condensates and nonlocalized cluster structures

International Nuclear Information System (INIS)

Funaki, Yasuro

2014-01-01

We discuss a container structure for non-gaslike cluster states, in which single Tohsaki-Horiuchi-Schuck-ROpke (THSR) wave functions are shown to be almost 100% equivalent to the full solutions of the corresponding RGM/GCM equations, for the inversion doublet band states in 20 Ne, α-linear-chain states, and α + α + A cluster states in 9 Λ Be. The recognition of the fact that the THSR wave function describes well not only gaslike cluster states but also non-gaslike cluster states is a recent remarkable development of nuclear cluster physics. This fact tells us that the cluster structure is composed of cluster-mean-field motion under the constraint of inter-cluster Pauli repulsion, in which we call the cluster-mean-field potential the container. We demonstrate that the evolution of the cluster structure of a nucleus is governed by the size parameter of the cluster-mean-field potential (container), for 16 O nucleus

MANAGING THE DEVELOPMENT OF AGRO-INDUSTRIAL CLUSTERS

Directory of Open Access Journals (Sweden)

D. V. Zavyalov

2018-01-01

Full Text Available Purpose: the purpose of the research is to design a concept of management system for agro-industrial clusters as self-organizing systems. The transition to a new technological way was marked not only by breakthrough solutions in the organization of production of goods, works and services, but also by the emergence of new (in some cases unique forms of inter-firm cooperation and interaction of economic agents in the real and financial sector of the economy. The concept of "digital economy" becomes the most important in economic research, and moreover - from a practical point of view, modern digitalization technologies in managing the activities of economic entities form new information and communication platforms for economic and scientific exchange. The penetration of digital technologies into life is one of the characteristic features of the future world. Not an exception is the agro-industrial sector, which is both strategically important for ensuring food security and has a high export potential. The article presents the concept of managing the development of agro-industrial clusters as self-organizing systems capable of integrating the activities of small and medium-sized businesses into the value-added chain based on modern information technologies. The obligatory and providing tools, mechanisms for implementing the concept, aimed at eliminating existing problems on the way of forming agro-industrial clusters, are disclosed.Methods: the agro-industrial cluster management model is developed using the methods of economic analysis and synthesis, and functional modelling.Results: conceptual model of cluster development management is presented to be used for the nascent clusters and the development of existing agro-industrial clusters.Conclusions and Relevance: as a result of the conducted research the reasons interfering development of cluster approach in the agroindustrial sector of economy are defined. Among them the main are: lack of

Optimal Thermal Unit Commitment Solution integrating Renewable Energy with Generator Outage

Directory of Open Access Journals (Sweden)

S. Sivasakthi

2017-06-01

Full Text Available The increasing concern of global climate changes, the promotion of renewable energy sources, primarily wind generation, is a welcome move to reduce the pollutant emissions from conventional power plants. Integration of wind power generation with the existing power network is an emerging research field. This paper presents a meta-heuristic algorithm based approach to determine the feasible dispatch solution for wind integrated thermal power system. The Unit Commitment (UC process aims to identify the best feasible generation scheme of the committed units such that the overall generation cost is reduced, when subjected to a variety of constraints at each time interval. As the UC formulation involves many variables and system and operational constraints, identifying the best solution is still a research task. Nowadays, it is inevitable to include power system reliability issues in operation strategy. The generator failure and malfunction are the prime influencing factor for reliability issues hence they have considered in UC formulation of wind integrated thermal power system. The modern evolutionary algorithm known as Grey Wolf Optimization (GWO algorithm is applied to solve the intended UC problem. The potential of the GWO algorithm is validated by the standard test systems. Besides, the ramp rate limits are also incorporated in the UC formulation. The simulation results reveal that the GWO algorithm has the capability of obtaining economical resolutions with good solution quality.

Machine Learning meets Mathematical Optimization to predict the optimal production of offshore wind parks

DEFF Research Database (Denmark)

Fischetti, Martina; Fraccaro, Marco

2018-01-01

In this paper we propose a combination of Mathematical Optimization and Machine Learning to estimate the value of optimized solutions. In particular, we investigate if a machine, trained on a large number of optimized solutions, could accurately estimate the value of the optimized solution for new...... in production between optimized/non optimized solutions, it is not trivial to understand the potential value of a new site without running a complete optimization. This could be too time consuming if a lot of sites need to be evaluated, therefore we propose to use Machine Learning to quickly estimate...... the potential of new sites (i.e., to estimate the optimized production of a site without explicitly running the optimization). To do so, we trained and tested different Machine Learning models on a dataset of 3000+ optimized layouts found by the optimizer. Thanks to the close collaboration with a leading...

A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

Science.gov (United States)

Zhang, Yong-Feng; Chiang, Hsiao-Dong

2017-09-01

A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.

A self-consistent model of rich clusters of galaxies. I. The galactic component of a cluster

International Nuclear Information System (INIS)

Konyukov, M.V.

1985-01-01

It is shown that to obtain the distribution function for the galactic component of a cluster reduces in the last analysis to solving the boundary-value problem for the gravitational potential of a self-consistent field. The distribution function is determined by two main parameters. An algorithm is constructed for the solution of the problem, and a program is set up to solve it. It is used to establish the region of values of the parameters in the problem for which solutions exist. The scheme proposed is extended to the case where there exists in the cluster a separate central body with a known density distribution (for example, a cD galaxy). A method is indicated for the estimation of the parameters of the model from the results of observations of clusters of galaxies in the optical range

Simultaneous contrast: evidence from licking microstructure and cross-solution comparisons.

Science.gov (United States)

Dwyer, Dominic M; Lydall, Emma S; Hayward, Andrew J

2011-04-01

The microstructure of rats' licking responses was analyzed to investigate both "classic" simultaneous contrast (e.g., Flaherty & Largen, 1975) and a novel discrete-trial contrast procedure where access to an 8% test solution of sucrose was preceded by a sample of either 2%, 8%, or 32% sucrose (Experiments 1 and 2, respectively). Consumption of a given concentration of sucrose was higher when consumed alongside a low rather than high concentration comparison solution (positive contrast) and consumption of a given concentration of sucrose was lower when consumed alongside a high rather than a low concentration comparison solution (negative contrast). Furthermore, positive contrast increased the size of lick clusters while negative contrast decreased the size of lick clusters. Lick cluster size has a positive monotonic relationship with the concentration of palatable solutions and so positive and negative contrasts produced changes in lick cluster size that were analogous to raising or lowering the concentration of the test solution respectively. Experiment 3 utilized the discrete-trial procedure and compared contrast between two solutions of the same type (sucrose-sucrose or maltodextrin-maltodextrin) or contrast across solutions (sucrose-maltodextrin or maltodextrin-sucrose). Contrast effects on consumption were present, but reduced in size, in the cross-solution conditions. Moreover, lick cluster sizes were not affected at all by cross-solution contrasts as they were by same-solution contrasts. These results are consistent with the idea that simultaneous contrast effects depend, at least partially, on sensory mechanisms.

Optimized bacterial expression and purification of the c-Src catalytic domain for solution NMR studies

International Nuclear Information System (INIS)

Piserchio, Andrea; Ghose, Ranajeet; Cowburn, David

2009-01-01

Progression of a host of human cancers is associated with elevated levels of expression and catalytic activity of the Src family of tyrosine kinases (SFKs), making them key therapeutic targets. Even with the availability of multiple crystal structures of active and inactive forms of the SFK catalytic domain (CD), a complete understanding of its catalytic regulation is unavailable. Also unavailable are atomic or near-atomic resolution information about their interactions, often weak or transient, with regulating phosphatases and downstream targets. Solution NMR, the biophysical method best suited to tackle this problem, was previously hindered by difficulties in bacterial expression and purification of sufficient quantities of soluble, properly folded protein for economically viable labeling with NMR-active isotopes. Through a choice of optimal constructs, co-expression with chaperones and optimization of the purification protocol, we have achieved the ability to bacterially produce large quantities of the isotopically-labeled CD of c-Src, the prototypical SFK, and of its activating Tyr-phosphorylated form. All constructs produce excellent spectra allowing solution NMR studies of this family in an efficient manner

Entropy landscape and non-Gibbs solutions in constraint satisfaction problems

International Nuclear Information System (INIS)

Dall'Asta, L.; Ramezanpour, A.; Zecchina, R.

2008-05-01

We study the entropy landscape of solutions for the bicoloring problem in random graphs, a representative difficult constraint satisfaction problem. Our goal is to classify which type of clusters of solutions are addressed by different algorithms. In the first part of the study we use the cavity method to obtain the number of clusters with a given internal entropy and determine the phase diagram of the problem, e.g. dynamical, rigidity and SAT-UNSAT transitions. In the second part of the paper we analyze different algorithms and locate their behavior in the entropy landscape of the problem. For instance we show that a smoothed version of a decimation strategy based on Belief Propagation is able to find solutions belonging to sub-dominant clusters even beyond the so called rigidity transition where the thermodynamically relevant clusters become frozen. These non-equilibrium solutions belong to the most probable unfrozen clusters. (author)

Application of the distributed genetic algorithm for loading pattern optimization problems

International Nuclear Information System (INIS)

Hashimoto, Hiroshi; Yamamoto, Akio

2000-01-01

The distributed genetic algorithm (DGA) is applied for loading pattern optimization problems of the pressurized water reactors (PWR). Due to stiff nature of the loading pattern optimizations (e.g. multi-modality and non-linearity), stochastic methods like the simulated annealing or the genetic algorithm (GA) are widely applied for these problems. A basic concept of DGA is based on that of GA. However, DGA equally distributes candidates of solutions (i.e. loading patterns) to several independent 'islands' and evolves them in each island. Migrations of some candidates are performed among islands with a certain period. Since candidates of solutions independently evolve in each island with accepting different genes of migrants from other islands, premature convergence in the traditional GA can be prevented. Because many candidate loading patterns should be evaluated in one generation of GA or DGA, the parallelization in these calculations works efficiently. Parallel efficiency was measured using our optimization code and good load balance was attained even in a heterogeneous cluster environment due to dynamic distribution of the calculation load. The optimization code is based on the client/server architecture with the TCP/IP native socket and a client (optimization module) and calculation server modules communicate the objects of loading patterns each other. Throughout the sensitivity study on optimization parameters of DGA, a suitable set of the parameters for a test problem was identified. Finally, optimization capability of DGA and the traditional GA was compared in the test problem and DGA provided better optimization results than the traditional GA. (author)

Resource-efficient generation of linear cluster states by linear optics with postselection

International Nuclear Information System (INIS)

Uskov, D B; Alsing, P M; Fanto, M L; Szep, A; Smith, A M; Kaplan, L; Kim, R

2015-01-01

We report on theoretical research in photonic cluster-state computing. Finding optimal schemes of generating non-classical photonic states is of critical importance for this field as physically implementable photon–photon entangling operations are currently limited to measurement-assisted stochastic transformations. A critical parameter for assessing the efficiency of such transformations is the success probability of a desired measurement outcome. At present there are several experimental groups that are capable of generating multi-photon cluster states carrying more than eight qubits. Separate photonic qubits or small clusters can be fused into a single cluster state by a probabilistic optical CZ gate conditioned on simultaneous detection of all photons with 1/9 success probability for each gate. This design mechanically follows the original theoretical scheme of cluster state generation proposed more than a decade ago by Raussendorf, Browne and Briegel. The optimality of the destructive CZ gate in application to linear optical cluster state generation has not been analyzed previously. Our results reveal that this method is far from the optimal one. Employing numerical optimization we have identified that the maximal success probability of fusing n unentangled dual-rail optical qubits into a linear cluster state is equal to (1/2) n−1 ; an m-tuple of photonic Bell pair states, commonly generated via spontaneous parametric down-conversion, can be fused into a single cluster with the maximal success probability of (1/4) m−1 . (paper)

Localization and orientation of heavy-atom cluster compounds in protein crystals using molecular replacement

International Nuclear Information System (INIS)

Dahms, Sven O.; Kuester, Miriam; Streb, Carsten; Roth, Christian; Sträter, Norbert; Than, Manuel E.

2013-01-01

A new approach is presented that allows the efficient localization and orientation of heavy-atom cluster compounds used in experimental phasing by a molecular replacement procedure. This permits the calculation of meaningful phases up to the highest resolution of the diffraction data. Heavy-atom clusters (HA clusters) containing a large number of specifically arranged electron-dense scatterers are especially useful for experimental phase determination of large complex structures, weakly diffracting crystals or structures with large unit cells. Often, the determination of the exact orientation of the HA cluster and hence of the individual heavy-atom positions proves to be the critical step in successful phasing and subsequent structure solution. Here, it is demonstrated that molecular replacement (MR) with either anomalous or isomorphous differences is a useful strategy for the correct placement of HA cluster compounds. The polyoxometallate cluster hexasodium α-metatungstate (HMT) was applied in phasing the structure of death receptor 6. Even though the HA cluster is bound in alternate partially occupied orientations and is located at a special position, its correct localization and orientation could be determined at resolutions as low as 4.9 Å. The broad applicability of this approach was demonstrated for five different derivative crystals that included the compounds tantalum tetradecabromide and trisodium phosphotungstate in addition to HMT. The correct placement of the HA cluster depends on the length of the intramolecular vectors chosen for MR, such that both a larger cluster size and the optimal choice of the wavelength used for anomalous data collection strongly affect the outcome

An energy-optimal solution for transportation control of cranes with double pendulum dynamics: Design and experiments