Optimization of dual-energy CT acquisitions for proton therapy using projection-based decomposition.

Science.gov (United States)

Vilches-Freixas, Gloria; Létang, Jean Michel; Ducros, Nicolas; Rit, Simon

2017-09-01

Dual-energy computed tomography (DECT) has been presented as a valid alternative to single-energy CT to reduce the uncertainty of the conversion of patient CT numbers to proton stopping power ratio (SPR) of tissues relative to water. The aim of this work was to optimize DECT acquisition protocols from simulations of X-ray images for the treatment planning of proton therapy using a projection-based dual-energy decomposition algorithm. We have investigated the effect of various voltages and tin filtration combinations on the SPR map accuracy and precision, and the influence of the dose allocation between the low-energy (LE) and the high-energy (HE) acquisitions. For all spectra combinations, virtual CT projections of the Gammex phantom were simulated with a realistic energy-integrating detector response model. Two situations were simulated: an ideal case without noise (infinite dose) and a realistic situation with Poisson noise corresponding to a 20 mGy total central dose. To determine the optimal dose balance, the proportion of LE-dose with respect to the total dose was varied from 10% to 90% while keeping the central dose constant, for four dual-energy spectra. SPR images were derived using a two-step projection-based decomposition approach. The ranges of 70 MeV, 90 MeV, and 100 MeV proton beams onto the adult female (AF) reference computational phantom of the ICRP were analytically determined from the reconstructed SPR maps. The energy separation between the incident spectra had a strong impact on the SPR precision. Maximizing the incident energy gap reduced image noise. However, the energy gap was not a good metric to evaluate the accuracy of the SPR. In terms of SPR accuracy, a large variability of the optimal spectra was observed when studying each phantom material separately. The SPR accuracy was almost flat in the 30-70% LE-dose range, while the precision showed a minimum slightly shifted in favor of lower LE-dose. Photon noise in the SPR images (20 mGy dose

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

Science.gov (United States)

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation

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Schell Thomas

2003-01-01

Full Text Available In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

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Shuyu Dai

2018-01-01

Full Text Available Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM (Ensemble Empirical Mode Decomposition and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, MGWO-SVM (Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, GWO-SVM (Support Vector Machine Optimized by Grey Wolf Optimization Algorithm, SVM (Support Vector

Multisensors Cooperative Detection Task Scheduling Algorithm Based on Hybrid Task Decomposition and MBPSO

Directory of Open Access Journals (Sweden)

Changyun Liu

2017-01-01

Full Text Available A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization (MBPSO is proposed. Firstly, aiming at the complex relationship between sensor resources and tasks, a hybrid task decomposition method is presented, and the resource scheduling problem is decomposed into subtasks; then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks. Secondly, the resource match optimization model based on the sensor resources and tasks is established, which considers several factors, such as the target priority, detecting benefit, handover times, and resource load. Finally, MBPSO algorithm is proposed to solve the match optimization model effectively, which is based on the improved updating means of particle’s velocity and position through the doubt factor and modified Sigmoid function. The experimental results show that the proposed algorithm is better in terms of convergence velocity, searching capability, solution accuracy, and efficiency.

Dictionary-Based Tensor Canonical Polyadic Decomposition

Science.gov (United States)

Cohen, Jeremy Emile; Gillis, Nicolas

2018-04-01

To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.

A Decomposition-Based Pricing Method for Solving a Large-Scale MILP Model for an Integrated Fishery

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M. Babul Hasan

2007-01-01

The IFP can be decomposed into a trawler-scheduling subproblem and a fish-processing subproblem in two different ways by relaxing different sets of constraints. We tried conventional decomposition techniques including subgradient optimization and Dantzig-Wolfe decomposition, both of which were unacceptably slow. We then developed a decomposition-based pricing method for solving the large fishery model, which gives excellent computation times. Numerical results for several planning horizon models are presented.

Simulation-optimization of large agro-hydrosystems using a decomposition approach

Science.gov (United States)

Schuetze, Niels; Grundmann, Jens

2014-05-01

In this contribution a stochastic simulation-optimization framework for decision support for optimal planning and operation of water supply of large agro-hydrosystems is presented. It is based on a decomposition solution strategy which allows for (i) the usage of numerical process models together with efficient Monte Carlo simulations for a reliable estimation of higher quantiles of the minimum agricultural water demand for full and deficit irrigation strategies at small scale (farm level), and (ii) the utilization of the optimization results at small scale for solving water resources management problems at regional scale. As a secondary result of several simulation-optimization runs at the smaller scale stochastic crop-water production functions (SCWPF) for different crops are derived which can be used as a basic tool for assessing the impact of climate variability on risk for potential yield. In addition, microeconomic impacts of climate change and the vulnerability of the agro-ecological systems are evaluated. The developed methodology is demonstrated through its application on a real-world case study for the South Al-Batinah region in the Sultanate of Oman where a coastal aquifer is affected by saltwater intrusion due to excessive groundwater withdrawal for irrigated agriculture.

Aerospace engineering design by systematic decomposition and multilevel optimization

Science.gov (United States)

Sobieszczanski-Sobieski, J.; Barthelemy, J. F. M.; Giles, G. L.

1984-01-01

A method for systematic analysis and optimization of large engineering systems, by decomposition of a large task into a set of smaller subtasks that is solved concurrently is described. The subtasks may be arranged in hierarchical levels. Analyses are carried out in each subtask using inputs received from other subtasks, and are followed by optimizations carried out from the bottom up. Each optimization at the lower levels is augmented by analysis of its sensitivity to the inputs received from other subtasks to account for the couplings among the subtasks in a formal manner. The analysis and optimization operations alternate iteratively until they converge to a system design whose performance is maximized with all constraints satisfied. The method, which is still under development, is tentatively validated by test cases in structural applications and an aircraft configuration optimization.

Optimal pattern synthesis for speech recognition based on principal component analysis

Science.gov (United States)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Decomposition with thermoeconomic isolation applied to the optimal synthesis/design and operation of an advanced tactical aircraft system

International Nuclear Information System (INIS)

Rancruel, Diego F.; Spakovsky, Michael R. von

2006-01-01

A decomposition methodology based on the concept of 'thermoeconomic isolation' and applied to the synthesis/design and operational optimization of an advanced tactical fighter aircraft is the focus of this paper. The total system is composed of six sub-systems of which five participate with degrees of freedom (493) in the optimization. They are the propulsion sub-system (PS), the environmental control sub-system (ECS), the fuel loop subsystem (FLS), the vapor compression and Polyalphaolefin (PAO) loops sub-system (VC/PAOS), and the airframe sub-system (AFS). The sixth subsystem comprises the expendable and permanent payloads as well as the equipment group. For each of the first five, detailed thermodynamic, geometric, physical, and aerodynamic models at both design and off-design were formulated and implemented. The most promising set of aircraft sub-system and system configurations were then determined based on both an energy integration and aerodynamic performance analysis at each stage of the mission (including the transient ones). Conceptual, time, and physical decomposition were subsequently applied to the synthesis/design and operational optimization of these aircraft configurations as well as to the highly dynamic process of heat generation and dissipation internal to the subsystems. The physical decomposition strategy used (i.e. Iterative Local-Global Optimization-ILGO) is the first to successfully closely approach the theoretical condition of 'thermoeconomic isolation' when applied to highly complex, highly dynamic non-linear systems. Developed at our Center for Energy Systems research, it has been effectively applied to a number of complex stationary and transportation applications

Decomposition with thermoeconomic isolation applied to the optimal synthesis/design and operation of an advanced tactical aircraft system

Energy Technology Data Exchange (ETDEWEB)

Rancruel, Diego F. [Center for Energy Systems Research, Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060 (United States); Spakovsky, Michael R. von [Center for Energy Systems Research, Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060 (United States)]. E-mail: vonspako@vt.edu

2006-12-15

A decomposition methodology based on the concept of 'thermoeconomic isolation' and applied to the synthesis/design and operational optimization of an advanced tactical fighter aircraft is the focus of this paper. The total system is composed of six sub-systems of which five participate with degrees of freedom (493) in the optimization. They are the propulsion sub-system (PS), the environmental control sub-system (ECS), the fuel loop subsystem (FLS), the vapor compression and Polyalphaolefin (PAO) loops sub-system (VC/PAOS), and the airframe sub-system (AFS). The sixth subsystem comprises the expendable and permanent payloads as well as the equipment group. For each of the first five, detailed thermodynamic, geometric, physical, and aerodynamic models at both design and off-design were formulated and implemented. The most promising set of aircraft sub-system and system configurations were then determined based on both an energy integration and aerodynamic performance analysis at each stage of the mission (including the transient ones). Conceptual, time, and physical decomposition were subsequently applied to the synthesis/design and operational optimization of these aircraft configurations as well as to the highly dynamic process of heat generation and dissipation internal to the subsystems. The physical decomposition strategy used (i.e. Iterative Local-Global Optimization-ILGO) is the first to successfully closely approach the theoretical condition of 'thermoeconomic isolation' when applied to highly complex, highly dynamic non-linear systems. Developed at our Center for Energy Systems research, it has been effectively applied to a number of complex stationary and transportation applications.

Pitfalls in VAR based return decompositions: A clarification

DEFF Research Database (Denmark)

Engsted, Tom; Pedersen, Thomas Quistgaard; Tanggaard, Carsten

in their analysis is not "cashflow news" but "inter- est rate news" which should not be zero. Consequently, in contrast to what Chen and Zhao claim, their decomposition does not serve as a valid caution against VAR based decompositions. Second, we point out that in order for VAR based decompositions to be valid......Based on Chen and Zhao's (2009) criticism of VAR based return de- compositions, we explain in detail the various limitations and pitfalls involved in such decompositions. First, we show that Chen and Zhao's interpretation of their excess bond return decomposition is wrong: the residual component...

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

Science.gov (United States)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Distributed Optimization based Dynamic Tariff for Congestion Management in Distribution Networks

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei; Zhao, Haoran

2017-01-01

This paper proposes a distributed optimization based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles (EVs) and heat pumps (HPs). The DDT method employs a decomposition based optimization method to have aggregators explicitly...... is able to minimize the overall energy consumption cost and line loss cost, which is different from previous decomposition-based methods such as multiagent system methods. In addition, a reconditioning method and an integral controller are introduced to improve convergence of the distributed optimization...... where challenges arise due to multiple congestion points, multiple types of flexible demands and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks....

An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

International Nuclear Information System (INIS)

Yin, Hao; Dong, Zhen; Chen, Yunlong; Ge, Jiafei; Lai, Loi Lei; Vaccaro, Alfredo; Meng, Anbo

2017-01-01

Highlights: • A secondary decomposition approach is applied in the data pre-processing. • The empirical mode decomposition is used to decompose the original time series. • IMF1 continues to be decomposed by applying wavelet packet decomposition. • Crisscross optimization algorithm is applied to train extreme learning machine. • The proposed SHD-CSO-ELM outperforms other pervious methods in the literature. - Abstract: Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

Science.gov (United States)

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-06-03

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

Sensitivity Analysis of the Proximal-Based Parallel Decomposition Methods

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Feng Ma

2014-01-01

Full Text Available The proximal-based parallel decomposition methods were recently proposed to solve structured convex optimization problems. These algorithms are eligible for parallel computation and can be used efficiently for solving large-scale separable problems. In this paper, compared with the previous theoretical results, we show that the range of the involved parameters can be enlarged while the convergence can be still established. Preliminary numerical tests on stable principal component pursuit problem testify to the advantages of the enlargement.

Maximum production rate optimization for sulphuric acid decomposition process in tubular plug-flow reactor

International Nuclear Information System (INIS)

Wang, Chao; Chen, Lingen; Xia, Shaojun; Sun, Fengrui

2016-01-01

A sulphuric acid decomposition process in a tubular plug-flow reactor with fixed inlet flow rate and completely controllable exterior wall temperature profile and reactants pressure profile is studied in this paper by using finite-time thermodynamics. The maximum production rate of the aimed product SO 2 and the optimal exterior wall temperature profile and reactants pressure profile are obtained by using nonlinear programming method. Then the optimal reactor with the maximum production rate is compared with the reference reactor with linear exterior wall temperature profile and the optimal reactor with minimum entropy generation rate. The result shows that the production rate of SO 2 of optimal reactor with the maximum production rate has an increase of more than 7%. The optimization of temperature profile has little influence on the production rate while the optimization of reactants pressure profile can significantly increase the production rate. The results obtained may provide some guidelines for the design of real tubular reactors. - Highlights: • Sulphuric acid decomposition process in tubular plug-flow reactor is studied. • Fixed inlet flow rate and controllable temperature and pressure profiles are set. • Maximum production rate of aimed product SO 2 is obtained. • Corresponding optimal temperature and pressure profiles are derived. • Production rate of SO 2 of optimal reactor increases by 7%.

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

Science.gov (United States)

Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

2018-06-01

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

A new decomposition-based computer-aided molecular/mixture design methodology for the design of optimal solvents and solvent mixtures

DEFF Research Database (Denmark)

Karunanithi, A.T.; Achenie, L.E.K.; Gani, Rafiqul

2005-01-01

This paper presents a novel computer-aided molecular/mixture design (CAMD) methodology for the design of optimal solvents and solvent mixtures. The molecular/mixture design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a performance objective is to be optim......This paper presents a novel computer-aided molecular/mixture design (CAMD) methodology for the design of optimal solvents and solvent mixtures. The molecular/mixture design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a performance objective...... is to be optimized subject to structural, property, and process constraints. The general molecular/mixture design problem is divided into two parts. For optimal single-compound design, the first part is solved. For mixture design, the single-compound design is first carried out to identify candidates...... and then the second part is solved to determine the optimal mixture. The decomposition of the CAMD MINLP model into relatively easy to solve subproblems is essentially a partitioning of the constraints from the original set. This approach is illustrated through two case studies. The first case study involves...

Steganography based on pixel intensity value decomposition

Science.gov (United States)

Abdulla, Alan Anwar; Sellahewa, Harin; Jassim, Sabah A.

2014-05-01

This paper focuses on steganography based on pixel intensity value decomposition. A number of existing schemes such as binary, Fibonacci, Prime, Natural, Lucas, and Catalan-Fibonacci (CF) are evaluated in terms of payload capacity and stego quality. A new technique based on a specific representation is proposed to decompose pixel intensity values into 16 (virtual) bit-planes suitable for embedding purposes. The proposed decomposition has a desirable property whereby the sum of all bit-planes does not exceed the maximum pixel intensity value, i.e. 255. Experimental results demonstrate that the proposed technique offers an effective compromise between payload capacity and stego quality of existing embedding techniques based on pixel intensity value decomposition. Its capacity is equal to that of binary and Lucas, while it offers a higher capacity than Fibonacci, Prime, Natural, and CF when the secret bits are embedded in 1st Least Significant Bit (LSB). When the secret bits are embedded in higher bit-planes, i.e., 2nd LSB to 8th Most Significant Bit (MSB), the proposed scheme has more capacity than Natural numbers based embedding. However, from the 6th bit-plane onwards, the proposed scheme offers better stego quality. In general, the proposed decomposition scheme has less effect in terms of quality on pixel value when compared to most existing pixel intensity value decomposition techniques when embedding messages in higher bit-planes.

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.

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

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Chun Wang

2017-01-01

Full Text Available A novel multiobjective memetic algorithm based on decomposition (MOMAD is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP, which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of MOEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament mechanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.

An Optimal Strategy for Accurate Bulge-to-disk Decomposition of Disk Galaxies

Energy Technology Data Exchange (ETDEWEB)

Gao Hua [Department of Astronomy, School of Physics, Peking University, Beijing 100871 (China); Ho, Luis C. [Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871 (China)

2017-08-20

The development of two-dimensional (2D) bulge-to-disk decomposition techniques has shown their advantages over traditional one-dimensional (1D) techniques, especially for galaxies with non-axisymmetric features. However, the full potential of 2D techniques has yet to be fully exploited. Secondary morphological features in nearby disk galaxies, such as bars, lenses, rings, disk breaks, and spiral arms, are seldom accounted for in 2D image decompositions, even though some image-fitting codes, such as GALFIT, are capable of handling them. We present detailed, 2D multi-model and multi-component decomposition of high-quality R -band images of a representative sample of nearby disk galaxies selected from the Carnegie-Irvine Galaxy Survey, using the latest version of GALFIT. The sample consists of five barred and five unbarred galaxies, spanning Hubble types from S0 to Sc. Traditional 1D decomposition is also presented for comparison. In detailed case studies of the 10 galaxies, we successfully model the secondary morphological features. Through a comparison of best-fit parameters obtained from different input surface brightness models, we identify morphological features that significantly impact bulge measurements. We show that nuclear and inner lenses/rings and disk breaks must be properly taken into account to obtain accurate bulge parameters, whereas outer lenses/rings and spiral arms have a negligible effect. We provide an optimal strategy to measure bulge parameters of typical disk galaxies, as well as prescriptions to estimate realistic uncertainties of them, which will benefit subsequent decomposition of a larger galaxy sample.

An Optimal Strategy for Accurate Bulge-to-disk Decomposition of Disk Galaxies

Science.gov (United States)

Gao, Hua; Ho, Luis C.

2017-08-01

The development of two-dimensional (2D) bulge-to-disk decomposition techniques has shown their advantages over traditional one-dimensional (1D) techniques, especially for galaxies with non-axisymmetric features. However, the full potential of 2D techniques has yet to be fully exploited. Secondary morphological features in nearby disk galaxies, such as bars, lenses, rings, disk breaks, and spiral arms, are seldom accounted for in 2D image decompositions, even though some image-fitting codes, such as GALFIT, are capable of handling them. We present detailed, 2D multi-model and multi-component decomposition of high-quality R-band images of a representative sample of nearby disk galaxies selected from the Carnegie-Irvine Galaxy Survey, using the latest version of GALFIT. The sample consists of five barred and five unbarred galaxies, spanning Hubble types from S0 to Sc. Traditional 1D decomposition is also presented for comparison. In detailed case studies of the 10 galaxies, we successfully model the secondary morphological features. Through a comparison of best-fit parameters obtained from different input surface brightness models, we identify morphological features that significantly impact bulge measurements. We show that nuclear and inner lenses/rings and disk breaks must be properly taken into account to obtain accurate bulge parameters, whereas outer lenses/rings and spiral arms have a negligible effect. We provide an optimal strategy to measure bulge parameters of typical disk galaxies, as well as prescriptions to estimate realistic uncertainties of them, which will benefit subsequent decomposition of a larger galaxy sample.

An Optimal Strategy for Accurate Bulge-to-disk Decomposition of Disk Galaxies

International Nuclear Information System (INIS)

Gao Hua; Ho, Luis C.

2017-01-01

The development of two-dimensional (2D) bulge-to-disk decomposition techniques has shown their advantages over traditional one-dimensional (1D) techniques, especially for galaxies with non-axisymmetric features. However, the full potential of 2D techniques has yet to be fully exploited. Secondary morphological features in nearby disk galaxies, such as bars, lenses, rings, disk breaks, and spiral arms, are seldom accounted for in 2D image decompositions, even though some image-fitting codes, such as GALFIT, are capable of handling them. We present detailed, 2D multi-model and multi-component decomposition of high-quality R -band images of a representative sample of nearby disk galaxies selected from the Carnegie-Irvine Galaxy Survey, using the latest version of GALFIT. The sample consists of five barred and five unbarred galaxies, spanning Hubble types from S0 to Sc. Traditional 1D decomposition is also presented for comparison. In detailed case studies of the 10 galaxies, we successfully model the secondary morphological features. Through a comparison of best-fit parameters obtained from different input surface brightness models, we identify morphological features that significantly impact bulge measurements. We show that nuclear and inner lenses/rings and disk breaks must be properly taken into account to obtain accurate bulge parameters, whereas outer lenses/rings and spiral arms have a negligible effect. We provide an optimal strategy to measure bulge parameters of typical disk galaxies, as well as prescriptions to estimate realistic uncertainties of them, which will benefit subsequent decomposition of a larger galaxy sample.

A new decomposition method for parallel processing multi-level optimization

International Nuclear Information System (INIS)

Park, Hyung Wook; Kim, Min Soo; Choi, Dong Hoon

2002-01-01

In practical designs, most of the multidisciplinary problems have a large-size and complicate design system. Since multidisciplinary problems have hundreds of analyses and thousands of variables, the grouping of analyses and the order of the analyses in the group affect the speed of the total design cycle. Therefore, it is very important to reorder and regroup the original design processes in order to minimize the total computational cost by decomposing large multidisciplinary problems into several MultiDisciplinary Analysis SubSystems (MDASS) and by processing them in parallel. In this study, a new decomposition method is proposed for parallel processing of multidisciplinary design optimization, such as Collaborative Optimization (CO) and Individual Discipline Feasible (IDF) method. Numerical results for two example problems are presented to show the feasibility of the proposed method

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)

Presentation of a Modified Boustrophedon Decomposition Algorithm for Optimal Configuration of Flat Fields to use in Path Planning Systems of Agricultural Vehicles

Directory of Open Access Journals (Sweden)

R Goudarzi

2018-03-01

Full Text Available Introduction The demand of pre-determined optimal coverage paths in agricultural environments have been increased due to the growing application of field robots and autonomous field machines. Also coverage path planning problem (CPP has been extensively studied in robotics and many algorithms have been provided in many topics, but differences and limitations in agriculture lead to several different heuristic and modified adaptive methods from robotics. In this paper, a modified and enhanced version of currently used decomposition algorithm in robotics (boustrophedon cellular decomposition has been presented as a main part of path planning systems of agricultural vehicles. Developed algorithm is based on the parallelization of the edges of the polygon representing the environment to satisfy the requirements of the problem as far as possible. This idea is based on "minimum facing to the cost making condition" in turn, it is derived from encounter concept as a basis of cost making factors. Materials and Methods Generally, a line termed as a slice in boustrophedon cellular decomposition (BCD, sweeps an area in a pre-determined direction and decomposes the area only at critical points (where two segments can be extended to top and bottom of the point. Furthermore, sweep line direction does not change until the decomposition finish. To implement the BCD for parallelization method, two modifications were applied in order to provide a modified version of the boustrophedon cellular decomposition (M-BCD. In the first modification, the longest edge (base edge is targeted, and sweep line direction is set in line with the base edge direction (sweep direction is set perpendicular to the sweep line direction. Then Sweep line moves through the environment and stops at the first (nearest critical point. Next sweep direction will be the same as previous, If the length of those polygon's newly added edges, during the decomposition, are less than or equal to the

Design of tailor-made chemical blend using a decomposition-based computer-aided approach

DEFF Research Database (Denmark)

Yunus, Nor Alafiza; Gernaey, Krist; Manan, Z.A.

2011-01-01

Computer aided techniques form an efficient approach to solve chemical product design problems such as the design of blended liquid products (chemical blending). In chemical blending, one tries to find the best candidate, which satisfies the product targets defined in terms of desired product...... methodology for blended liquid products that identifies a set of feasible chemical blends. The blend design problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model where the objective is to find the optimal blended gasoline or diesel product subject to types of chemicals...... and their compositions and a set of desired target properties of the blended product as design constraints. This blend design problem is solved using a decomposition approach, which eliminates infeasible and/or redundant candidates gradually through a hierarchy of (property) model based constraints. This decomposition...

Post-decomposition optimizations using pattern matching and rule-based clustering for multi-patterning technology

Science.gov (United States)

Wang, Lynn T.-N.; Madhavan, Sriram

2018-03-01

A pattern matching and rule-based polygon clustering methodology with DFM scoring is proposed to detect decomposition-induced manufacturability detractors and fix the layout designs prior to manufacturing. A pattern matcher scans the layout for pre-characterized patterns from a library. If a pattern were detected, rule-based clustering identifies the neighboring polygons that interact with those captured by the pattern. Then, DFM scores are computed for the possible layout fixes: the fix with the best score is applied. The proposed methodology was applied to two 20nm products with a chip area of 11 mm2 on the metal 2 layer. All the hotspots were resolved. The number of DFM spacing violations decreased by 7-15%.

Optimization and kinetics decomposition of monazite using NaOH

International Nuclear Information System (INIS)

MV Purwani; Suyanti; Deddy Husnurrofiq

2015-01-01

Decomposition of monazite with NaOH has been done. Decomposition performed at high temperature on furnace. The parameters studied were the comparison NaOH / monazite, temperature and time decomposition. From the research decomposition for 100 grams of monazite with NaOH, it can be concluded that the greater the ratio of NaOH / monazite, the greater the conversion. In the temperature influences decomposition 400 - 700°C, the greater the reaction rate constant with increasing temperature greater decomposition. Comparison NaOH / monazite optimum was 1.5 and the optimum time of 3 hours. Relations ratio NaOH / monazite with conversion (x) following the polynomial equation y = 0.1579x 2 – 0.2855x + 0.8301 (y = conversion and x = ratio of NaOH/monazite). Decomposition reaction of monazite with NaOH was second orde reaction, the relationship between temperature (T) with a reaction rate constant (k), k = 6.106.e - 1006.8 /T or ln k = - 1006.8/T + 6.106, frequency factor A = 448.541, activation energy E = 8.371 kJ/mol. (author)

Decomposition techniques in mathematical programming engineering and science applications

CERN Document Server

Conejo, Antonio J; Minguez, Roberto; Garcia-Bertrand, Raquel

2006-01-01

Optimization plainly dominates the design, planning, operation, and c- trol of engineering systems. This is a book on optimization that considers particular cases of optimization problems, those with a decomposable str- ture that can be advantageously exploited. Those decomposable optimization problems are ubiquitous in engineering and science applications. The book considers problems with both complicating constraints and complicating va- ables, and analyzes linear and nonlinear problems, with and without in- ger variables. The decomposition techniques analyzed include Dantzig-Wolfe, Benders, Lagrangian relaxation, Augmented Lagrangian decomposition, and others. Heuristic techniques are also considered. Additionally, a comprehensive sensitivity analysis for characterizing the solution of optimization problems is carried out. This material is particularly novel and of high practical interest. This book is built based on many clarifying, illustrative, and compu- tional examples, which facilitate the learning p...

Benders’ Decomposition for Curriculum-Based Course Timetabling

DEFF Research Database (Denmark)

Bagger, Niels-Christian F.; Sørensen, Matias; Stidsen, Thomas R.

2018-01-01

feasibility. We compared our algorithm with other approaches from the literature for a total of 32 data instances. We obtained a lower bound on 23 of the instances, which were at least as good as the lower bounds obtained by the state-of-the-art, and on eight of these, our lower bounds were higher. On two......In this paper we applied Benders’ decomposition to the Curriculum-Based Course Timetabling (CBCT) problem. The objective of the CBCT problem is to assign a set of lectures to time slots and rooms. Our approach was based on segmenting the problem into time scheduling and room allocation problems...... of the instances, our lower bound was an improvement of the currently best-known. Lastly, we compared our decomposition to the model without the decomposition on an additional six instances, which are much larger than the other 32. To our knowledge, this was the first time that lower bounds were calculated...

APPROACH ON INTELLIGENT OPTIMIZATION DESIGN BASED ON COMPOUND KNOWLEDGE

Institute of Scientific and Technical Information of China (English)

Yao Jianchu; Zhou Ji; Yu Jun

2003-01-01

A concept of an intelligent optimal design approach is proposed, which is organized by a kind of compound knowledge model. The compound knowledge consists of modularized quantitative knowledge, inclusive experience knowledge and case-based sample knowledge. By using this compound knowledge model, the abundant quantity information of mathematical programming and the symbolic knowledge of artificial intelligence can be united together in this model. The intelligent optimal design model based on such a compound knowledge and the automatically generated decomposition principles based on it are also presented. Practically, it is applied to the production planning, process schedule and optimization of production process of a refining & chemical work and a great profit is achieved. Specially, the methods and principles are adaptable not only to continuous process industry, but also to discrete manufacturing one.

Optimization of wavelet decomposition for image compression and feature preservation.

Science.gov (United States)

Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T

2003-09-01

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

Tree decomposition based fast search of RNA structures including pseudoknots in genomes.

Science.gov (United States)

Song, Yinglei; Liu, Chunmei; Malmberg, Russell; Pan, Fangfang; Cai, Liming

2005-01-01

Searching genomes for RNA secondary structure with computational methods has become an important approach to the annotation of non-coding RNAs. However, due to the lack of efficient algorithms for accurate RNA structure-sequence alignment, computer programs capable of fast and effectively searching genomes for RNA secondary structures have not been available. In this paper, a novel RNA structure profiling model is introduced based on the notion of a conformational graph to specify the consensus structure of an RNA family. Tree decomposition yields a small tree width t for such conformation graphs (e.g., t = 2 for stem loops and only a slight increase for pseudo-knots). Within this modelling framework, the optimal alignment of a sequence to the structure model corresponds to finding a maximum valued isomorphic subgraph and consequently can be accomplished through dynamic programming on the tree decomposition of the conformational graph in time O(k(t)N(2)), where k is a small parameter; and N is the size of the projiled RNA structure. Experiments show that the application of the alignment algorithm to search in genomes yields the same search accuracy as methods based on a Covariance model with a significant reduction in computation time. In particular; very accurate searches of tmRNAs in bacteria genomes and of telomerase RNAs in yeast genomes can be accomplished in days, as opposed to months required by other methods. The tree decomposition based searching tool is free upon request and can be downloaded at our site h t t p ://w.uga.edu/RNA-informatics/software/index.php.

An ill-conditioning conformal radiotherapy analysis based on singular values decomposition

International Nuclear Information System (INIS)

Lefkopoulos, D.; Grandjean, P.; Bendada, S.; Dominique, C.; Platoni, K.; Schlienger, M.

1995-01-01

Clinical experience in stereotactic radiotherapy of irregular complex lesions had shown that optimization algorithms were necessary to improve the dose distribution. We have developed a general optimization procedure which can be applied to different conformal irradiation techniques. In this presentation this procedure is tested on the stereotactic radiotherapy modality of complex cerebral lesions treated with multi-isocentric technique based on the 'associated targets methodology'. In this inverse procedure we use the singular value decomposition (SVD) analysis which proposes several optimal solutions for the narrow beams weights of each isocentre. The SVD analysis quantifies the ill-conditioning of the dosimetric calculation of the stereotactic irradiation, using the condition number which is the ratio of the bigger to smaller singular values. Our dose distribution optimization approach consists on the study of the irradiation parameters influence on the stereotactic radiotherapy inverse problem. The adjustment of the different irradiation parameters into the 'SVD optimizer' procedure is realized taking into account the ratio of the quality reconstruction to the time calculation. It will permit a more efficient use of the 'SVD optimizer' in clinical applications for real 3D lesions. The evaluation criteria for the choice of satisfactory solutions are based on the dose-volume histograms and clinical considerations. We will present the efficiency of ''SVD optimizer'' to analyze and predict the ill-conditioning in stereotactic radiotherapy and to recognize the topography of the different beams in order to create optimal reconstructed weighting vector. The planification of stereotactic treatments using the ''SVD optimizer'' is examined for mono-isocentrically and complex dual-isocentrically treated lesions. The application of the SVD optimization technique provides conformal dose distribution for complex intracranial lesions. It is a general optimization procedure

Eigenvalue Decomposition-Based Modified Newton Algorithm

Directory of Open Access Journals (Sweden)

Wen-jun Wang

2013-01-01

Full Text Available When the Hessian matrix is not positive, the Newton direction may not be the descending direction. A new method named eigenvalue decomposition-based modified Newton algorithm is presented, which first takes the eigenvalue decomposition of the Hessian matrix, then replaces the negative eigenvalues with their absolute values, and finally reconstructs the Hessian matrix and modifies the searching direction. The new searching direction is always the descending direction. The convergence of the algorithm is proven and the conclusion on convergence rate is presented qualitatively. Finally, a numerical experiment is given for comparing the convergence domains of the modified algorithm and the classical algorithm.

Asynchronous Task-Based Polar Decomposition on Manycore Architectures

KAUST Repository

Sukkari, Dalal

2016-10-25

This paper introduces the first asynchronous, task-based implementation of the polar decomposition on manycore architectures. Based on a new formulation of the iterative QR dynamically-weighted Halley algorithm (QDWH) for the calculation of the polar decomposition, the proposed implementation replaces the original and hostile LU factorization for the condition number estimator by the more adequate QR factorization to enable software portability across various architectures. Relying on fine-grained computations, the novel task-based implementation is also capable of taking advantage of the identity structure of the matrix involved during the QDWH iterations, which decreases the overall algorithmic complexity. Furthermore, the artifactual synchronization points have been severely weakened compared to previous implementations, unveiling look-ahead opportunities for better hardware occupancy. The overall QDWH-based polar decomposition can then be represented as a directed acyclic graph (DAG), where nodes represent computational tasks and edges define the inter-task data dependencies. The StarPU dynamic runtime system is employed to traverse the DAG, to track the various data dependencies and to asynchronously schedule the computational tasks on the underlying hardware resources, resulting in an out-of-order task scheduling. Benchmarking experiments show significant improvements against existing state-of-the-art high performance implementations (i.e., Intel MKL and Elemental) for the polar decomposition on latest shared-memory vendors\\' systems (i.e., Intel Haswell/Broadwell/Knights Landing, NVIDIA K80/P100 GPUs and IBM Power8), while maintaining high numerical accuracy.

Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

Directory of Open Access Journals (Sweden)

Hui Lu

2014-01-01

Full Text Available Test task scheduling problem (TTSP is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme (IES to increase the efficiency. Then ten chaotic maps are applied into the multiobjective evolutionary algorithm based on decomposition (MOEA/D in three phases, that is, initial population and crossover and mutation operators. To identify a good approach for hybrid MOEA/D and chaos and indicate the effectiveness of the improving IES several experiments are performed. The Pareto front and the statistical results demonstrate that different chaotic maps in different phases have different effects for solving the TTSP especially the circle map and ICMIC map. The similarity degree of distribution between chaotic maps and the problem is a very essential factor for the application of chaotic maps. In addition, the experiments of comparisons of CMOEA/D and variable neighborhood MOEA/D (VNM indicate that our algorithm has the best performance in solving the TTSP.

Hydrothermal decomposition of liquid crystal in subcritical water

International Nuclear Information System (INIS)

Zhuang, Xuning; He, Wenzhi; Li, Guangming; Huang, Juwen; Lu, Shangming; Hou, Lianjiao

2014-01-01

Highlights: • Hydrothermal technology can effectively decompose the liquid crystal of 4-octoxy-4'-cyanobiphenyl. • The decomposition rate reached 97.6% under the optimized condition. • Octoxy-4'-cyanobiphenyl was mainly decomposed into simple and innocuous products. • The mechanism analysis reveals the decomposition reaction process. - Abstract: Treatment of liquid crystal has important significance for the environment protection and human health. This study proposed a hydrothermal process to decompose the liquid crystal of 4-octoxy-4′-cyanobiphenyl. Experiments were conducted with a 5.7 mL stainless tube reactor and heated by a salt-bath. Factors affecting the decomposition rate of 4-octoxy-4′-cyanobiphenyl were evaluated with HPLC. The decomposed liquid products were characterized by GC-MS. Under optimized conditions i.e., 0.2 mL H 2 O 2 supply, pH value 6, temperature 275 °C and reaction time 5 min, 97.6% of 4-octoxy-4′-cyanobiphenyl was decomposed into simple and environment-friendly products. Based on the mechanism analysis and products characterization, a possible hydrothermal decomposition pathway was proposed. The results indicate that hydrothermal technology is a promising choice for liquid crystal treatment

Sparse time-frequency decomposition based on dictionary adaptation.

Science.gov (United States)

Hou, Thomas Y; Shi, Zuoqiang

2016-04-13

In this paper, we propose a time-frequency analysis method to obtain instantaneous frequencies and the corresponding decomposition by solving an optimization problem. In this optimization problem, the basis that is used to decompose the signal is not known a priori. Instead, it is adapted to the signal and is determined as part of the optimization problem. In this sense, this optimization problem can be seen as a dictionary adaptation problem, in which the dictionary is adaptive to one signal rather than a training set in dictionary learning. This dictionary adaptation problem is solved by using the augmented Lagrangian multiplier (ALM) method iteratively. We further accelerate the ALM method in each iteration by using the fast wavelet transform. We apply our method to decompose several signals, including signals with poor scale separation, signals with outliers and polluted by noise and a real signal. The results show that this method can give accurate recovery of both the instantaneous frequencies and the intrinsic mode functions. © 2016 The Author(s).

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach

Science.gov (United States)

Tarai, Madhumita; Kumar, Keshav; Divya, O.; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

2017-09-01

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

Empirical projection-based basis-component decomposition method

Science.gov (United States)

Brendel, Bernhard; Roessl, Ewald; Schlomka, Jens-Peter; Proksa, Roland

2009-02-01

Advances in the development of semiconductor based, photon-counting x-ray detectors stimulate research in the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition can be performed in which in addition to the conventional approach of Alvarez and Macovski a third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition of the measured projection data into the basis component projections, conventional filtered-backprojection reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition was obtained by maximizing the likelihood-function of the measurements. This procedure is time consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector. Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection domain. We compare this empirical approach with the maximum-likelihood (ML) approach considering image noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered a viable alternative to the ML approach.

Avoiding spurious submovement decompositions: a globally optimal algorithm

International Nuclear Information System (INIS)

Rohrer, Brandon Robinson; Hogan, Neville

2003-01-01

Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to extract them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.

Dose optimization for dual-energy contrast-enhanced digital mammography based on an energy-resolved photon-counting detector: A Monte Carlo simulation study

Science.gov (United States)

Lee, Youngjin; Lee, Seungwan; Kang, Sooncheol; Eom, Jisoo

2017-03-01

Dual-energy contrast-enhanced digital mammography (CEDM) has been used to decompose breast images and improve diagnostic accuracy for tumor detection. However, this technique causes an increase of radiation dose and an inaccuracy in material decomposition due to the limitations of conventional X-ray detectors. In this study, we simulated the dual-energy CEDM with an energy-resolved photon-counting detector (ERPCD) for reducing radiation dose and improving the quantitative accuracy of material decomposition images. The ERPCD-based dual-energy CEDM was compared to the conventional dual-energy CEDM in terms of radiation dose and quantitative accuracy. The correlation between radiation dose and image quality was also evaluated for optimizing the ERPCD-based dual-energy CEDM technique. The results showed that the material decomposition errors of the ERPCD-based dual-energy CEDM were 0.56-0.67 times lower than those of the conventional dual-energy CEDM. The imaging performance of the proposed technique was optimized at the radiation dose of 1.09 mGy, which is a half of the MGD for a single view mammogram. It can be concluded that the ERPCD-based dual-energy CEDM with an optimal exposure level is able to improve the quality of material decomposition images as well as reduce radiation dose.

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

KAUST Repository

Sukkari, Dalal E.; Ltaief, Hatem; Faverge, Mathieu; Keyes, David E.

2017-01-01

This paper introduces the first asynchronous, task-based formulation of the polar decomposition and its corresponding implementation on manycore architectures. Based on a formulation of the iterative QR dynamically-weighted Halley algorithm (QDWH) for the calculation of the polar decomposition, the proposed implementation replaces the original LU factorization for the condition number estimator by the more adequate QR factorization to enable software portability across various architectures. Relying on fine-grained computations, the novel task-based implementation is capable of taking advantage of the identity structure of the matrix involved during the QDWH iterations, which decreases the overall algorithmic complexity. Furthermore, the artifactual synchronization points have been weakened compared to previous implementations, unveiling look-ahead opportunities for better hardware occupancy. The overall QDWH-based polar decomposition can then be represented as a directed acyclic graph (DAG), where nodes represent computational tasks and edges define the inter-task data dependencies. The StarPU dynamic runtime system is employed to traverse the DAG, to track the various data dependencies and to asynchronously schedule the computational tasks on the underlying hardware resources, resulting in an out-of-order task scheduling. Benchmarking experiments show significant improvements against existing state-of-the-art high performance implementations for the polar decomposition on latest shared-memory vendors' systems, while maintaining numerical accuracy.

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

KAUST Repository

Sukkari, Dalal E.

2017-09-29

This paper introduces the first asynchronous, task-based formulation of the polar decomposition and its corresponding implementation on manycore architectures. Based on a formulation of the iterative QR dynamically-weighted Halley algorithm (QDWH) for the calculation of the polar decomposition, the proposed implementation replaces the original LU factorization for the condition number estimator by the more adequate QR factorization to enable software portability across various architectures. Relying on fine-grained computations, the novel task-based implementation is capable of taking advantage of the identity structure of the matrix involved during the QDWH iterations, which decreases the overall algorithmic complexity. Furthermore, the artifactual synchronization points have been weakened compared to previous implementations, unveiling look-ahead opportunities for better hardware occupancy. The overall QDWH-based polar decomposition can then be represented as a directed acyclic graph (DAG), where nodes represent computational tasks and edges define the inter-task data dependencies. The StarPU dynamic runtime system is employed to traverse the DAG, to track the various data dependencies and to asynchronously schedule the computational tasks on the underlying hardware resources, resulting in an out-of-order task scheduling. Benchmarking experiments show significant improvements against existing state-of-the-art high performance implementations for the polar decomposition on latest shared-memory vendors\\' systems, while maintaining numerical accuracy.

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach.

Science.gov (United States)

Tarai, Madhumita; Kumar, Keshav; Divya, O; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

2017-09-05

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix. Copyright © 2017 Elsevier B.V. All rights reserved.

Dose optimization for dual-energy contrast-enhanced digital mammography based on an energy-resolved photon-counting detector: A Monte Carlo simulation study

International Nuclear Information System (INIS)

Lee, Youngjin; Lee, Seungwan; Kang, Sooncheol; Eom, Jisoo

2017-01-01

Dual-energy contrast-enhanced digital mammography (CEDM) has been used to decompose breast images and improve diagnostic accuracy for tumor detection. However, this technique causes an increase of radiation dose and an inaccuracy in material decomposition due to the limitations of conventional X-ray detectors. In this study, we simulated the dual-energy CEDM with an energy-resolved photon-counting detector (ERPCD) for reducing radiation dose and improving the quantitative accuracy of material decomposition images. The ERPCD-based dual-energy CEDM was compared to the conventional dual-energy CEDM in terms of radiation dose and quantitative accuracy. The correlation between radiation dose and image quality was also evaluated for optimizing the ERPCD-based dual-energy CEDM technique. The results showed that the material decomposition errors of the ERPCD-based dual-energy CEDM were 0.56–0.67 times lower than those of the conventional dual-energy CEDM. The imaging performance of the proposed technique was optimized at the radiation dose of 1.09 mGy, which is a half of the MGD for a single view mammogram. It can be concluded that the ERPCD-based dual-energy CEDM with an optimal exposure level is able to improve the quality of material decomposition images as well as reduce radiation dose. - Highlights: • Dual-energy mammography based on a photon-counting detector was simulated. • Radiation dose and image quality were evaluated for optimizing the proposed technique. • The proposed technique reduced radiation dose as well as improved image quality. • The proposed technique was optimized at the radiation dose of 1.09 mGy.

Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition.

Science.gov (United States)

Lu, Lijun; Ma, Xiaomian; Mohy-Ud-Din, Hassan; Ma, Jianhua; Feng, Qianjin; Rahmim, Arman; Chen, Wufan

2018-02-01

The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images. We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic 82 Rb MP PET scan data, we optimized and compared its performance with other restoration methods. The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive 82 Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac 82 Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms. The proposed method is effective for restoration and enhancement of dynamic PET images. Copyright © 2017 Elsevier B.V. All rights reserved.

Structural system identification based on variational mode decomposition

Science.gov (United States)

Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.

2018-03-01

In this paper, a new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD). The VMD approach is a decomposition algorithm that has been developed as a means to overcome some of the drawbacks and limitations of the empirical mode decomposition method. The VMD-based modal identification algorithm decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies, such that when combined their cumulative modal responses reproduce the original acceleration response. The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data. Finally, after extracting modal responses from available sensors, the mode shape vector for each of the decomposed modes in the system is identified from all obtained modal response data. To demonstrate the efficiency of the algorithm, a series of numerical, laboratory, and field case studies were evaluated. The laboratory case study utilized the vibration response of a three-story shear frame, whereas the field study leveraged the ambient vibration response of a pedestrian bridge to characterize the modal properties of the structure. The modal properties of the shear frame were computed using analytical approach for a comparison with the experimental modal frequencies. Results from these case studies demonstrated that the proposed method is efficient and accurate in identifying modal data of the structures.

TRUST MODEL FOR SOCIAL NETWORK USING SINGULAR VALUE DECOMPOSITION

Directory of Open Access Journals (Sweden)

Davis Bundi Ntwiga

2016-06-01

Full Text Available For effective interactions to take place in a social network, trust is important. We model trust of agents using the peer to peer reputation ratings in the network that forms a real valued matrix. Singular value decomposition discounts the reputation ratings to estimate the trust levels as trust is the subjective probability of future expectations based on current reputation ratings. Reputation and trust are closely related and singular value decomposition can estimate trust using the real valued matrix of the reputation ratings of the agents in the network. Singular value decomposition is an ideal technique in error elimination when estimating trust from reputation ratings. Reputation estimation of trust is optimal at the discounting of 20 %.

A study on optimal task decomposition of networked parallel computing using PVM

International Nuclear Information System (INIS)

Seong, Kwan Jae; Kim, Han Gyoo

1998-01-01

A numerical study is performed to investigate the effect of task decomposition on networked parallel processes using Parallel Virtual Machine (PVM). In our study, a PVM program distributed over a network of workstations is used in solving a finite difference version of a one dimensional heat equation, where natural choice of PVM programming structure would be the master-slave paradigm, with the aim of finding an optimal configuration resulting in least computing time including communication overhead among machines. Given a set of PVM tasks comprised of one master and five slave programs, it is found that there exists a pseudo-optimal number of machines, which does not necessarily coincide with the number of tasks, that yields the best performance when the network is under a light usage. Increasing the number of machines beyond this optimal one does not improve computing performance since increase in communication overhead among the excess number of machines offsets the decrease in CPU time obtained by distributing the PVM tasks among these machines. However, when the network traffic is heavy, the results exhibit a more random characteristic that is explained by the random nature of data transfer time

Danburite decomposition by hydrochloric acid

International Nuclear Information System (INIS)

Mamatov, E.D.; Ashurov, N.A.; Mirsaidov, U.

2011-01-01

Present article is devoted to decomposition of danburite of Ak-Arkhar Deposit of Tajikistan by hydrochloric acid. The interaction of boron containing ores of Ak-Arkhar Deposit of Tajikistan with mineral acids, including hydrochloric acid was studied. The optimal conditions of extraction of valuable components from danburite composition were determined. The chemical composition of danburite of Ak-Arkhar Deposit was determined as well. The kinetics of decomposition of calcined danburite by hydrochloric acid was studied. The apparent activation energy of the process of danburite decomposition by hydrochloric acid was calculated.

INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization

Energy Technology Data Exchange (ETDEWEB)

Groer, Christopher S [ORNL; Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL

2012-10-01

It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.

Optimization of large-scale industrial systems : an emerging method

Energy Technology Data Exchange (ETDEWEB)

Hammache, A.; Aube, F.; Benali, M.; Cantave, R. [Natural Resources Canada, Varennes, PQ (Canada). CANMET Energy Technology Centre

2006-07-01

This paper reviewed optimization methods of large-scale industrial production systems and presented a novel systematic multi-objective and multi-scale optimization methodology. The methodology was based on a combined local optimality search with global optimality determination, and advanced system decomposition and constraint handling. The proposed method focused on the simultaneous optimization of the energy, economy and ecology aspects of industrial systems (E{sup 3}-ISO). The aim of the methodology was to provide guidelines for decision-making strategies. The approach was based on evolutionary algorithms (EA) with specifications including hybridization of global optimality determination with a local optimality search; a self-adaptive algorithm to account for the dynamic changes of operating parameters and design variables occurring during the optimization process; interactive optimization; advanced constraint handling and decomposition strategy; and object-oriented programming and parallelization techniques. Flowcharts of the working principles of the basic EA were presented. It was concluded that the EA uses a novel decomposition and constraint handling technique to enhance the Pareto solution search procedure for multi-objective problems. 6 refs., 9 figs.

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

Science.gov (United States)

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

Implementation of QR-decomposition based on CORDIC for unitary MUSIC algorithm

Science.gov (United States)

Lounici, Merwan; Luan, Xiaoming; Saadi, Wahab

2013-07-01

The DOA (Direction Of Arrival) estimation with subspace methods such as MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) is based on an accurate estimation of the eigenvalues and eigenvectors of covariance matrix. QR decomposition is implemented with the Coordinate Rotation DIgital Computer (CORDIC) algorithm. QRD requires only additions and shifts [6], so it is faster and more regular than other methods. In this article the hardware architecture of an EVD (Eigen Value Decomposition) processor based on TSA (triangular systolic array) for QR decomposition is proposed. Using Xilinx System Generator (XSG), the design is implemented and the estimated logic device resource values are presented for different matrix sizes.

Ozone decomposition

Directory of Open Access Journals (Sweden)

Batakliev Todor

2014-06-01

Full Text Available Catalytic ozone decomposition is of great significance because ozone is a toxic substance commonly found or generated in human environments (aircraft cabins, offices with photocopiers, laser printers, sterilizers. Considerable work has been done on ozone decomposition reported in the literature. This review provides a comprehensive summary of the literature, concentrating on analysis of the physico-chemical properties, synthesis and catalytic decomposition of ozone. This is supplemented by a review on kinetics and catalyst characterization which ties together the previously reported results. Noble metals and oxides of transition metals have been found to be the most active substances for ozone decomposition. The high price of precious metals stimulated the use of metal oxide catalysts and particularly the catalysts based on manganese oxide. It has been determined that the kinetics of ozone decomposition is of first order importance. A mechanism of the reaction of catalytic ozone decomposition is discussed, based on detailed spectroscopic investigations of the catalytic surface, showing the existence of peroxide and superoxide surface intermediates

A Benders decomposition approach for a combined heat and power economic dispatch

International Nuclear Information System (INIS)

Abdolmohammadi, Hamid Reza; Kazemi, Ahad

2013-01-01

Highlights: • Benders decomposition algorithm to solve combined heat and power economic dispatch. • Decomposing the CHPED problem into master problem and subproblem. • Considering non-convex heat-power feasible region efficiently. • Solving 4 units and 5 units system with 2 and 3 co-generation units, respectively. • Obtaining better or as well results in terms of objective values. - Abstract: Recently, cogeneration units have played an increasingly important role in the utility industry. Therefore the optimal utilization of multiple combined heat and power (CHP) systems is an important optimization task in power system operation. Unlike power economic dispatch, which has a single equality constraint, two equality constraints must be met in combined heat and power economic dispatch (CHPED) problem. Moreover, in the cogeneration units, the power capacity limits are functions of the unit heat productions and the heat capacity limits are functions of the unit power generations. Thus, CHPED is a complicated optimization problem. In this paper, an algorithm based on Benders decomposition (BD) is proposed to solve the economic dispatch (ED) problem for cogeneration systems. In the proposed method, combined heat and power economic dispatch problem is decomposed into a master problem and subproblem. The subproblem generates the Benders cuts and master problem uses them as a new inequality constraint which is added to the previous constraints. The iterative process will continue until upper and lower bounds of the objective function optimal values are close enough and a converged optimal solution is found. Benders decomposition based approach is able to provide a good framework to consider the non-convex feasible operation regions of cogeneration units efficiently. In this paper, a four-unit system with two cogeneration units and a five-unit system with three cogeneration units are analyzed to exhibit the effectiveness of the proposed approach. In all cases, the

Effects of catalyst-bed’s structure parameters on decomposition and combustion characteristics of an ammonium dinitramide (ADN)-based thruster

International Nuclear Information System (INIS)

Yu, Yu-Song; Li, Guo-Xiu; Zhang, Tao; Chen, Jun; Wang, Meng

2015-01-01

Highlights: • The decomposition and combustion process is investigated by numerical method. • Heat transfer in catalyst bed is modeled using non-isothermal and radiation model. • The wall heat transfer can impact on the distribution of temperature and species. • The value of catalyst bed length, diameter and wall thickness are optimized. - Abstract: The present investigation numerically studies the evolutions of decomposition and combustion within an ADN-based thruster, and the effects of the catalyst-bed’s three structure parameters (length, diameter, and wall thickness) on the general performance of ADN-based thruster have been systematically investigated. Based upon the calculated results, it can be known that the distribution of temperature gives a Gaussian manner at the exits of the catalyst-bed and the combustion chamber, and the temperature can be obviously effected by each the three structure parameters of the catalyst-bed. With the rise of each the three structure parameter, the temperature will first increases and decreases, and there exists an optimal design value making the temperature be the highest. Via the comparison on the maximal temperature at combustion chamber’s exit and the specific impulse, it can be obtained that the wall thickness plays an important role in the influences on the general performance of ADN-based thruster while the catalyst-bed’s length has the weak effects on the general performance among the three structure parameters.

Kernel based pattern analysis methods using eigen-decompositions for reading Icelandic sagas

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Carstensen, Jens Michael

We want to test the applicability of kernel based eigen-decomposition methods, compared to the traditional eigen-decomposition methods. We have implemented and tested three kernel based methods methods, namely PCA, MAF and MNF, all using a Gaussian kernel. We tested the methods on a multispectral...... image of a page in the book 'hauksbok', which contains Icelandic sagas....

Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

Science.gov (United States)

Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

2013-05-01

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition

Directory of Open Access Journals (Sweden)

Qinghua Xie

2017-01-01

Full Text Available Recently, a general polarimetric model-based decomposition framework was proposed by Chen et al., which addresses several well-known limitations in previous decomposition methods and implements a simultaneous full-parameter inversion by using complete polarimetric information. However, it only employs four typical models to characterize the volume scattering component, which limits the parameter inversion performance. To overcome this issue, this paper presents two general polarimetric model-based decomposition methods by incorporating the generalized volume scattering model (GVSM or simplified adaptive volume scattering model, (SAVSM proposed by Antropov et al. and Huang et al., respectively, into the general decomposition framework proposed by Chen et al. By doing so, the final volume coherency matrix structure is selected from a wide range of volume scattering models within a continuous interval according to the data itself without adding unknowns. Moreover, the new approaches rely on one nonlinear optimization stage instead of four as in the previous method proposed by Chen et al. In addition, the parameter inversion procedure adopts the modified algorithm proposed by Xie et al. which leads to higher accuracy and more physically reliable output parameters. A number of Monte Carlo simulations of polarimetric synthetic aperture radar (PolSAR data are carried out and show that the proposed method with GVSM yields an overall improvement in the final accuracy of estimated parameters and outperforms both the version using SAVSM and the original approach. In addition, C-band Radarsat-2 and L-band AIRSAR fully polarimetric images over the San Francisco region are also used for testing purposes. A detailed comparison and analysis of decomposition results over different land-cover types are conducted. According to this study, the use of general decomposition models leads to a more accurate quantitative retrieval of target parameters. However, there

A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

Directory of Open Access Journals (Sweden)

Chaolong Jia

2015-01-01

Full Text Available Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

DEFF Research Database (Denmark)

Hansen, Per Christian; Jensen, Søren Holdt

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated...... with working Matlab code and applications in speech processing....

Orbital-Optimized MP3 and MP2.5 with Density-Fitting and Cholesky Decomposition Approximations.

Science.gov (United States)

Bozkaya, Uğur

2016-03-08

Efficient implementations of the orbital-optimized MP3 and MP2.5 methods with the density-fitting (DF-OMP3 and DF-OMP2.5) and Cholesky decomposition (CD-OMP3 and CD-OMP2.5) approaches are presented. The DF/CD-OMP3 and DF/CD-OMP2.5 methods are applied to a set of alkanes to compare the computational cost with the conventional orbital-optimized MP3 (OMP3) [Bozkaya J. Chem. Phys. 2011, 135, 224103] and the orbital-optimized MP2.5 (OMP2.5) [Bozkaya and Sherrill J. Chem. Phys. 2014, 141, 204105]. Our results demonstrate that the DF-OMP3 and DF-OMP2.5 methods provide considerably lower computational costs than OMP3 and OMP2.5. Further application results show that the orbital-optimized methods are very helpful for the study of open-shell noncovalent interactions, aromatic bond dissociation energies, and hydrogen transfer reactions. We conclude that the DF-OMP3 and DF-OMP2.5 methods are very promising for molecular systems with challenging electronic structures.

Satellite Image Time Series Decomposition Based on EEMD

Directory of Open Access Journals (Sweden)

Yun-long Kong

2015-11-01

Full Text Available Satellite Image Time Series (SITS have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs. EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD. Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS, Normalized Difference Vegetation Index (NDVI, and Global Environment Monitoring Index (GEMI time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

1.7. Acid decomposition of kaolin clays of Ziddi Deposit. 1.7.1. The hydrochloric acid decomposition of kaolin clays and siallites

International Nuclear Information System (INIS)

Mirsaidov, U.M.; Mirzoev, D.Kh.; Boboev, Kh.E.

2016-01-01

Present article of book is devoted to hydrochloric acid decomposition of kaolin clays and siallites. The chemical composition of kaolin clays and siallites was determined. The influence of temperature, process duration, acid concentration on hydrochloric acid decomposition of kaolin clays and siallites was studied. The optimal conditions of hydrochloric acid decomposition of kaolin clays and siallites were determined.

On the Dual-Decomposition-Based Resource and Power Allocation with Sleeping Strategy for Heterogeneous Networks

KAUST Repository

Alsharoa, Ahmad M.

2015-05-01

In this paper, the problem of radio and power resource management in long term evolution heterogeneous networks (LTE HetNets) is investigated. The goal is to minimize the total power consumption of the network while satisfying the user quality of service determined by each target data rate. We study the model where one macrocell base station is placed in the cell center, and multiple small cell base stations and femtocell access points are distributed around it. The dual decomposition technique is adopted to jointly optimize the power and carrier allocation in the downlink direction in addition to the selection of turned off small cell base stations. Our numerical results investigate the performance of the proposed scheme versus different system parameters and show an important saving in terms of total power consumption. © 2015 IEEE.

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Directory of Open Access Journals (Sweden)

Yuqi Dong

2016-12-01

Full Text Available Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

Cooperative Bacterial Foraging Optimization

Directory of Open Access Journals (Sweden)

Hanning Chen

2009-01-01

Full Text Available Bacterial Foraging Optimization (BFO is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. This paper presents a variation on the original BFO algorithm, namely, the Cooperative Bacterial Foraging Optimization (CBFO, which significantly improve the original BFO in solving complex optimization problems. This significant improvement is achieved by applying two cooperative approaches to the original BFO, namely, the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level. The experiments compare the performance of two CBFO variants with the original BFO, the standard PSO and a real-coded GA on four widely used benchmark functions. The new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

rCUR: an R package for CUR matrix decomposition

Directory of Open Access Journals (Sweden)

Bodor András

2012-05-01

Full Text Available Abstract Background Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD. Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Since they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the field from which the data are drawn. Results We present an implementation to perform CUR matrix decompositions, in the form of a freely available, open source R-package called rCUR. This package will help users to perform CUR-based analysis on large-scale data, such as those obtained from different high-throughput technologies, in an interactive and exploratory manner. We show two examples that illustrate how CUR-based techniques make it possible to reduce significantly the number of probes, while at the same time maintaining major trends in data and keeping the same classification accuracy. Conclusions The package rCUR provides functions for the users to perform CUR-based matrix decompositions in the R environment. In gene expression studies, it gives an additional way of analysis of differential expression and discriminant gene selection based on the use of statistical leverage scores. These scores, which have been used historically in diagnostic regression

The thermal decomposition behavior of ammonium perchlorate and of an ammonium-perchlorate-based composite propellant

Energy Technology Data Exchange (ETDEWEB)

Behrens, R.; Minier, L.

1998-03-24

The thermal decomposition of ammonium perchlorate (AP) and ammonium-perchlorate-based composite propellants is studied using the simultaneous thermogravimetric modulated beam mass spectrometry (STMBMS) technique. The main objective of the present work is to evaluate whether the STMBMS can provide new data on these materials that will have sufficient detail on the reaction mechanisms and associated reaction kinetics to permit creation of a detailed model of the thermal decomposition process. Such a model is a necessary ingredient to engineering models of ignition and slow-cookoff for these AP-based composite propellants. Results show that the decomposition of pure AP is controlled by two processes. One occurs at lower temperatures (240 to 270 C), produces mainly H{sub 2}O, O{sub 2}, Cl{sub 2}, N{sub 2}O and HCl, and is shown to occur in the solid phase within the AP particles. 200{micro} diameter AP particles undergo 25% decomposition in the solid phase, whereas 20{micro} diameter AP particles undergo only 13% decomposition. The second process is dissociative sublimation of AP to NH{sub 3} + HClO{sub 4} followed by the decomposition of, and reaction between, these two products in the gas phase. The dissociative sublimation process occurs over the entire temperature range of AP decomposition, but only becomes dominant at temperatures above those for the solid-phase decomposition. AP-based composite propellants are used extensively in both small tactical rocket motors and large strategic rocket systems.

Fusion of remote sensing images based on pyramid decomposition with Baldwinian Clonal Selection Optimization

Science.gov (United States)

Jin, Haiyan; Xing, Bei; Wang, Lei; Wang, Yanyan

2015-11-01

In this paper, we put forward a novel fusion method for remote sensing images based on the contrast pyramid (CP) using the Baldwinian Clonal Selection Algorithm (BCSA), referred to as CPBCSA. Compared with classical methods based on the transform domain, the method proposed in this paper adopts an improved heuristic evolutionary algorithm, wherein the clonal selection algorithm includes Baldwinian learning. In the process of image fusion, BCSA automatically adjusts the fusion coefficients of different sub-bands decomposed by CP according to the value of the fitness function. BCSA also adaptively controls the optimal search direction of the coefficients and accelerates the convergence rate of the algorithm. Finally, the fusion images are obtained via weighted integration of the optimal fusion coefficients and CP reconstruction. Our experiments show that the proposed method outperforms existing methods in terms of both visual effect and objective evaluation criteria, and the fused images are more suitable for human visual or machine perception.

Limited-memory adaptive snapshot selection for proper orthogonal decomposition

Energy Technology Data Exchange (ETDEWEB)

Oxberry, Geoffrey M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kostova-Vassilevska, Tanya [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Arrighi, Bill [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Chand, Kyle [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2015-04-02

Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory bounding the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.

An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods

Science.gov (United States)

Zhang, Hongqin; Tian, Xiangjun

2018-04-01

Ensemble-based data assimilation methods often use the so-called localization scheme to improve the representation of the ensemble background error covariance (Be). Extensive research has been undertaken to reduce the computational cost of these methods by using the localized ensemble samples to localize Be by means of a direct decomposition of the local correlation matrix C. However, the computational costs of the direct decomposition of the local correlation matrix C are still extremely high due to its high dimension. In this paper, we propose an efficient local correlation matrix decomposition approach based on the concept of alternating directions. This approach is intended to avoid direct decomposition of the correlation matrix. Instead, we first decompose the correlation matrix into 1-D correlation matrices in the three coordinate directions, then construct their empirical orthogonal function decomposition at low resolution. This procedure is followed by the 1-D spline interpolation process to transform the above decompositions to the high-resolution grid. Finally, an efficient correlation matrix decomposition is achieved by computing the very similar Kronecker product. We conducted a series of comparison experiments to illustrate the validity and accuracy of the proposed local correlation matrix decomposition approach. The effectiveness of the proposed correlation matrix decomposition approach and its efficient localization implementation of the nonlinear least-squares four-dimensional variational assimilation are further demonstrated by several groups of numerical experiments based on the Advanced Research Weather Research and Forecasting model.

Fast heap transform-based QR-decomposition of real and complex matrices: algorithms and codes

Science.gov (United States)

Grigoryan, Artyom M.

2015-03-01

In this paper, we describe a new look on the application of Givens rotations to the QR-decomposition problem, which is similar to the method of Householder transformations. We apply the concept of the discrete heap transform, or signal-induced unitary transforms which had been introduced by Grigoryan (2006) and used in signal and image processing. Both cases of real and complex nonsingular matrices are considered and examples of performing QR-decomposition of square matrices are given. The proposed method of QR-decomposition for the complex matrix is novel and differs from the known method of complex Givens rotation and is based on analytical equations for the heap transforms. Many examples illustrated the proposed heap transform method of QR-decomposition are given, algorithms are described in detail, and MATLAB-based codes are included.

Ultra-precision machining induced phase decomposition at surface of Zn-Al based alloy

International Nuclear Information System (INIS)

To, S.; Zhu, Y.H.; Lee, W.B.

2006-01-01

The microstructural changes and phase transformation of an ultra-precision machined Zn-Al based alloy were examined using X-ray diffraction and back-scattered electron microscopy techniques. Decomposition of the Zn-rich η phase and the related changes in crystal orientation was detected at the surface of the ultra-precision machined alloy specimen. The effects of the machining parameters, such as cutting speed and depth of cut, on the phase decomposition were discussed in comparison with the tensile and rolling induced microstrucutural changes and phase decomposition

Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals.

Science.gov (United States)

Ze Wang; Chi Man Wong; Feng Wan

2017-07-01

An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.

Efficient decomposition and linearization methods for the stochastic transportation problem

International Nuclear Information System (INIS)

Holmberg, K.

1993-01-01

The stochastic transportation problem can be formulated as a convex transportation problem with nonlinear objective function and linear constraints. We compare several different methods based on decomposition techniques and linearization techniques for this problem, trying to find the most efficient method or combination of methods. We discuss and test a separable programming approach, the Frank-Wolfe method with and without modifications, the new technique of mean value cross decomposition and the more well known Lagrangian relaxation with subgradient optimization, as well as combinations of these approaches. Computational tests are presented, indicating that some new combination methods are quite efficient for large scale problems. (authors) (27 refs.)

Decomposition Technology Development of Organic Component in a Decontamination Waste Solution

International Nuclear Information System (INIS)

Jung, Chong Hun; Oh, W. Z.; Won, H. J.; Choi, W. K.; Kim, G. N.; Moon, J. K.

2007-11-01

Through the project of 'Decomposition Technology Development of Organic Component in a Decontamination Waste Solution', the followings were studied. 1. Investigation of decontamination characteristics of chemical decontamination process 2. Analysis of COD, ferrous ion concentration, hydrogen peroxide concentration 3. Decomposition tests of hardly decomposable organic compounds 4. Improvement of organic acid decomposition process by ultrasonic wave and UV light 5. Optimization of decomposition process using a surrogate decontamination waste solution

Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

International Nuclear Information System (INIS)

Zhang, Yachao; Liu, Kaipei; Qin, Liang; An, Xueli

2016-01-01

Highlights: • Variational mode decomposition is adopted to process original wind power series. • A novel combined model based on machine learning methods is established. • An improved differential evolution algorithm is proposed for weight adjustment. • Probabilistic interval prediction is performed by quantile regression averaging. - Abstract: Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self-adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid

A novel ECG data compression method based on adaptive Fourier decomposition

Science.gov (United States)

Tan, Chunyu; Zhang, Liming

2017-12-01

This paper presents a novel electrocardiogram (ECG) compression method based on adaptive Fourier decomposition (AFD). AFD is a newly developed signal decomposition approach, which can decompose a signal with fast convergence, and hence reconstruct ECG signals with high fidelity. Unlike most of the high performance algorithms, our method does not make use of any preprocessing operation before compression. Huffman coding is employed for further compression. Validated with 48 ECG recordings of MIT-BIH arrhythmia database, the proposed method achieves the compression ratio (CR) of 35.53 and the percentage root mean square difference (PRD) of 1.47% on average with N = 8 decomposition times and a robust PRD-CR relationship. The results demonstrate that the proposed method has a good performance compared with the state-of-the-art ECG compressors.

Aligning observed and modelled behaviour based on workflow decomposition

Science.gov (United States)

Wang, Lu; Du, YuYue; Liu, Wei

2017-09-01

When business processes are mostly supported by information systems, the availability of event logs generated from these systems, as well as the requirement of appropriate process models are increasing. Business processes can be discovered, monitored and enhanced by extracting process-related information. However, some events cannot be correctly identified because of the explosion of the amount of event logs. Therefore, a new process mining technique is proposed based on a workflow decomposition method in this paper. Petri nets (PNs) are used to describe business processes, and then conformance checking of event logs and process models is investigated. A decomposition approach is proposed to divide large process models and event logs into several separate parts that can be analysed independently; while an alignment approach based on a state equation method in PN theory enhances the performance of conformance checking. Both approaches are implemented in programmable read-only memory (ProM). The correctness and effectiveness of the proposed methods are illustrated through experiments.

Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm

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Shuyu Dai

2018-04-01

Full Text Available For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. Currently, China has become the largest energy consumer in the world. Therefore, establishing an appropriate energy consumption prediction model and accurately forecasting energy consumption in China have practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. For forecasting the energy consumption in China accurately, considering the main driving factors of energy consumption, a novel model, EEMD-ISFLA-LSSVM (Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm, is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP (Gross Domestic Product, industrial structure (the proportion of the second industry added value, energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple subsequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM (Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, the data from 1990 to 2009 are taken as the training set, and the data from 2010 to 2016 are taken as the test set to make an

Decomposition Technology Development of Organic Component in a Decontamination Waste Solution

Energy Technology Data Exchange (ETDEWEB)

Jung, Chong Hun; Oh, W. Z.; Won, H. J.; Choi, W. K.; Kim, G. N.; Moon, J. K

2007-11-15

Through the project of 'Decomposition Technology Development of Organic Component in a Decontamination Waste Solution', the followings were studied. 1. Investigation of decontamination characteristics of chemical decontamination process 2. Analysis of COD, ferrous ion concentration, hydrogen peroxide concentration 3. Decomposition tests of hardly decomposable organic compounds 4. Improvement of organic acid decomposition process by ultrasonic wave and UV light 5. Optimization of decomposition process using a surrogate decontamination waste solution.

The optimized expansion based low-rank method for wavefield extrapolation

KAUST Repository

Wu, Zedong

2014-03-01

Spectral methods are fast becoming an indispensable tool for wavefield extrapolation, especially in anisotropic media because it tends to be dispersion and artifact free as well as highly accurate when solving the wave equation. However, for inhomogeneous media, we face difficulties in dealing with the mixed space-wavenumber domain extrapolation operator efficiently. To solve this problem, we evaluated an optimized expansion method that can approximate this operator with a low-rank variable separation representation. The rank defines the number of inverse Fourier transforms for each time extrapolation step, and thus, the lower the rank, the faster the extrapolation. The method uses optimization instead of matrix decomposition to find the optimal wavenumbers and velocities needed to approximate the full operator with its explicit low-rank representation. As a result, we obtain lower rank representations compared with the standard low-rank method within reasonable accuracy and thus cheaper extrapolations. Additional bounds set on the range of propagated wavenumbers to adhere to the physical wave limits yield unconditionally stable extrapolations regardless of the time step. An application on the BP model provided superior results compared to those obtained using the decomposition approach. For transversely isotopic media, because we used the pure P-wave dispersion relation, we obtained solutions that were free of the shear wave artifacts, and the algorithm does not require that n > 0. In addition, the required rank for the optimization approach to obtain high accuracy in anisotropic media was lower than that obtained by the decomposition approach, and thus, it was more efficient. A reverse time migration result for the BP tilted transverse isotropy model using this method as a wave propagator demonstrated the ability of the algorithm.

Partial differential equation-based approach for empirical mode decomposition: application on image analysis.

Science.gov (United States)

Niang, Oumar; Thioune, Abdoulaye; El Gueirea, Mouhamed Cheikh; Deléchelle, Eric; Lemoine, Jacques

2012-09-01

The major problem with the empirical mode decomposition (EMD) algorithm is its lack of a theoretical framework. So, it is difficult to characterize and evaluate this approach. In this paper, we propose, in the 2-D case, the use of an alternative implementation to the algorithmic definition of the so-called "sifting process" used in the original Huang's EMD method. This approach, especially based on partial differential equations (PDEs), was presented by Niang in previous works, in 2005 and 2007, and relies on a nonlinear diffusion-based filtering process to solve the mean envelope estimation problem. In the 1-D case, the efficiency of the PDE-based method, compared to the original EMD algorithmic version, was also illustrated in a recent paper. Recently, several 2-D extensions of the EMD method have been proposed. Despite some effort, 2-D versions for EMD appear poorly performing and are very time consuming. So in this paper, an extension to the 2-D space of the PDE-based approach is extensively described. This approach has been applied in cases of both signal and image decomposition. The obtained results confirm the usefulness of the new PDE-based sifting process for the decomposition of various kinds of data. Some results have been provided in the case of image decomposition. The effectiveness of the approach encourages its use in a number of signal and image applications such as denoising, detrending, or texture analysis.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

Science.gov (United States)

Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

2016-07-15

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Capturing molecular multimode relaxation processes in excitable gases based on decomposition of acoustic relaxation spectra

Science.gov (United States)

Zhu, Ming; Liu, Tingting; Wang, Shu; Zhang, Kesheng

2017-08-01

Existing two-frequency reconstructive methods can only capture primary (single) molecular relaxation processes in excitable gases. In this paper, we present a reconstructive method based on the novel decomposition of frequency-dependent acoustic relaxation spectra to capture the entire molecular multimode relaxation process. This decomposition of acoustic relaxation spectra is developed from the frequency-dependent effective specific heat, indicating that a multi-relaxation process is the sum of the interior single-relaxation processes. Based on this decomposition, we can reconstruct the entire multi-relaxation process by capturing the relaxation times and relaxation strengths of N interior single-relaxation processes, using the measurements of acoustic absorption and sound speed at 2N frequencies. Experimental data for the gas mixtures CO2-N2 and CO2-O2 validate our decomposition and reconstruction approach.

Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

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Duo Hao

2017-11-01

Full Text Available Cameras mounted on vehicles frequently suffer from image shake due to the vehicles’ motions. To remove jitter motions and preserve intentional motions, a hybrid digital image stabilization method is proposed that uses variational mode decomposition (VMD and relative entropy (RE. In this paper, the global motion vector (GMV is initially decomposed into several narrow-banded modes by VMD. REs, which exhibit the difference of probability distribution between two modes, are then calculated to identify the intentional and jitter motion modes. Finally, the summation of the jitter motion modes constitutes jitter motions, whereas the subtraction of the resulting sum from the GMV represents the intentional motions. The proposed stabilization method is compared with several known methods, namely, medium filter (MF, Kalman filter (KF, wavelet decomposition (MD method, empirical mode decomposition (EMD-based method, and enhanced EMD-based method, to evaluate stabilization performance. Experimental results show that the proposed method outperforms the other stabilization methods.

Lattice QCD with Domain Decomposition on Intel Xeon Phi Co-Processors

Energy Technology Data Exchange (ETDEWEB)

Heybrock, Simon; Joo, Balint; Kalamkar, Dhiraj D; Smelyanskiy, Mikhail; Vaidyanathan, Karthikeyan; Wettig, Tilo; Dubey, Pradeep

2014-12-01

The gap between the cost of moving data and the cost of computing continues to grow, making it ever harder to design iterative solvers on extreme-scale architectures. This problem can be alleviated by alternative algorithms that reduce the amount of data movement. We investigate this in the context of Lattice Quantum Chromodynamics and implement such an alternative solver algorithm, based on domain decomposition, on Intel Xeon Phi co-processor (KNC) clusters. We demonstrate close-to-linear on-chip scaling to all 60 cores of the KNC. With a mix of single- and half-precision the domain-decomposition method sustains 400-500 Gflop/s per chip. Compared to an optimized KNC implementation of a standard solver [1], our full multi-node domain-decomposition solver strong-scales to more nodes and reduces the time-to-solution by a factor of 5.

CP decomposition approach to blind separation for DS-CDMA system using a new performance index

Science.gov (United States)

Rouijel, Awatif; Minaoui, Khalid; Comon, Pierre; Aboutajdine, Driss

2014-12-01

In this paper, we present a canonical polyadic (CP) tensor decomposition isolating the scaling matrix. This has two major implications: (i) the problem conditioning shows up explicitly and could be controlled through a constraint on the so-called coherences and (ii) a performance criterion concerning the factor matrices can be exactly calculated and is more realistic than performance metrics used in the literature. Two new algorithms optimizing the CP decomposition based on gradient descent are proposed. This decomposition is illustrated by an application to direct-sequence code division multiplexing access (DS-CDMA) systems; computer simulations are provided and demonstrate the good behavior of these algorithms, compared to others in the literature.

Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach

International Nuclear Information System (INIS)

Monjoly, Stéphanie; André, Maïna; Calif, Rudy; Soubdhan, Ted

2017-01-01

This paper introduces a new approach for the forecasting of solar radiation series at 1 h ahead. We investigated on several techniques of multiscale decomposition of clear sky index K_c data such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Decomposition. From these differents methods, we built 11 decomposition components and 1 residu signal presenting different time scales. We performed classic forecasting models based on linear method (Autoregressive process AR) and a non linear method (Neural Network model). The choice of forecasting method is adaptative on the characteristic of each component. Hence, we proposed a modeling process which is built from a hybrid structure according to the defined flowchart. An analysis of predictive performances for solar forecasting from the different multiscale decompositions and forecast models is presented. From multiscale decomposition, the solar forecast accuracy is significantly improved, particularly using the wavelet decomposition method. Moreover, multistep forecasting with the proposed hybrid method resulted in additional improvement. For example, in terms of RMSE error, the obtained forecasting with the classical NN model is about 25.86%, this error decrease to 16.91% with the EMD-Hybrid Model, 14.06% with the EEMD-Hybid model and to 7.86% with the WD-Hybrid Model. - Highlights: • Hourly forecasting of GHI in tropical climate with many cloud formation processes. • Clear sky Index decomposition using three multiscale decomposition methods. • Combination of multiscale decomposition methods with AR-NN models to predict GHI. • Comparison of the proposed hybrid model with the classical models (AR, NN). • Best results using Wavelet-Hybrid model in comparison with classical models.

Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

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Yaolong Li

2017-01-01

Full Text Available By focusing on the issue of rolling element bearing (REB performance degradation assessment (PDA, a solution based on variational mode decomposition (VMD and Gath-Geva clustering time series segmentation (GGCTSS has been proposed. VMD is a new decomposition method. Since it is different from the recursive decomposition method, for example, empirical mode decomposition (EMD, local mean decomposition (LMD, and local characteristic-scale decomposition (LCD, VMD needs a priori parameters. In this paper, we will propose a method to optimize the parameters in VMD, namely, the number of decomposition modes and moderate bandwidth constraint, based on genetic algorithm. Executing VMD with the acquired parameters, the BLIMFs are obtained. By taking the envelope of the BLIMFs, the sensitive BLIMFs are selected. And then we take the amplitude of the defect frequency (ADF as a degradative feature. To get the performance degradation assessment, we are going to use the method called Gath-Geva clustering time series segmentation. Afterwards, the method is carried out by two pieces of run-to-failure data. The results indicate that the extracted feature could depict the process of degradation precisely.

A novel method for EMG decomposition based on matched filters

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Ailton Luiz Dias Siqueira Júnior

Full Text Available Introduction Decomposition of electromyography (EMG signals into the constituent motor unit action potentials (MUAPs can allow for deeper insights into the underlying processes associated with the neuromuscular system. The vast majority of the methods for EMG decomposition found in the literature depend on complex algorithms and specific instrumentation. As an attempt to contribute to solving these issues, we propose a method based on a bank of matched filters for the decomposition of EMG signals. Methods Four main units comprise our method: a bank of matched filters, a peak detector, a motor unit classifier and an overlapping resolution module. The system’s performance was evaluated with simulated and real EMG data. Classification accuracy was measured by comparing the responses of the system with known data from the simulator and with the annotations of a human expert. Results The results show that decomposition of non-overlapping MUAPs can be achieved with up to 99% accuracy for signals with up to 10 active motor units and a signal-to-noise ratio (SNR of 10 dB. For overlapping MUAPs with up to 10 motor units per signal and a SNR of 20 dB, the technique allows for correct classification of approximately 71% of the MUAPs. The method is capable of processing, decomposing and classifying a 50 ms window of data in less than 5 ms using a standard desktop computer. Conclusion This article contributes to the ongoing research on EMG decomposition by describing a novel technique capable of delivering high rates of success by means of a fast algorithm, suggesting its possible use in future real-time embedded applications, such as myoelectric prostheses control and biofeedback systems.

Power System Decomposition for Practical Implementation of Bulk-Grid Voltage Control Methods

Energy Technology Data Exchange (ETDEWEB)

Vallem, Mallikarjuna R.; Vyakaranam, Bharat GNVSR; Holzer, Jesse T.; Elizondo, Marcelo A.; Samaan, Nader A.

2017-10-19

Power system algorithms such as AC optimal power flow and coordinated volt/var control of the bulk power system are computationally intensive and become difficult to solve in operational time frames. The computational time required to run these algorithms increases exponentially as the size of the power system increases. The solution time for multiple subsystems is less than that for solving the entire system simultaneously, and the local nature of the voltage problem lends itself to such decomposition. This paper describes an algorithm that can be used to perform power system decomposition from the point of view of the voltage control problem. Our approach takes advantage of the dominant localized effect of voltage control and is based on clustering buses according to the electrical distances between them. One of the contributions of the paper is to use multidimensional scaling to compute n-dimensional Euclidean coordinates for each bus based on electrical distance to perform algorithms like K-means clustering. A simple coordinated reactive power control of photovoltaic inverters for voltage regulation is used to demonstrate the effectiveness of the proposed decomposition algorithm and its components. The proposed decomposition method is demonstrated on the IEEE 118-bus system.

COMPOSITE POLYMERICADDITIVESDESIGNATED FORCONCRETEMIXES BASED ONPOLYACRYLATES, PRODUCTS OF THERMAL DECOMPOSITION OF POLYAMIDE-6 AND LOW-MOLECULAR POLYETHYLENE

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Polyakov Vyacheslav Sergeevich

2012-07-01

4 the optimal composite additive that increases the time period of stiffening of the cement grout , improves the water resistance and the compressive strength of concrete, represents the composition of polyacrylates and polymethacrylates, products of thermal decomposition of polyamide-6 and low-molecular polyethylene in the weight ratio of 1:1:0.5.

Palm vein recognition based on directional empirical mode decomposition

Science.gov (United States)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation

International Nuclear Information System (INIS)

Wang, Yamin; Wu, Lei

2016-01-01

This paper presents a comprehensive analysis on practical challenges of empirical mode decomposition (EMD) based algorithms on wind speed and solar irradiation forecasts that have been largely neglected in literature, and proposes an alternative approach to mitigate such challenges. Specifically, the challenges are: (1) Decomposed sub-series are very sensitive to the original time series data. That is, sub-series of the new time series, consisting of the original one plus a limit number of new data samples, may significantly differ from those used in training forecasting models. In turn, forecasting models established by original sub-series may not be suitable for newly decomposed sub-series and have to be trained more frequently; and (2) Key environmental factors usually play a critical role in non-decomposition based methods for forecasting wind speed and solar irradiation. However, it is difficult to incorporate such critical environmental factors into forecasting models of individual decomposed sub-series, because the correlation between the original data and environmental factors is lost after decomposition. Numerical case studies on wind speed and solar irradiation forecasting show that the performance of existing EMD-based forecasting methods could be worse than the non-decomposition based forecasting model, and are not effective in practical cases. Finally, the approximated forecasting model based on EMD is proposed to mitigate the challenges and achieve better forecasting results than existing EMD-based forecasting algorithms and the non-decomposition based forecasting models on practical wind speed and solar irradiation forecasting cases. - Highlights: • Two challenges of existing EMD-based forecasting methods are discussed. • Significant changes of sub-series in each step of the rolling forecast procedure. • Difficulties in incorporating environmental factors into sub-series forecasting models. • The approximated forecasting method is proposed to

Thermal Decomposition Behaviors and Burning Characteristics of AN/Nitramine-Based Composite Propellant

Science.gov (United States)

Naya, Tomoki; Kohga, Makoto

2015-04-01

Ammonium nitrate (AN) has attracted much attention due to its clean burning nature as an oxidizer. However, an AN-based composite propellant has the disadvantages of low burning rate and poor ignitability. In this study, we added nitramine of cyclotrimethylene trinitramine (RDX) or cyclotetramethylene tetranitramine (HMX) as a high-energy material to AN propellants to overcome these disadvantages. The thermal decomposition and burning rate characteristics of the prepared propellants were examined as the ratio of AN and nitramine was varied. In the thermal decomposition process, AN/RDX propellants showed unique mass loss peaks in the lower temperature range that were not observed for AN or RDX propellants alone. AN and RDX decomposed continuously as an almost single oxidizer in the AN/RDX propellant. In contrast, AN/HMX propellants exhibited thermal decomposition characteristics similar to those of AN and HMX, which decomposed almost separately in the thermal decomposition of the AN/HMX propellant. The ignitability was improved and the burning rate increased by the addition of nitramine for both AN/RDX and AN/HMX propellants. The increased burning rates of AN/RDX propellants were greater than those of AN/HMX. The difference in the thermal decomposition and burning characteristics was caused by the interaction between AN and RDX.

Adaptive Hybrid Visual Servo Regulation of Mobile Robots Based on Fast Homography Decomposition

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Chunfu Wu

2015-01-01

Full Text Available For the monocular camera-based mobile robot system, an adaptive hybrid visual servo regulation algorithm which is based on a fast homography decomposition method is proposed to drive the mobile robot to its desired position and orientation, even when object’s imaging depth and camera’s position extrinsic parameters are unknown. Firstly, the homography’s particular properties caused by mobile robot’s 2-DOF motion are taken into account to induce a fast homography decomposition method. Secondly, the homography matrix and the extracted orientation error, incorporated with the desired view’s single feature point, are utilized to form an error vector and its open-loop error function. Finally, Lyapunov-based techniques are exploited to construct an adaptive regulation control law, followed by the experimental verification. The experimental results show that the proposed fast homography decomposition method is not only simple and efficient, but also highly precise. Meanwhile, the designed control law can well enable mobile robot position and orientation regulation despite the lack of depth information and camera’s position extrinsic parameters.

Speckle imaging using the principle value decomposition method

International Nuclear Information System (INIS)

Sherman, J.W.

1978-01-01

Obtaining diffraction-limited images in the presence of atmospheric turbulence is a topic of current interest. Two types of approaches have evolved: real-time correction and speckle imaging. A speckle imaging reconstruction method was developed by use of an ''optimal'' filtering approach. This method is based on a nonlinear integral equation which is solved by principle value decomposition. The method was implemented on a CDC 7600 for study. The restoration algorithm is discussed and its performance is illustrated. 7 figures

Adaptive variational mode decomposition method for signal processing based on mode characteristic

Science.gov (United States)

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

2018-07-01

Variational mode decomposition is a completely non-recursive decomposition model, where all the modes are extracted concurrently. However, the model requires a preset mode number, which limits the adaptability of the method since a large deviation in the number of mode set will cause the discard or mixing of the mode. Hence, a method called Adaptive Variational Mode Decomposition (AVMD) was proposed to automatically determine the mode number based on the characteristic of intrinsic mode function. The method was used to analyze the simulation signals and the measured signals in the hydropower plant. Comparisons have also been conducted to evaluate the performance by using VMD, EMD and EWT. It is indicated that the proposed method has strong adaptability and is robust to noise. It can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

Three-dimensional decomposition models for carbon productivity

International Nuclear Information System (INIS)

Meng, Ming; Niu, Dongxiao

2012-01-01

This paper presents decomposition models for the change in carbon productivity, which is considered a key indicator that reflects the contributions to the control of greenhouse gases. Carbon productivity differential was used to indicate the beginning of decomposition. After integrating the differential equation and designing the Log Mean Divisia Index equations, a three-dimensional absolute decomposition model for carbon productivity was derived. Using this model, the absolute change of carbon productivity was decomposed into a summation of the absolute quantitative influences of each industrial sector, for each influence factor (technological innovation and industrial structure adjustment) in each year. Furthermore, the relative decomposition model was built using a similar process. Finally, these models were applied to demonstrate the decomposition process in China. The decomposition results reveal several important conclusions: (a) technological innovation plays a far more important role than industrial structure adjustment; (b) industry and export trade exhibit great influence; (c) assigning the responsibility for CO 2 emission control to local governments, optimizing the structure of exports, and eliminating backward industrial capacity are highly essential to further increase China's carbon productivity. -- Highlights: ► Using the change of carbon productivity to measure a country's contribution. ► Absolute and relative decomposition models for carbon productivity are built. ► The change is decomposed to the quantitative influence of three-dimension. ► Decomposition results can be used for improving a country's carbon productivity.

Tissue artifact removal from respiratory signals based on empirical mode decomposition.

Science.gov (United States)

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-05-01

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.

An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

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Xiangmin Guan

2015-01-01

Full Text Available Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

Decomposition of ammonium nitrate in homogeneous and catalytic denitration

International Nuclear Information System (INIS)

Anan'ev, A. V.; Tananaev, I. G.; Shilov, V. P.

2005-01-01

Ammonium nitrate is one of potentially explosive by-products of spent fuel reprocessing. Decomposition of ammonium nitrate in the HNO 3 -HCOOH system was studied in the presence or absence of Pt/SiO 2 catalyst. It was found that decomposition of ammonium nitrate is due to homogeneous noncatalytic oxidation of ammonium ion with nitrous acid generated in the HNO 3 -HCOOH system during denitration. The platinum catalyst initiates the reaction of HNO 3 with HCOOH to form HNO 2 . The regular trends were revealed and the optimal conditions of decomposition of ammonium nitrate in nitric acid solutions were found [ru

Optimal design of hydraulic excavator working device based on multiple surrogate models

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Qingying Qiu

2016-05-01

Full Text Available The optimal design of hydraulic excavator working device is often characterized by computationally expensive analysis methods such as finite element analysis. Significant difficulties also exist when using a sensitivity-based decomposition approach to such practical engineering problems because explicit mathematical formulas between the objective function and design variables are impossible to formulate. An effective alternative is known as the surrogate model. The purpose of this article is to provide a comparative study on multiple surrogate models, including the response surface methodology, Kriging, radial basis function, and support vector machine, and select the one that best fits the optimization of the working device. In this article, a new modeling strategy based on the combination of the dimension variables between hinge joints and the forces loaded on hinge joints of the working device is proposed. In addition, the extent to which the accuracy of the surrogate models depends on different design variables is presented. The bionic intelligent optimization algorithm is then used to obtain the optimal results, which demonstrate that the maximum stresses calculated by the predicted method and finite element analysis are quite similar, but the efficiency of the former is much higher than that of the latter.

Ammonia synthesis and decomposition on a Ru-based catalyst modeled by first-principles

DEFF Research Database (Denmark)

Hellman, A.; Honkala, Johanna Karoliina; Remediakis, Ioannis

2009-01-01

A recently published first-principles model for the ammonia synthesis on an unpromoted Ru-based catalyst is extended to also describe ammonia decomposition. In addition, further analysis concerning trends in ammonia productivity, surface conditions during the reaction, and macro-properties, such ......A recently published first-principles model for the ammonia synthesis on an unpromoted Ru-based catalyst is extended to also describe ammonia decomposition. In addition, further analysis concerning trends in ammonia productivity, surface conditions during the reaction, and macro......-properties, such as apparent activation energies and reaction orders are provided. All observed trends in activity are captured by the model and the absolute value of ammonia synthesis/decomposition productivity is predicted to within a factor of 1-100 depending on the experimental conditions. Moreover it is shown: (i......) that small changes in the relative adsorption potential energies are sufficient to get a quantitative agreement between theory and experiment (Appendix A) and (ii) that it is possible to reproduce results from the first-principles model by a simple micro-kinetic model (Appendix B)....

Optimized Kernel Entropy Components.

Science.gov (United States)

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest.

Science.gov (United States)

Ma, Suliang; Chen, Mingxuan; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-04-16

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

Science.gov (United States)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

The design and implementation of signal decomposition system of CL multi-wavelet transform based on DSP builder

Science.gov (United States)

Huang, Yan; Wang, Zhihui

2015-12-01

With the development of FPGA, DSP Builder is widely applied to design system-level algorithms. The algorithm of CL multi-wavelet is more advanced and effective than scalar wavelets in processing signal decomposition. Thus, a system of CL multi-wavelet based on DSP Builder is designed for the first time in this paper. The system mainly contains three parts: a pre-filtering subsystem, a one-level decomposition subsystem and a two-level decomposition subsystem. It can be converted into hardware language VHDL by the Signal Complier block that can be used in Quartus II. After analyzing the energy indicator, it shows that this system outperforms Daubenchies wavelet in signal decomposition. Furthermore, it has proved to be suitable for the implementation of signal fusion based on SoPC hardware, and it will become a solid foundation in this new field.

Variance decomposition-based sensitivity analysis via neural networks

International Nuclear Information System (INIS)

Marseguerra, Marzio; Masini, Riccardo; Zio, Enrico; Cojazzi, Giacomo

2003-01-01

This paper illustrates a method for efficiently performing multiparametric sensitivity analyses of the reliability model of a given system. These analyses are of great importance for the identification of critical components in highly hazardous plants, such as the nuclear or chemical ones, thus providing significant insights for their risk-based design and management. The technique used to quantify the importance of a component parameter with respect to the system model is based on a classical decomposition of the variance. When the model of the system is realistically complicated (e.g. by aging, stand-by, maintenance, etc.), its analytical evaluation soon becomes impractical and one is better off resorting to Monte Carlo simulation techniques which, however, could be computationally burdensome. Therefore, since the variance decomposition method requires a large number of system evaluations, each one to be performed by Monte Carlo, the need arises for possibly substituting the Monte Carlo simulation model with a fast, approximated, algorithm. Here we investigate an approach which makes use of neural networks appropriately trained on the results of a Monte Carlo system reliability/availability evaluation to quickly provide with reasonable approximation, the values of the quantities of interest for the sensitivity analyses. The work was a joint effort between the Department of Nuclear Engineering of the Polytechnic of Milan, Italy, and the Institute for Systems, Informatics and Safety, Nuclear Safety Unit of the Joint Research Centre in Ispra, Italy which sponsored the project

A unified statistical framework for material decomposition using multienergy photon counting x-ray detectors

International Nuclear Information System (INIS)

Choi, Jiyoung; Kang, Dong-Goo; Kang, Sunghoon; Sung, Younghun; Ye, Jong Chul

2013-01-01

Purpose: Material decomposition using multienergy photon counting x-ray detectors (PCXD) has been an active research area over the past few years. Even with some success, the problem of optimal energy selection and three material decomposition including malignant tissue is still on going research topic, and more systematic studies are required. This paper aims to address this in a unified statistical framework in a mammographic environment.Methods: A unified statistical framework for energy level optimization and decomposition of three materials is proposed. In particular, an energy level optimization algorithm is derived using the theory of the minimum variance unbiased estimator, and an iterative algorithm is proposed for material composition as well as system parameter estimation under the unified statistical estimation framework. To verify the performance of the proposed algorithm, the authors performed simulation studies as well as real experiments using physical breast phantom and ex vivo breast specimen. Quantitative comparisons using various performance measures were conducted, and qualitative performance evaluations for ex vivo breast specimen were also performed by comparing the ground-truth malignant tissue areas identified by radiologists.Results: Both simulation and real experiments confirmed that the optimized energy bins by the proposed method allow better material decomposition quality. Moreover, for the specimen thickness estimation errors up to 2 mm, the proposed method provides good reconstruction results in both simulation and real ex vivo breast phantom experiments compared to existing methods.Conclusions: The proposed statistical framework of PCXD has been successfully applied for the energy optimization and decomposition of three material in a mammographic environment. Experimental results using the physical breast phantom and ex vivo specimen support the practicality of the proposed algorithm

Randomized interpolative decomposition of separated representations

Science.gov (United States)

Biagioni, David J.; Beylkin, Daniel; Beylkin, Gregory

2015-01-01

We introduce an algorithm to compute tensor interpolative decomposition (dubbed CTD-ID) for the reduction of the separation rank of Canonical Tensor Decompositions (CTDs). Tensor ID selects, for a user-defined accuracy ɛ, a near optimal subset of terms of a CTD to represent the remaining terms via a linear combination of the selected terms. CTD-ID can be used as an alternative to or in combination with the Alternating Least Squares (ALS) algorithm. We present examples of its use within a convergent iteration to compute inverse operators in high dimensions. We also briefly discuss the spectral norm as a computational alternative to the Frobenius norm in estimating approximation errors of tensor ID. We reduce the problem of finding tensor IDs to that of constructing interpolative decompositions of certain matrices. These matrices are generated via randomized projection of the terms of the given tensor. We provide cost estimates and several examples of the new approach to the reduction of separation rank.

Decomposition methods for unsupervised learning

DEFF Research Database (Denmark)

Mørup, Morten

2008-01-01

This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

Decomposition of silica-alumina ores of Afghanistan by sulfuric acid

International Nuclear Information System (INIS)

Khomidi, A.K.; Mamatov, E.D.

2016-01-01

Present article is devoted to decomposition of silica-alumina ores of Afghanistan by sulfuric acid. Physicochemical properties of initial silica-alumina ores were studied by means of X-ray phase, differential thermal and silicate analysis. The influence of temperature, process duration and acid concentration on extraction rate of valuable components was considered. The optimal conditions of decomposition of silica-alumina ores of Afghanistan by sulfuric acid were proposed.

An Improved Algorithm to Delineate Urban Targets with Model-Based Decomposition of PolSAR Data

Directory of Open Access Journals (Sweden)

Dingfeng Duan

2017-10-01

Full Text Available In model-based decomposition algorithms using polarimetric synthetic aperture radar (PolSAR data, urban targets are typically identified based on the existence of strong double-bounced scattering. However, urban targets with large azimuth orientation angles (AOAs produce strong volumetric scattering that appears similar to scattering characteristics from tree canopies. Due to scattering ambiguity, urban targets can be classified into the vegetation category if the same classification scheme of the model-based PolSAR decomposition algorithms is followed. To resolve the ambiguity and to reduce the misclassification eventually, we introduced a correlation coefficient that characterized scattering mechanisms of urban targets with variable AOAs. Then, an existing volumetric scattering model was modified, and a PolSAR decomposition algorithm developed. The validity and effectiveness of the algorithm were examined using four PolSAR datasets. The algorithm was valid and effective to delineate urban targets with a wide range of AOAs, and applicable to a broad range of ground targets from urban areas, and from upland and flooded forest stands.

1.7.2. The hydrochloric acid decomposition of pre-baked kaolin clays and siallites

International Nuclear Information System (INIS)

Mirsaidov, U.M.; Mirzoev, D.Kh.; Boboev, Kh.E.

2016-01-01

Present article of book is devoted to hydrochloric acid decomposition of pre-baked kaolin clays and siallites. The chemical composition of kaolin clays and siallites was determined. The influence of temperature, process duration, acid concentration on hydrochloric acid decomposition of pre-baked kaolin clays and siallites was studied. The optimal conditions of hydrochloric acid decomposition of pre-baked kaolin clays and siallites were determined.

Reaction mechanism of reductive decomposition of FGD gypsum with anthracite

International Nuclear Information System (INIS)

Zheng, Da; Lu, Hailin; Sun, Xiuyun; Liu, Xiaodong; Han, Weiqing; Wang, Lianjun

2013-01-01

Highlights: • The reaction mechanism was different if the molar ratio of C/CaSO 4 was different. • The yield of CaO rises with an increase in temperature. • The optimal ratio of C/CaSO 4 = 1.2:1. • The decomposition process is mainly apparent solid–solid reaction with liquid-phase involved. - Abstract: The process of decomposition reaction between flue gas desulfurization (FGD) gypsum and anthracite is complex, which depends on the reaction conditions and atmosphere. In this study, thermogravimetric analysis with Fourier transform infrared spectroscopy (TGA-FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and the experiment in a tubular reactor were used to characterize the decomposition reaction in a nitrogen atmosphere under different conditions. The reaction mechanism analysis showed that the decomposition reaction process and mechanism were different when the molar proportion of C/CaSO 4 was changed. The experiment results showed that appropriate increase in the C/CaSO 4 proportion and higher temperatures were suitable for the formation of the main production of CaO, which can help us to understand the solid state reaction mechanism better. Via kinetic analysis of the reaction between anthracite and FGD gypsum under the optimal molar ratio of C/CaSO 4 , the mechanism model of the reaction was confirmed and the decomposition process was a two-step reaction which was in accordance with apparent solid–solid reaction

Emergy-Based Regional Socio-Economic Metabolism Analysis: An Application of Data Envelopment Analysis and Decomposition Analysis

OpenAIRE

Zilong Zhang; Xingpeng Chen; Peter Heck

2014-01-01

Integrated analysis on socio-economic metabolism could provide a basis for understanding and optimizing regional sustainability. The paper conducted socio-economic metabolism analysis by means of the emergy accounting method coupled with data envelopment analysis and decomposition analysis techniques to assess the sustainability of Qingyang city and its eight sub-region system, as well as to identify the major driving factors of performance change during 2000–2007, to serve as the basis for f...

Decomposition and parallelization strategies for solving large-scale MDO problems

Energy Technology Data Exchange (ETDEWEB)

Grauer, M.; Eschenauer, H.A. [Research Center for Multidisciplinary Analyses and Applied Structural Optimization, FOMAAS, Univ. of Siegen (Germany)

2007-07-01

During previous years, structural optimization has been recognized as a useful tool within the discriptiones of engineering and economics. However, the optimization of large-scale systems or structures is impeded by an immense solution effort. This was the reason to start a joint research and development (R and D) project between the Institute of Mechanics and Control Engineering and the Information and Decision Sciences Institute within the Research Center for Multidisciplinary Analyses and Applied Structural Optimization (FOMAAS) on cluster computing for parallel and distributed solution of multidisciplinary optimization (MDO) problems based on the OpTiX-Workbench. Here the focus of attention will be put on coarsegrained parallelization and its implementation on clusters of workstations. A further point of emphasis was laid on the development of a parallel decomposition strategy called PARDEC, for the solution of very complex optimization problems which cannot be solved efficiently by sequential integrated optimization. The use of the OptiX-Workbench together with the FEM ground water simulation system FEFLOW is shown for a special water management problem. (orig.)

Multi-Material Design Optimization of Composite Structures

DEFF Research Database (Denmark)

Hvejsel, Christian Frier

properties. The modeling encompasses discrete orientationing of orthotropic materials, selection between different distinct materials as well as removal of material representing holes in the structure within a unified parametrization. The direct generalization of two-phase topology optimization to any number...... of a relaxation-based search heuristic that accelerates a Generalized Benders' Decomposition technique for global optimization and enables the solution of medium-scale problems to global optimality. Improvements in the ability to solve larger problems to global optimality are found and potentially further...... improvements may be obtained with this technique in combination with cheaper heuristics....

The nitric acid decomposition of calcined danburite concentrate of Ak-Arkhar Deposit

International Nuclear Information System (INIS)

Kurbonov, A.S.; Mamatov, E.D.; Suleymani, M.; Borudzherdi, A.; Mirsaidov, U.M.

2011-01-01

Present article is devoted to nitric acid decomposition of calcined danburite concentrate of Ak-Arkhar Deposit of Tajikistan. The obtaining of boric acid from pre backed danburite concentrate by decomposition of nitric acid was studied. The chemical composition of danburite concentrate was determined. The laboratory study of danburite leaching by nitric acid was conducted. The influence of temperature, process duration, nitric acid concentration on nitric acid decomposition of calcined danburite concentrate of Ak-Arkhar Deposit was studied as well. The optimal conditions of nitric acid decomposition of calcined danburite concentrate of Ak-Arkhar Deposit, including temperature, process duration, nitric acid concentration and particle size were proposed.

Symmetric Tensor Decomposition

DEFF Research Database (Denmark)

Brachat, Jerome; Comon, Pierre; Mourrain, Bernard

2010-01-01

We present an algorithm for decomposing a symmetric tensor, of dimension n and order d, as a sum of rank-1 symmetric tensors, extending the algorithm of Sylvester devised in 1886 for binary forms. We recall the correspondence between the decomposition of a homogeneous polynomial in n variables...... of polynomial equations of small degree in non-generic cases. We propose a new algorithm for symmetric tensor decomposition, based on this characterization and on linear algebra computations with Hankel matrices. The impact of this contribution is two-fold. First it permits an efficient computation...... of the decomposition of any tensor of sub-generic rank, as opposed to widely used iterative algorithms with unproved global convergence (e.g. Alternate Least Squares or gradient descents). Second, it gives tools for understanding uniqueness conditions and for detecting the rank....

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array

Directory of Open Access Journals (Sweden)

Yu-Fei Gao

2017-04-01

Full Text Available This paper investigates a two-dimensional angle of arrival (2D AOA estimation algorithm for the electromagnetic vector sensor (EMVS array based on Type-2 block component decomposition (BCD tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD method.

Gradient-based methods for production optimization of oil reservoirs

Energy Technology Data Exchange (ETDEWEB)

Suwartadi, Eka

2012-07-01

Production optimization for water flooding in the secondary phase of oil recovery is the main topic in this thesis. The emphasis has been on numerical optimization algorithms, tested on case examples using simple hypothetical oil reservoirs. Gradientbased optimization, which utilizes adjoint-based gradient computation, is used to solve the optimization problems. The first contribution of this thesis is to address output constraint problems. These kinds of constraints are natural in production optimization. Limiting total water production and water cut at producer wells are examples of such constraints. To maintain the feasibility of an optimization solution, a Lagrangian barrier method is proposed to handle the output constraints. This method incorporates the output constraints into the objective function, thus avoiding additional computations for the constraints gradient (Jacobian) which may be detrimental to the efficiency of the adjoint method. The second contribution is the study of the use of second-order adjoint-gradient information for production optimization. In order to speedup convergence rate in the optimization, one usually uses quasi-Newton approaches such as BFGS and SR1 methods. These methods compute an approximation of the inverse of the Hessian matrix given the first-order gradient from the adjoint method. The methods may not give significant speedup if the Hessian is ill-conditioned. We have developed and implemented the Hessian matrix computation using the adjoint method. Due to high computational cost of the Newton method itself, we instead compute the Hessian-timesvector product which is used in a conjugate gradient algorithm. Finally, the last contribution of this thesis is on surrogate optimization for water flooding in the presence of the output constraints. Two kinds of model order reduction techniques are applied to build surrogate models. These are proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM

Photoacoustic imaging optimization with raw signal deconvolution and empirical mode decomposition

Science.gov (United States)

Guo, Chengwen; Wang, Jing; Qin, Yu; Zhan, Hongchen; Yuan, Jie; Cheng, Qian; Wang, Xueding

2018-02-01

Photoacoustic (PA) signal of an ideal optical absorb particle is a single N-shape wave. PA signals of a complicated biological tissue can be considered as the combination of individual N-shape waves. However, the N-shape wave basis not only complicates the subsequent work, but also results in aliasing between adjacent micro-structures, which deteriorates the quality of the final PA images. In this paper, we propose a method to improve PA image quality through signal processing method directly working on raw signals, which including deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent point spread function (PSF) which is measured in advance. Then, EMD is adopted to adaptively re-shape the PA signals with two constraints, positive polarity and spectrum consistence. With our proposed method, the built PA images can yield more detail structural information. Micro-structures are clearly separated and revealed. To validate the effectiveness of this method, we present numerical simulations and phantom studies consist of a densely distributed point sources model and a blood vessel model. In the future, our study might hold the potential for clinical PA imaging as it can help to distinguish micro-structures from the optimized images and even measure the size of objects from deconvolved signals.

Task decomposition for a multilimbed robot to work in reachable but unorientable space

Science.gov (United States)

Su, Chau; Zheng, Yuan F.

1991-01-01

Robot manipulators installed on legged mobile platforms are suggested for enlarging robot workspace. To plan the motion of such a system, the arm-platform motion coordination problem is raised, and a task decomposition is proposed to solve the problem. A given task described by the destination position and orientation of the end effector is decomposed into subtasks for arm manipulation and for platform configuration, respectively. The former is defined as the end-effector position and orientation with respect to the platform, and the latter as the platform position and orientation in the base coordinates. Three approaches are proposed for the task decomposition. The approaches are also evaluated in terms of the displacements, from which an optimal approach can be selected.

Optimal PMU Placement with Uncertainty Using Pareto Method

Directory of Open Access Journals (Sweden)

A. Ketabi

2012-01-01

Full Text Available This paper proposes a method for optimal placement of Phasor Measurement Units (PMUs in state estimation considering uncertainty. State estimation has first been turned into an optimization exercise in which the objective function is selected to be the number of unobservable buses which is determined based on Singular Value Decomposition (SVD. For the normal condition, Differential Evolution (DE algorithm is used to find the optimal placement of PMUs. By considering uncertainty, a multiobjective optimization exercise is hence formulated. To achieve this, DE algorithm based on Pareto optimum method has been proposed here. The suggested strategy is applied on the IEEE 30-bus test system in several case studies to evaluate the optimal PMUs placement.

Cooperative Rendezvous and Docking for Underwater Robots Using Model Predictive Control and Dual Decomposition

DEFF Research Database (Denmark)

Nielsen, Mikkel Cornelius; Johansen, Tor Arne; Blanke, Mogens

2018-01-01

This paper considers the problem of rendezvous and docking with visual constraints in the context of underwater robots with camera-based navigation. The objective is the convergence of the vehicles to a common point while maintaining visual contact. The proposed solution includes the design of a ...... of a distributed model predictive controller based on dual decomposition, which allows for optimization in a decentralized fashion. The proposed distributed controller enables rendezvous and docking between vehicles while maintaining visual contact....

Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition

OpenAIRE

Guo, Qi; Chen, Bo-Wei; Jiang, Feng; Ji, Xiangyang; Kung, Sun-Yuan

2015-01-01

This study presents a divide-and-conquer (DC) approach based on feature space decomposition for classification. When large-scale datasets are present, typical approaches usually employed truncated kernel methods on the feature space or DC approaches on the sample space. However, this did not guarantee separability between classes, owing to overfitting. To overcome such problems, this work proposes a novel DC approach on feature spaces consisting of three steps. Firstly, we divide the feature ...

A copyright protection scheme for digital images based on shuffled singular value decomposition and visual cryptography.

Science.gov (United States)

Devi, B Pushpa; Singh, Kh Manglem; Roy, Sudipta

2016-01-01

This paper proposes a new watermarking algorithm based on the shuffled singular value decomposition and the visual cryptography for copyright protection of digital images. It generates the ownership and identification shares of the image based on visual cryptography. It decomposes the image into low and high frequency sub-bands. The low frequency sub-band is further divided into blocks of same size after shuffling it and then the singular value decomposition is applied to each randomly selected block. Shares are generated by comparing one of the elements in the first column of the left orthogonal matrix with its corresponding element in the right orthogonal matrix of the singular value decomposition of the block of the low frequency sub-band. The experimental results show that the proposed scheme clearly verifies the copyright of the digital images, and is robust to withstand several image processing attacks. Comparison with the other related visual cryptography-based algorithms reveals that the proposed method gives better performance. The proposed method is especially resilient against the rotation attack.

Climate fails to predict wood decomposition at regional scales

Science.gov (United States)

Mark A. Bradford; Robert J. Warren; Petr Baldrian; Thomas W. Crowther; Daniel S. Maynard; Emily E. Oldfield; William R. Wieder; Stephen A. Wood; Joshua R. King

2014-01-01

Decomposition of organic matter strongly influences ecosystem carbon storage1. In Earth-system models, climate is a predominant control on the decomposition rates of organic matter2, 3, 4, 5. This assumption is based on the mean response of decomposition to climate, yet there is a growing appreciation in other areas of global change science that projections based on...

Decomposition of Polarimetric SAR Images Based on Second- and Third-order Statics Analysis

Science.gov (United States)

Kojima, S.; Hensley, S.

2012-12-01

There are many papers concerning the research of the decomposition of polerimetric SAR imagery. Most of them are based on second-order statics analysis that Freeman and Durden [1] suggested for the reflection symmetry condition that implies that the co-polarization and cross-polarization correlations are close to zero. Since then a number of improvements and enhancements have been proposed to better understand the underlying backscattering mechanisms present in polarimetric SAR images. For example, Yamaguchi et al. [2] added the helix component into Freeman's model and developed a 4 component scattering model for the non-reflection symmetry condition. In addition, Arii et al. [3] developed an adaptive model-based decomposition method that could estimate both the mean orientation angle and a degree of randomness for the canopy scattering for each pixel in a SAR image without the reflection symmetry condition. This purpose of this research is to develop a new decomposition method based on second- and third-order statics analysis to estimate the surface, dihedral, volume and helix scattering components from polarimetric SAR images without the specific assumptions concerning the model for the volume scattering. In addition, we evaluate this method by using both simulation and real UAVSAR data and compare this method with other methods. We express the volume scattering component using the wire formula and formulate the relationship equation between backscattering echo and each component such as the surface, dihedral, volume and helix via linearization based on second- and third-order statics. In third-order statics, we calculate the correlation of the correlation coefficients for each polerimetric data and get one new relationship equation to estimate each polarization component such as HH, VV and VH for the volume. As a result, the equation for the helix component in this method is the same formula as one in Yamaguchi's method. However, the equation for the volume

Effects of magnesium-based hydrogen storage materials on the thermal decomposition, burning rate, and explosive heat of ammonium perchlorate-based composite solid propellant.

Science.gov (United States)

Liu, Leili; Li, Jie; Zhang, Lingyao; Tian, Siyu

2018-01-15

MgH 2 , Mg 2 NiH 4 , and Mg 2 CuH 3 were prepared, and their structure and hydrogen storage properties were determined through X-ray photoelectron spectroscopy and thermal analyzer. The effects of MgH 2 , Mg 2 NiH 4 , and Mg 2 CuH 3 on the thermal decomposition, burning rate, and explosive heat of ammonium perchlorate-based composite solid propellant were subsequently studied. Results indicated that MgH 2 , Mg 2 NiH 4 , and Mg 2 CuH 3 can decrease the thermal decomposition peak temperature and increase the total released heat of decomposition. These compounds can improve the effect of thermal decomposition of the propellant. The burning rates of the propellant increased using Mg-based hydrogen storage materials as promoter. The burning rates of the propellant also increased using MgH 2 instead of Al in the propellant, but its explosive heat was not enlarged. Nonetheless, the combustion heat of MgH 2 was higher than that of Al. A possible mechanism was thus proposed. Copyright © 2017. Published by Elsevier B.V.

Optimized waveform relaxation domain decomposition method for discrete finite volume non stationary convection diffusion equation

International Nuclear Information System (INIS)

Berthe, P.M.

2013-01-01

In the context of nuclear waste repositories, we consider the numerical discretization of the non stationary convection diffusion equation. Discontinuous physical parameters and heterogeneous space and time scales lead us to use different space and time discretizations in different parts of the domain. In this work, we choose the discrete duality finite volume (DDFV) scheme and the discontinuous Galerkin scheme in time, coupled by an optimized Schwarz waveform relaxation (OSWR) domain decomposition method, because this allows the use of non-conforming space-time meshes. The main difficulty lies in finding an upwind discretization of the convective flux which remains local to a sub-domain and such that the multi domain scheme is equivalent to the mono domain one. These difficulties are first dealt with in the one-dimensional context, where different discretizations are studied. The chosen scheme introduces a hybrid unknown on the cell interfaces. The idea of up winding with respect to this hybrid unknown is extended to the DDFV scheme in the two-dimensional setting. The well-posedness of the scheme and of an equivalent multi domain scheme is shown. The latter is solved by an OSWR algorithm, the convergence of which is proved. The optimized parameters in the Robin transmission conditions are obtained by studying the continuous or discrete convergence rates. Several test-cases, one of which inspired by nuclear waste repositories, illustrate these results. (author) [fr

Nonlinear QR code based optical image encryption using spiral phase transform, equal modulus decomposition and singular value decomposition

Science.gov (United States)

Kumar, Ravi; Bhaduri, Basanta; Nishchal, Naveen K.

2018-01-01

In this study, we propose a quick response (QR) code based nonlinear optical image encryption technique using spiral phase transform (SPT), equal modulus decomposition (EMD) and singular value decomposition (SVD). First, the primary image is converted into a QR code and then multiplied with a spiral phase mask (SPM). Next, the product is spiral phase transformed with particular spiral phase function, and further, the EMD is performed on the output of SPT, which results into two complex images, Z 1 and Z 2. Among these, Z 1 is further Fresnel propagated with distance d, and Z 2 is reserved as a decryption key. Afterwards, SVD is performed on Fresnel propagated output to get three decomposed matrices i.e. one diagonal matrix and two unitary matrices. The two unitary matrices are modulated with two different SPMs and then, the inverse SVD is performed using the diagonal matrix and modulated unitary matrices to get the final encrypted image. Numerical simulation results confirm the validity and effectiveness of the proposed technique. The proposed technique is robust against noise attack, specific attack, and brutal force attack. Simulation results are presented in support of the proposed idea.

Simulation-based optimization parametric optimization techniques and reinforcement learning

CERN Document Server

Gosavi, Abhijit

2003-01-01

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

On the hadron mass decomposition

Science.gov (United States)

Lorcé, Cédric

2018-02-01

We argue that the standard decompositions of the hadron mass overlook pressure effects, and hence should be interpreted with great care. Based on the semiclassical picture, we propose a new decomposition that properly accounts for these pressure effects. Because of Lorentz covariance, we stress that the hadron mass decomposition automatically comes along with a stability constraint, which we discuss for the first time. We show also that if a hadron is seen as made of quarks and gluons, one cannot decompose its mass into more than two contributions without running into trouble with the consistency of the physical interpretation. In particular, the so-called quark mass and trace anomaly contributions appear to be purely conventional. Based on the current phenomenological values, we find that in average quarks exert a repulsive force inside nucleons, balanced exactly by the gluon attractive force.

On the hadron mass decomposition

Energy Technology Data Exchange (ETDEWEB)

Lorce, Cedric [Universite Paris-Saclay, Centre de Physique Theorique, Ecole Polytechnique, CNRS, Palaiseau (France)

2018-02-15

We argue that the standard decompositions of the hadron mass overlook pressure effects, and hence should be interpreted with great care. Based on the semiclassical picture, we propose a new decomposition that properly accounts for these pressure effects. Because of Lorentz covariance, we stress that the hadron mass decomposition automatically comes along with a stability constraint, which we discuss for the first time. We show also that if a hadron is seen as made of quarks and gluons, one cannot decompose its mass into more than two contributions without running into trouble with the consistency of the physical interpretation. In particular, the so-called quark mass and trace anomaly contributions appear to be purely conventional. Based on the current phenomenological values, we find that in average quarks exert a repulsive force inside nucleons, balanced exactly by the gluon attractive force. (orig.)

Spectral decomposition of optimal asset-liability management

NARCIS (Netherlands)

Decamps, M.; de Schepper, A.; Goovaerts, M.

2009-01-01

This paper concerns optimal asset-liability management when the assets and the liabilities are modeled by means of correlated geometric Brownian motions as suggested in Gerber and Shiu [2003. Geometric Brownian motion models for assets and liabilities: from pension funding to optimal dividends.

Research on Multiaircraft Cooperative Suppression Interference Array Based on an Improved Multiobjective Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Huan Zhang

2017-01-01

Full Text Available For the problem of multiaircraft cooperative suppression interference array (MACSIA against the enemy air defense radar network in electronic warfare mission planning, firstly, the concept of route planning security zone is proposed and the solution to get the minimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security zone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function, and the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization algorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to maintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation, and the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained and verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle swarm optimization algorithm for solving the problem of MACSIA is feasible and effective.

Early stage litter decomposition across biomes

Science.gov (United States)

Ika Djukic; Sebastian Kepfer-Rojas; Inger Kappel Schmidt; Klaus Steenberg Larsen; Claus Beier; Björn Berg; Kris Verheyen; Adriano Caliman; Alain Paquette; Alba Gutiérrez-Girón; Alberto Humber; Alejandro Valdecantos; Alessandro Petraglia; Heather Alexander; Algirdas Augustaitis; Amélie Saillard; Ana Carolina Ruiz Fernández; Ana I. Sousa; Ana I. Lillebø; Anderson da Rocha Gripp; André-Jean Francez; Andrea Fischer; Andreas Bohner; Andrey Malyshev; Andrijana Andrić; Andy Smith; Angela Stanisci; Anikó Seres; Anja Schmidt; Anna Avila; Anne Probst; Annie Ouin; Anzar A. Khuroo; Arne Verstraeten; Arely N. Palabral-Aguilera; Artur Stefanski; Aurora Gaxiola; Bart Muys; Bernard Bosman; Bernd Ahrends; Bill Parker; Birgit Sattler; Bo Yang; Bohdan Juráni; Brigitta Erschbamer; Carmen Eugenia Rodriguez Ortiz; Casper T. Christiansen; E. Carol Adair; Céline Meredieu; Cendrine Mony; Charles A. Nock; Chi-Ling Chen; Chiao-Ping Wang; Christel Baum; Christian Rixen; Christine Delire; Christophe Piscart; Christopher Andrews; Corinna Rebmann; Cristina Branquinho; Dana Polyanskaya; David Fuentes Delgado; Dirk Wundram; Diyaa Radeideh; Eduardo Ordóñez-Regil; Edward Crawford; Elena Preda; Elena Tropina; Elli Groner; Eric Lucot; Erzsébet Hornung; Esperança Gacia; Esther Lévesque; Evanilde Benedito; Evgeny A. Davydov; Evy Ampoorter; Fabio Padilha Bolzan; Felipe Varela; Ferdinand Kristöfel; Fernando T. Maestre; Florence Maunoury-Danger; Florian Hofhansl; Florian Kitz; Flurin Sutter; Francisco Cuesta; Francisco de Almeida Lobo; Franco Leandro de Souza; Frank Berninger; Franz Zehetner; Georg Wohlfahrt; George Vourlitis; Geovana Carreño-Rocabado; Gina Arena; Gisele Daiane Pinha; Grizelle González; Guylaine Canut; Hanna Lee; Hans Verbeeck; Harald Auge; Harald Pauli; Hassan Bismarck Nacro; Héctor A. Bahamonde; Heike Feldhaar; Heinke Jäger; Helena C. Serrano; Hélène Verheyden; Helge Bruelheide; Henning Meesenburg; Hermann Jungkunst; Hervé Jactel; Hideaki Shibata; Hiroko Kurokawa; Hugo López Rosas; Hugo L. Rojas Villalobos; Ian Yesilonis; Inara Melece; Inge Van Halder; Inmaculada García Quirós; Isaac Makelele; Issaka Senou; István Fekete; Ivan Mihal; Ivika Ostonen; Jana Borovská; Javier Roales; Jawad Shoqeir; Jean-Christophe Lata; Jean-Paul Theurillat; Jean-Luc Probst; Jess Zimmerman; Jeyanny Vijayanathan; Jianwu Tang; Jill Thompson; Jiří Doležal; Joan-Albert Sanchez-Cabeza; Joël Merlet; Joh Henschel; Johan Neirynck; Johannes Knops; John Loehr; Jonathan von Oppen; Jónína Sigríður Þorláksdóttir; Jörg Löffler; José-Gilberto Cardoso-Mohedano; José-Luis Benito-Alonso; Jose Marcelo Torezan; Joseph C. Morina; Juan J. Jiménez; Juan Dario Quinde; Juha Alatalo; Julia Seeber; Jutta Stadler; Kaie Kriiska; Kalifa Coulibaly; Karibu Fukuzawa; Katalin Szlavecz; Katarína Gerhátová; Kate Lajtha; Kathrin Käppeler; Katie A. Jennings; Katja Tielbörger; Kazuhiko Hoshizaki; Ken Green; Lambiénou Yé; Laryssa Helena Ribeiro Pazianoto; Laura Dienstbach; Laura Williams; Laura Yahdjian; Laurel M. Brigham; Liesbeth van den Brink; Lindsey Rustad; al. et

2018-01-01

Through litter decomposition enormous amounts of carbon is emitted to the atmosphere. Numerous large-scale decomposition experiments have been conducted focusing on this fundamental soil process in order to understand the controls on the terrestrial carbon transfer to the atmosphere. However, previous studies were mostly based on site-specific litter and methodologies...

Bayesian Multi-Energy Computed Tomography reconstruction approaches based on decomposition models

International Nuclear Information System (INIS)

Cai, Caifang

2013-01-01

Multi-Energy Computed Tomography (MECT) makes it possible to get multiple fractions of basis materials without segmentation. In medical application, one is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical MECT measurements are usually obtained with polychromatic X-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam poly-chromaticity fail to estimate the correct decomposition fractions and result in Beam-Hardening Artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log pre-processing and the water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on non-linear forward models counting the beam poly-chromaticity show great potential for giving accurate fraction images.This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint Maximum A Posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a non-quadratic cost function. To solve it, the use of a monotone Conjugate Gradient (CG) algorithm with suboptimal descent steps is proposed.The performances of the proposed approach are analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also

Aging-driven decomposition in zolpidem hemitartrate hemihydrate and the single-crystal structure of its decomposition products.

Science.gov (United States)

Vega, Daniel R; Baggio, Ricardo; Roca, Mariana; Tombari, Dora

2011-04-01

The "aging-driven" decomposition of zolpidem hemitartrate hemihydrate (form A) has been followed by X-ray powder diffraction (XRPD), and the crystal and molecular structures of the decomposition products studied by single-crystal methods. The process is very similar to the "thermally driven" one, recently described in the literature for form E (Halasz and Dinnebier. 2010. J Pharm Sci 99(2): 871-874), resulting in a two-phase system: the neutral free base (common to both decomposition processes) and, in the present case, a novel zolpidem tartrate monohydrate, unique to the "aging-driven" decomposition. Our room-temperature single-crystal analysis gives for the free base comparable results as the high-temperature XRPD ones already reported by Halasz and Dinnebier: orthorhombic, Pcba, a = 9.6360(10) Å, b = 18.2690(5) Å, c = 18.4980(11) Å, and V = 3256.4(4) Å(3) . The unreported zolpidem tartrate monohydrate instead crystallizes in monoclinic P21 , which, for comparison purposes, we treated in the nonstandard setting P1121 with a = 20.7582(9) Å, b = 15.2331(5) Å, c = 7.2420(2) Å, γ = 90.826(2)°, and V = 2289.73(14) Å(3) . The structure presents two complete moieties in the asymmetric unit (z = 4, z' = 2). The different phases obtained in both decompositions are readily explained, considering the diverse genesis of both processes. Copyright © 2010 Wiley-Liss, Inc.

Synthesis of Phase-Only Reconfigurable Linear Arrays Using Multiobjective Invasive Weed Optimization Based on Decomposition

Directory of Open Access Journals (Sweden)

Yan Liu

2014-01-01

Full Text Available Synthesis of phase-only reconfigurable array aims at finding a common amplitude distribution and different phase distributions for the array to form different patterns. In this paper, the synthesis problem is formulated as a multiobjective optimization problem and solved by a new proposed algorithm MOEA/D-IWO. First, novel strategies are introduced in invasive weed optimization (IWO to make original IWO fit for solving multiobjective optimization problems; then, the modified IWO is integrated into the framework of the recently well proved competitive multiobjective optimization algorithm MOEA/D to form a new competitive MOEA/D-IWO algorithm. At last, two sets of experiments are carried out to illustrate the effectiveness of MOEA/D-IWO. In addition, MOEA/D-IWO is compared with MOEA/D-DE, a new version of MOEA/D. The comparing results show the superiority of MOEA/D-IWO and indicate its potential for solving the antenna array synthesis problems.

Solving network design problems via decomposition, aggregation and approximation

CERN Document Server

Bärmann, Andreas

2016-01-01

Andreas Bärmann develops novel approaches for the solution of network design problems as they arise in various contexts of applied optimization. At the example of an optimal expansion of the German railway network until 2030, the author derives a tailor-made decomposition technique for multi-period network design problems. Next, he develops a general framework for the solution of network design problems via aggregation of the underlying graph structure. This approach is shown to save much computation time as compared to standard techniques. Finally, the author devises a modelling framework for the approximation of the robust counterpart under ellipsoidal uncertainty, an often-studied case in the literature. Each of these three approaches opens up a fascinating branch of research which promises a better theoretical understanding of the problem and an increasing range of solvable application settings at the same time. Contents Decomposition for Multi-Period Network Design Solving Network Design Problems via Ag...

Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Boyang Qu

2017-12-01

Full Text Available The intermittency of wind power and the large-scale integration of electric vehicles (EVs bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

Polarimetric SAR interferometry-based decomposition modelling for reliable scattering retrieval

Science.gov (United States)

Agrawal, Neeraj; Kumar, Shashi; Tolpekin, Valentyn

2016-05-01

Fully Polarimetric SAR (PolSAR) data is used for scattering information retrieval from single SAR resolution cell. Single SAR resolution cell may contain contribution from more than one scattering objects. Hence, single or dual polarized data does not provide all the possible scattering information. So, to overcome this problem fully Polarimetric data is used. It was observed in previous study that fully Polarimetric data of different dates provide different scattering values for same object and coefficient of determination obtained from linear regression between volume scattering and aboveground biomass (AGB) shows different values for the SAR dataset of different dates. Scattering values are important input elements for modelling of forest aboveground biomass. In this research work an approach is proposed to get reliable scattering from interferometric pair of fully Polarimetric RADARSAT-2 data. The field survey for data collection was carried out for Barkot forest during November 10th to December 5th, 2014. Stratified random sampling was used to collect field data for circumference at breast height (CBH) and tree height measurement. Field-measured AGB was compared with the volume scattering elements obtained from decomposition modelling of individual PolSAR images and PolInSAR coherency matrix. Yamaguchi 4-component decomposition was implemented to retrieve scattering elements from SAR data. PolInSAR based decomposition was the great challenge in this work and it was implemented with certain assumptions to create Hermitian coherency matrix with co-registered polarimetric interferometric pair of SAR data. Regression analysis between field-measured AGB and volume scattering element obtained from PolInSAR data showed highest (0.589) coefficient of determination. The same regression with volume scattering elements of individual SAR images showed 0.49 and 0.50 coefficients of determination for master and slave images respectively. This study recommends use of

Optimal design of distributed control and embedded systems

CERN Document Server

Çela, Arben; Li, Xu-Guang; Niculescu, Silviu-Iulian

2014-01-01

Optimal Design of Distributed Control and Embedded Systems focuses on the design of special control and scheduling algorithms based on system structural properties as well as on analysis of the influence of induced time-delay on systems performances. It treats the optimal design of distributed and embedded control systems (DCESs) with respect to communication and calculation-resource constraints, quantization aspects, and potential time-delays induced by the associated  communication and calculation model. Particular emphasis is put on optimal control signal scheduling based on the system state. In order to render  this complex optimization problem feasible in real time, a time decomposition is based on periodicity induced by the static scheduling is operated. The authors present a co-design approach which subsumes the synthesis of the optimal control laws and the generation of an optimal schedule of control signals on real-time networks as well as the execution of control tasks on a single processor. The a...

Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

DEFF Research Database (Denmark)

Hansen, Per Christian; Jensen, Søren Holdt

2007-01-01

We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... with working Matlab code and applications in speech processing....

Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

Science.gov (United States)

Bagherzadeh, Seyed Amin; Asadi, Davood

2017-05-01

In search of a precise method for analyzing nonlinear and non-stationary flight data of an aircraft in the icing condition, an Empirical Mode Decomposition (EMD) algorithm enhanced by multi-objective optimization is introduced. In the proposed method, dissimilar IMF definitions are considered by the Genetic Algorithm (GA) in order to find the best decision parameters of the signal trend. To resolve disadvantages of the classical algorithm caused by the envelope concept, the signal trend is estimated directly in the proposed method. Furthermore, in order to simplify the performance and understanding of the EMD algorithm, the proposed method obviates the need for a repeated sifting process. The proposed enhanced EMD algorithm is verified by some benchmark signals. Afterwards, the enhanced algorithm is applied to simulated flight data in the icing condition in order to detect the ice assertion on the aircraft. The results demonstrate the effectiveness of the proposed EMD algorithm in aircraft ice detection by providing a figure of merit for the icing severity.

Decomposition of pre calcined aluminium silicate ores of Afghanistan by hydrochloric acid

International Nuclear Information System (INIS)

Khomidi, A.K.; Mamatov, E.D.

2015-01-01

Present article is devoted to decomposition of pre calcined aluminium silicate ores of Afghanistan by hydrochloric acid. The physicochemical properties of initial aluminium silicate ores were studied by means of X-ray phase, differential thermal and silicate analysis. The chemical composition of aluminium containing ores was determined. The optimal conditions of interaction of initial and pre calcined siallites with hydrochloric acid were defined. The kinetics of acid decomposition of aluminium silicate ores was studied as well.

A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme

Science.gov (United States)

Ghoman, Satyajit S.

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of

Mathematical modelling of the decomposition of explosives

International Nuclear Information System (INIS)

Smirnov, Lev P

2010-01-01

Studies on mathematical modelling of the molecular and supramolecular structures of explosives and the elementary steps and overall processes of their decomposition are analyzed. Investigations on the modelling of combustion and detonation taking into account the decomposition of explosives are also considered. It is shown that solution of problems related to the decomposition kinetics of explosives requires the use of a complex strategy based on the methods and concepts of chemical physics, solid state physics and theoretical chemistry instead of empirical approach.

Thermal Decomposition Characteristics of Orthorhombic Ammonium Perchlorate (o-AP) and an 0-AP/HTPB-Based Propellant

International Nuclear Information System (INIS)

BEHRENS JR., RICHARD; MINIER, LEANNA M.G.

1999-01-01

A study to characterize the low-temperature reactive processes for o-AP and an AP/HTPB-based propellant (class 1.3) is being conducted in the laboratory using the techniques of simultaneous thermogravimetric modulated beam mass spectrometry (STMBMS) and scanning electron microscopy (SEM). The results presented in this paper are a follow up of the previous work that showed the overall decomposition to be complex and controlled by both physical and chemical processes. The decomposition is characterized by the occurrence of one major event that consumes up to(approx)35% of the AP, depending upon particle size, and leaves behind a porous agglomerate of AP. The major gaseous products released during this event include H(sub 2)O, O(sub 2), Cl(sub 2), N(sub 2)O and HCl. The recent efforts provide further insight into the decomposition processes for o-AP. The temporal behaviors of the gas formation rates (GFRs) for the products indicate that the major decomposition event consists of three chemical channels. The first and third channels are affected by the pressure in the reaction cell and occur at the surface or in the gas phase above the surface of the AP particles. The second channel is not affected by pressure and accounts for the solid-phase reactions characteristic of o-AP. The third channel involves the interactions of the decomposition products with the surface of the AP. SEM images of partially decomposed o-AP provide insight to how the morphology changes as the decomposition progresses. A conceptual model has been developed, based upon the STMBMS and SEM results, that provides a basic description of the processes. The thermal decomposition characteristics of the propellant are evaluated from the identities of the products and the temporal behaviors of their GFRs. First, the volatile components in the propellant evolve from the propellant as it is heated. Second, the hot AP (and HClO(sub 4)) at the AP-binder interface oxidize the binder through reactions that

Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems

International Nuclear Information System (INIS)

Cao, Dingzhou; Murat, Alper; Chinnam, Ratna Babu

2013-01-01

This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliability or minimizing the cost given certain constraints. The few studies that treated redundancy allocation problem as a multi-objective optimization problem relied on meta-heuristic solution approaches. However, meta-heuristic approaches have significant limitations: they do not guarantee that Pareto points are optimal and, more importantly, they may not identify all the Pareto-optimal points. In this paper, we treat redundancy allocation problem as a multi-objective problem, as is typical in practice. We decompose the original problem into several multi-objective sub-problems, efficiently and exactly solve sub-problems, and then systematically combine the solutions. The decomposition-based approach can efficiently generate all the Pareto-optimal solutions for redundancy allocation problems. Experimental results demonstrate the effectiveness and efficiency of the proposed method over meta-heuristic methods on a numerical example taken from the literature.

Effect of catalyst for the decomposition of VOCs in a NTP reactor

International Nuclear Information System (INIS)

Mohanty, Suchitra; Das, Smrutiprava; Paikaray, Rita; Sahoo, Gourishankar; Samantaray, Subrata

2015-01-01

Air pollution has become a major cause of human distress both directly and indirectly. VOCs are becoming the major air pollutants. So the decomposition of VOCs is present need of our society. Non-thermal plasma reactor (NTP) is proven to be effective for low concentration VOCs decomposition. For safe and effective application of DBD, optimization of treatment process requires different plasma parameter characterization. So electron temperature and electron density parameters of VOCs show the decomposition path ways. In this piece of work by taking the emission spectra and comparing the line intensity ratios, the electron temperature and density were determined. Also the decomposition rate in terms of the deposited products on the dielectric surface was studied. Decomposition rate increases in presence of catalyst as compared to the pure compound in presence of a carrier gas. Decomposition process was studied by UV-VIS, FTIR, OES Spectroscopic methods and by GCMS. Deposited products are analyzed by UV-VIS and FTIR spectroscopy. Plasma parameters like electron temperature, density are studied with OES. And gaseous products are studied by GCMS showing the peaks for the by products. (author)

Research on technology of online gas chromatograph for SF6 decomposition products

Science.gov (United States)

Li, L.; Fan, X. P.; Zhou, Y. Y.; Tang, N.; Zou, Z. L.; Liu, M. Z.; Huang, G. J.

2017-12-01

Sulfur hexafluoride (SF6) decomposition products were qualitatively and quantitatively analyzed by several gas chromatographs in the laboratory. Test conditions and methods were selected and optimized to minimize and eliminate the SF6’ influences on detection of other trace components. The effective separation and detection of selected characteristic gases were achieved. And by comparison among different types of gas chromatograph, it was found that GPTR-S101 can effectively separate and detect SF6 decomposition products and has best the best detection limit and sensitivity. On the basis of GPTR-S101, online gas chromatograph for SF6decomposition products (GPTR-S201) was developed. It lays the foundation for further online monitoring and diagnosis of SF6.

The Speech multi features fusion perceptual hash algorithm based on tensor decomposition

Science.gov (United States)

Huang, Y. B.; Fan, M. H.; Zhang, Q. Y.

2018-03-01

With constant progress in modern speech communication technologies, the speech data is prone to be attacked by the noise or maliciously tampered. In order to make the speech perception hash algorithm has strong robustness and high efficiency, this paper put forward a speech perception hash algorithm based on the tensor decomposition and multi features is proposed. This algorithm analyses the speech perception feature acquires each speech component wavelet packet decomposition. LPCC, LSP and ISP feature of each speech component are extracted to constitute the speech feature tensor. Speech authentication is done by generating the hash values through feature matrix quantification which use mid-value. Experimental results showing that the proposed algorithm is robust for content to maintain operations compared with similar algorithms. It is able to resist the attack of the common background noise. Also, the algorithm is highly efficiency in terms of arithmetic, and is able to meet the real-time requirements of speech communication and complete the speech authentication quickly.

Decomposition mechanism of trichloroethylene based on by-product distribution in the hybrid barrier discharge plasma process

Energy Technology Data Exchange (ETDEWEB)

Han, Sang-Bo [Industry Applications Research Laboratory, Korea Electrotechnology Research Institute, Changwon, Kyeongnam (Korea, Republic of); Oda, Tetsuji [Department of Electrical Engineering, The University of Tokyo, Tokyo 113-8656 (Japan)

2007-05-15

The hybrid barrier discharge plasma process combined with ozone decomposition catalysts was studied experimentally for decomposing dilute trichloroethylene (TCE). Based on the fundamental experiment for catalytic activities on ozone decomposition, MnO{sub 2} was selected for application in the main experiments for its higher catalytic abilities than other metal oxides. A lower initial TCE concentration existed in the working gas; the larger ozone concentration was generated from the barrier discharge plasma treatment. Near complete decomposition of dichloro-acetylchloride (DCAC) into Cl{sub 2} and CO{sub x} was observed for an initial TCE concentration of less than 250 ppm. C=C {pi} bond cleavage in TCE gave a carbon single bond of DCAC through oxidation reaction during the barrier discharge plasma treatment. Those DCAC were easily broken in the subsequent catalytic reaction. While changing oxygen concentration in working gas, oxygen radicals in the plasma space strongly reacted with precursors of DCAC compared with those of trichloro-acetaldehyde. A chlorine radical chain reaction is considered as a plausible decomposition mechanism in the barrier discharge plasma treatment. The potential energy of oxygen radicals at the surface of the catalyst is considered as an important factor in causing reactive chemical reactions.

Decomposition techniques

Science.gov (United States)

Chao, T.T.; Sanzolone, R.F.

1992-01-01

Sample decomposition is a fundamental and integral step in the procedure of geochemical analysis. It is often the limiting factor to sample throughput, especially with the recent application of the fast and modern multi-element measurement instrumentation. The complexity of geological materials makes it necessary to choose the sample decomposition technique that is compatible with the specific objective of the analysis. When selecting a decomposition technique, consideration should be given to the chemical and mineralogical characteristics of the sample, elements to be determined, precision and accuracy requirements, sample throughput, technical capability of personnel, and time constraints. This paper addresses these concerns and discusses the attributes and limitations of many techniques of sample decomposition along with examples of their application to geochemical analysis. The chemical properties of reagents as to their function as decomposition agents are also reviewed. The section on acid dissolution techniques addresses the various inorganic acids that are used individually or in combination in both open and closed systems. Fluxes used in sample fusion are discussed. The promising microwave-oven technology and the emerging field of automation are also examined. A section on applications highlights the use of decomposition techniques for the determination of Au, platinum group elements (PGEs), Hg, U, hydride-forming elements, rare earth elements (REEs), and multi-elements in geological materials. Partial dissolution techniques used for geochemical exploration which have been treated in detail elsewhere are not discussed here; nor are fire-assaying for noble metals and decomposition techniques for X-ray fluorescence or nuclear methods be discussed. ?? 1992.

Fe catalysts for methane decomposition to produce hydrogen and carbon nano materials

KAUST Repository

Zhou, Lu; Enakonda, Linga Reddy; Harb, Moussab; Saih, Youssef; Aguilar Tapia, Antonio; Ould-Chikh, Samy; Hazemann, Jean-louis; Li, Jun; Wei, Nini; Gary, Daniel; Del-Gallo, Pascal; Basset, Jean-Marie

2017-01-01

Conducting catalytic methane decomposition over Fe catalysts is a green and economic route to produce H2 without CO/CO2 contamination. Fused 65wt% and impregnated 20wt% Fe catalysts were synthesized with different additives to investigate their activity, whereas showing Fe-Al2O3 combination as the best catalyst. Al2O3 is speculated to expose more Fe00 for the selective deposition of carbon nano tubes (CNTs). A fused Fe (65wt%)-Al2O3 sample was further investigated by means of H2-TPR, in-situ XRD, HRTEM and XAS to conclude 750°C is the optimized temperature for H2 pre-reduction and reaction to obtain a high activity. Based on density functional theory (DFT) study, a reaction mechanism over Fe catalysts was proposed to explain the formation of graphite from unstable supersaturated iron carbides decomposition. A carbon deposition model was further proposed which explains the formation of different carbon nano materials.

Fe catalysts for methane decomposition to produce hydrogen and carbon nano materials

KAUST Repository

Zhou, Lu

2017-02-21

Conducting catalytic methane decomposition over Fe catalysts is a green and economic route to produce H2 without CO/CO2 contamination. Fused 65wt% and impregnated 20wt% Fe catalysts were synthesized with different additives to investigate their activity, whereas showing Fe-Al2O3 combination as the best catalyst. Al2O3 is speculated to expose more Fe00 for the selective deposition of carbon nano tubes (CNTs). A fused Fe (65wt%)-Al2O3 sample was further investigated by means of H2-TPR, in-situ XRD, HRTEM and XAS to conclude 750°C is the optimized temperature for H2 pre-reduction and reaction to obtain a high activity. Based on density functional theory (DFT) study, a reaction mechanism over Fe catalysts was proposed to explain the formation of graphite from unstable supersaturated iron carbides decomposition. A carbon deposition model was further proposed which explains the formation of different carbon nano materials.

A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.

Science.gov (United States)

Yang, Shaofu; Liu, Qingshan; Wang, Jun

2018-04-01

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

Note on Symplectic SVD-Like Decomposition

Directory of Open Access Journals (Sweden)

AGOUJIL Said

2016-02-01

Full Text Available The aim of this study was to introduce a constructive method to compute a symplectic singular value decomposition (SVD-like decomposition of a 2n-by-m rectangular real matrix A, based on symplectic refectors.This approach used a canonical Schur form of skew-symmetric matrix and it allowed us to compute eigenvalues for the structured matrices as Hamiltonian matrix JAA^T.

Three-Component Decomposition Based on Stokes Vector for Compact Polarimetric SAR

Directory of Open Access Journals (Sweden)

Hanning Wang

2015-09-01

Full Text Available In this paper, a three-component decomposition algorithm is proposed for processing compact polarimetric SAR images. By using the correspondence between the covariance matrix and the Stokes vector, three-component scattering models for CTLR and DCP modes are established. The explicit expression of decomposition results is then derived by setting the contribution of volume scattering as a free parameter. The degree of depolarization is taken as the upper bound of the free parameter, for the constraint that the weighting factor of each scattering component should be nonnegative. Several methods are investigated to estimate the free parameter suitable for decomposition. The feasibility of this algorithm is validated by AIRSAR data over San Francisco and RADARSAT-2 data over Flevoland.

Decomposition studies of group 6 hexacarbonyl complexes. Pt. 2. Modelling of the decomposition process

Energy Technology Data Exchange (ETDEWEB)

Usoltsev, Ilya; Eichler, Robert; Tuerler, Andreas [Paul Scherrer Institut (PSI), Villigen (Switzerland); Bern Univ. (Switzerland)

2016-11-01

The decomposition behavior of group 6 metal hexacarbonyl complexes (M(CO){sub 6}) in a tubular flow reactor is simulated. A microscopic Monte-Carlo based model is presented for assessing the first bond dissociation enthalpy of M(CO){sub 6} complexes. The suggested approach superimposes a microscopic model of gas adsorption chromatography with a first-order heterogeneous decomposition model. The experimental data on the decomposition of Mo(CO){sub 6} and W(CO){sub 6} are successfully simulated by introducing available thermodynamic data. Thermodynamic data predicted by relativistic density functional theory is used in our model to deduce the most probable experimental behavior of the corresponding Sg carbonyl complex. Thus, the design of a chemical experiment with Sg(CO){sub 6} is suggested, which is sensitive to benchmark our theoretical understanding of the bond stability in carbonyl compounds of the heaviest elements.

Applications of tensor (multiway array) factorizations and decompositions in data mining

DEFF Research Database (Denmark)

Mørup, Morten

2011-01-01

Tensor (multiway array) factorization and decomposition has become an important tool for data mining. Fueled by the computational power of modern computer researchers can now analyze large-scale tensorial structured data that only a few years ago would have been impossible. Tensor factorizations...... have several advantages over two-way matrix factorizations including uniqueness of the optimal solution and component identification even when most of the data is missing. Furthermore, multiway decomposition techniques explicitly exploit the multiway structure that is lost when collapsing some...... of the modes of the tensor in order to analyze the data by regular matrix factorization approaches. Multiway decomposition is being applied to new fields every year and there is no doubt that the future will bring many exciting new applications. The aim of this overview is to introduce the basic concepts...

Exact Partial Information Decompositions for Gaussian Systems Based on Dependency Constraints

Directory of Open Access Journals (Sweden)

Jim W. Kay

2018-03-01

Full Text Available The Partial Information Decomposition, introduced by Williams P. L. et al. (2010, provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method ( I dep has recently been proposed by James R. G. et al. (2017 for computing a two-predictor partial information decomposition over discrete spaces. A lattice of maximum entropy probability models is constructed based on marginal dependency constraints, and the unique information that a particular predictor has about the target is defined as the minimum increase in joint predictor-target mutual information when that particular predictor-target marginal dependency is constrained. Here, we apply the I dep approach to Gaussian systems, for which the marginally constrained maximum entropy models are Gaussian graphical models. Closed form solutions for the I dep PID are derived for both univariate and multivariate Gaussian systems. Numerical and graphical illustrations are provided, together with practical and theoretical comparisons of the I dep PID with the minimum mutual information partial information decomposition ( I mmi , which was discussed by Barrett A. B. (2015. The results obtained using I dep appear to be more intuitive than those given with other methods, such as I mmi , in which the redundant and unique information components are constrained to depend only on the predictor-target marginal distributions. In particular, it is proved that the I mmi method generally produces larger estimates of redundancy and synergy than does the I dep method. In discussion of the practical examples, the PIDs are complemented by the use of tests of deviance for the comparison of Gaussian graphical models.

Design and cost of the sulfuric acid decomposition reactor for the sulfur based hydrogen processes - HTR2008-58009

International Nuclear Information System (INIS)

Hu, T. Y.; Connolly, S. M.; Lahoda, E. J.; Kriel, W.

2008-01-01

The key interface component between the reactor and chemical systems for the sulfuric acid based processes to make hydrogen is the sulfuric acid decomposition reactor. The materials issues for the decomposition reactor are severe since sulfuric acid must be heated, vaporized and decomposed. SiC has been identified and proven by others to be an acceptable material. However, SiC has a significant design issue when it must be interfaced with metals for connection to the remainder of the process. Westinghouse has developed a design utilizing SiC for the high temperature portions of the reactor that are in contact with the sulfuric acid and polymeric coated steel for low temperature portions. This design is expected to have a reasonable cost for an operating lifetime of 20 years. It can be readily maintained in the field, and is transportable by truck (maximum OD is 4.5 meters). This paper summarizes the detailed engineering design of the Westinghouse Decomposition Reactor and the decomposition reactor's capital cost. (authors)

Multiple Shooting and Time Domain Decomposition Methods

CERN Document Server

Geiger, Michael; Körkel, Stefan; Rannacher, Rolf

2015-01-01

This book offers a comprehensive collection of the most advanced numerical techniques for the efficient and effective solution of simulation and optimization problems governed by systems of time-dependent differential equations. The contributions present various approaches to time domain decomposition, focusing on multiple shooting and parareal algorithms.  The range of topics covers theoretical analysis of the methods, as well as their algorithmic formulation and guidelines for practical implementation. Selected examples show that the discussed approaches are mandatory for the solution of challenging practical problems. The practicability and efficiency of the presented methods is illustrated by several case studies from fluid dynamics, data compression, image processing and computational biology, giving rise to possible new research topics.  This volume, resulting from the workshop Multiple Shooting and Time Domain Decomposition Methods, held in Heidelberg in May 2013, will be of great interest to applied...

Quantum game theory based on the Schmidt decomposition

International Nuclear Information System (INIS)

Ichikawa, Tsubasa; Tsutsui, Izumi; Cheon, Taksu

2008-01-01

We present a novel formulation of quantum game theory based on the Schmidt decomposition, which has the merit that the entanglement of quantum strategies is manifestly quantified. We apply this formulation to 2-player, 2-strategy symmetric games and obtain a complete set of quantum Nash equilibria. Apart from those available with the maximal entanglement, these quantum Nash equilibria are extensions of the Nash equilibria in classical game theory. The phase structure of the equilibria is determined for all values of entanglement, and thereby the possibility of resolving the dilemmas by entanglement in the game of Chicken, the Battle of the Sexes, the Prisoners' Dilemma, and the Stag Hunt, is examined. We find that entanglement transforms these dilemmas with each other but cannot resolve them, except in the Stag Hunt game where the dilemma can be alleviated to a certain degree

Wavelet decomposition based principal component analysis for face recognition using MATLAB

Science.gov (United States)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition

Science.gov (United States)

Gou, Shuiping; Wang, Yueyue; Wang, Zhilong; Peng, Yong; Zhang, Xiaopeng; Jiao, Licheng; Wu, Jianshe

2013-01-01

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images. PMID:24023764

Wavelet optimization for content-based image retrieval in medical databases.

Science.gov (United States)

Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

2010-04-01

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study

International Nuclear Information System (INIS)

Zhai, Qingqing; Yang, Jun; Zhao, Yu

2014-01-01

Variance-based sensitivity analysis has been widely studied and asserted itself among practitioners. Monte Carlo simulation methods are well developed in the calculation of variance-based sensitivity indices but they do not make full use of each model run. Recently, several works mentioned a scatter-plot partitioning method to estimate the variance-based sensitivity indices from given data, where a single bunch of samples is sufficient to estimate all the sensitivity indices. This paper focuses on the space-partition method in the estimation of variance-based sensitivity indices, and its convergence and other performances are investigated. Since the method heavily depends on the partition scheme, the influence of the partition scheme is discussed and the optimal partition scheme is proposed based on the minimized estimator's variance. A decomposition and integration procedure is proposed to improve the estimation quality for higher order sensitivity indices. The proposed space-partition method is compared with the more traditional method and test cases show that it outperforms the traditional one

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

Science.gov (United States)

Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

2017-08-01

Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

Two Topics in Data Analysis: Sample-based Optimal Transport and Analysis of Turbulent Spectra from Ship Track Data

Science.gov (United States)

Kuang, Simeng Max

This thesis contains two topics in data analysis. The first topic consists of the introduction of algorithms for sample-based optimal transport and barycenter problems. In chapter 1, a family of algorithms is introduced to solve both the L2 optimal transport problem and the Wasserstein barycenter problem. Starting from a theoretical perspective, the new algorithms are motivated from a key characterization of the barycenter measure, which suggests an update that reduces the total transportation cost and stops only when the barycenter is reached. A series of general theorems is given to prove the convergence of all the algorithms. We then extend the algorithms to solve sample-based optimal transport and barycenter problems, in which only finite sample sets are available instead of underlying probability distributions. A unique feature of the new approach is that it compares sample sets in terms of the expected values of a set of feature functions, which at the same time induce the function space of optimal maps and can be chosen by users to incorporate their prior knowledge of the data. All the algorithms are implemented and applied to various synthetic example and practical applications. On synthetic examples it is found that both the SOT algorithm and the SCB algorithm are able to find the true solution and often converge in a handful of iterations. On more challenging applications including Gaussian mixture models, color transfer and shape transform problems, the algorithms give very good results throughout despite the very different nature of the corresponding datasets. In chapter 2, a preconditioning procedure is developed for the L2 and more general optimal transport problems. The procedure is based on a family of affine map pairs, which transforms the original measures into two new measures that are closer to each other, while preserving the optimality of solutions. It is proved that the preconditioning procedure minimizes the remaining transportation cost

A Matrix-Free Posterior Ensemble Kalman Filter Implementation Based on a Modified Cholesky Decomposition

Directory of Open Access Journals (Sweden)

Elias D. Nino-Ruiz

2017-07-01

Full Text Available In this paper, a matrix-free posterior ensemble Kalman filter implementation based on a modified Cholesky decomposition is proposed. The method works as follows: the precision matrix of the background error distribution is estimated based on a modified Cholesky decomposition. The resulting estimator can be expressed in terms of Cholesky factors which can be updated based on a series of rank-one matrices in order to approximate the precision matrix of the analysis distribution. By using this matrix, the posterior ensemble can be built by either sampling from the posterior distribution or using synthetic observations. Furthermore, the computational effort of the proposed method is linear with regard to the model dimension and the number of observed components from the model domain. Experimental tests are performed making use of the Lorenz-96 model. The results reveal that, the accuracy of the proposed implementation in terms of root-mean-square-error is similar, and in some cases better, to that of a well-known ensemble Kalman filter (EnKF implementation: the local ensemble transform Kalman filter. In addition, the results are comparable to those obtained by the EnKF with large ensemble sizes.

Strategic bidding in electricity markets using particle swarm optimization

International Nuclear Information System (INIS)

Yucekaya, Ahmet D.; Valenzuela, Jorge; Dozier, Gerry

2009-01-01

Profit maximization for power companies is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. In this paper, we present two particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market. The first method uses a conventional PSO technique to find solutions. The second method uses a decomposition technique in conjunction with the PSO approach. This new decomposition-based PSO dramatically outperforms the conventional form of PSO. We show that for nonlinear cost functions PSO solutions provide higher expected profits than marginal cost-based bidding. (author)

Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

Science.gov (United States)

Baraldi, P.; Bonfanti, G.; Zio, E.

2018-03-01

The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.

Calculation and decomposition of spot price using interior point nonlinear optimisation methods

International Nuclear Information System (INIS)

Xie, K.; Song, Y.H.

2004-01-01

Optimal pricing for real and reactive power is a very important issue in a deregulation environment. This paper summarises the optimal pricing problem as an extended optimal power flow problem. Then, spot prices are decomposed into different components reflecting various ancillary services. The derivation of the proposed decomposition model is described in detail. Primary-Dual Interior Point method is applied to avoid 'go' 'no go' gauge. In addition, the proposed approach can be extended to cater for other types of ancillary services. (author)

An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

Science.gov (United States)

Cui, Xinchun; Niu, Yuying; Zheng, Xiangwei; Han, Yingshuai

2018-01-01

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness.

Combinatorial geometry domain decomposition strategies for Monte Carlo simulations

Energy Technology Data Exchange (ETDEWEB)

Li, G.; Zhang, B.; Deng, L.; Mo, Z.; Liu, Z.; Shangguan, D.; Ma, Y.; Li, S.; Hu, Z. [Institute of Applied Physics and Computational Mathematics, Beijing, 100094 (China)

2013-07-01

Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)

Combinatorial geometry domain decomposition strategies for Monte Carlo simulations

International Nuclear Information System (INIS)

Li, G.; Zhang, B.; Deng, L.; Mo, Z.; Liu, Z.; Shangguan, D.; Ma, Y.; Li, S.; Hu, Z.

2013-01-01

Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)

Wavelet decomposition and neuro-fuzzy hybrid system applied to short-term wind power

Energy Technology Data Exchange (ETDEWEB)

Fernandez-Jimenez, L.A.; Mendoza-Villena, M. [La Rioja Univ., Logrono (Spain). Dept. of Electrical Engineering; Ramirez-Rosado, I.J.; Abebe, B. [Zaragoza Univ., Zaragoza (Spain). Dept. of Electrical Engineering

2010-03-09

Wind energy has become increasingly popular as a renewable energy source. However, the integration of wind farms in the electrical power systems presents several problems, including the chaotic fluctuation of wind flow which results in highly varied power generation from a wind farm. An accurate forecast of wind power generation has important consequences in the economic operation of the integrated power system. This paper presented a new statistical short-term wind power forecasting model based on wavelet decomposition and neuro-fuzzy systems optimized with a genetic algorithm. The paper discussed wavelet decomposition; the proposed wind power forecasting model; and computer results. The original time series, the mean electric power generated in a wind farm, was decomposing into wavelet coefficients that were utilized as inputs for the forecasting model. The forecasting results obtained with the final models were compared to those obtained with traditional forecasting models showing a better performance for all the forecasting horizons. 13 refs., 1 tab., 4 figs.

A medium term bulk production cost model based on decomposition techniques

Energy Technology Data Exchange (ETDEWEB)

Ramos, A.; Munoz, L. [Univ. Pontificia Comillas, Madrid (Spain). Inst. de Investigacion Tecnologica; Martinez-Corcoles, F.; Martin-Corrochano, V. [IBERDROLA, Madrid (Spain)

1995-11-01

This model provides the minimum variable cost subject to operating constraints (generation, transmission and fuel constraints). Generation constraints include power reserve margin with respect to the system peak load, first Kirchhoff`s law at each node, hydro energy scheduling, maintenance scheduling, and generation limitations. Transmission constraints cover the second Kirchhoff`s law and transmission limitations. The generation and transmission economic dispatch is approximated by the linearized (also called DC) load flow. Network losses are included as a non linear approximation. Fuel constraints include minimum consumption quotas and fuel scheduling for domestic coal thermal plants. This production costing problem is formulated as a large-scale non linear optimization problem solved by generalized Benders decomposition method. Master problem determines the inter-period decisions, i.e., maintenance, fuel and hydro scheduling, and each subproblem solves the intra-period decisions, i.e., generation and transmission economic dispatch for one period. The model has been implemented in GAMS, a mathematical programming language. An application to the large-scale Spanish electric power system is presented. 11 refs

Phase-only asymmetric optical cryptosystem based on random modulus decomposition

Science.gov (United States)

Xu, Hongfeng; Xu, Wenhui; Wang, Shuaihua; Wu, Shaofan

2018-06-01

We propose a phase-only asymmetric optical cryptosystem based on random modulus decomposition (RMD). The cryptosystem is presented for effectively improving the capacity to resist various attacks, including the attack of iterative algorithms. On the one hand, RMD and phase encoding are combined to remove the constraints that can be used in the attacking process. On the other hand, the security keys (geometrical parameters) introduced by Fresnel transform can increase the key variety and enlarge the key space simultaneously. Numerical simulation results demonstrate the strong feasibility, security and robustness of the proposed cryptosystem. This cryptosystem will open up many new opportunities in the application fields of optical encryption and authentication.

Analysis of large fault trees based on functional decomposition

International Nuclear Information System (INIS)

Contini, Sergio; Matuzas, Vaidas

2011-01-01

With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis, a significant enhancement has been achieved with respect to previous approaches, both in terms of efficiency and accuracy of the overall outcome of the analysis. However, the exponential increase of the number of nodes with the complexity of the fault tree may prevent the construction of the BDD. In these cases, the only way to complete the analysis is to reduce the complexity of the BDD by applying the truncation technique, which nevertheless implies the problem of estimating the truncation error or upper and lower bounds of the top-event unavailability. This paper describes a new method to analyze large coherent fault trees which can be advantageously applied when the working memory is not sufficient to construct the BDD. It is based on the decomposition of the fault tree into simpler disjoint fault trees containing a lower number of variables. The analysis of each simple fault tree is performed by using all the computational resources. The results from the analysis of all simpler fault trees are re-combined to obtain the results for the original fault tree. Two decomposition methods are herewith described: the first aims at determining the minimal cut sets (MCS) and the upper and lower bounds of the top-event unavailability; the second can be applied to determine the exact value of the top-event unavailability. Potentialities, limitations and possible variations of these methods will be discussed with reference to the results of their application to some complex fault trees.

Analysis of large fault trees based on functional decomposition

Energy Technology Data Exchange (ETDEWEB)

Contini, Sergio, E-mail: sergio.contini@jrc.i [European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, 21020 Ispra (Italy); Matuzas, Vaidas [European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, 21020 Ispra (Italy)

2011-03-15

With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis, a significant enhancement has been achieved with respect to previous approaches, both in terms of efficiency and accuracy of the overall outcome of the analysis. However, the exponential increase of the number of nodes with the complexity of the fault tree may prevent the construction of the BDD. In these cases, the only way to complete the analysis is to reduce the complexity of the BDD by applying the truncation technique, which nevertheless implies the problem of estimating the truncation error or upper and lower bounds of the top-event unavailability. This paper describes a new method to analyze large coherent fault trees which can be advantageously applied when the working memory is not sufficient to construct the BDD. It is based on the decomposition of the fault tree into simpler disjoint fault trees containing a lower number of variables. The analysis of each simple fault tree is performed by using all the computational resources. The results from the analysis of all simpler fault trees are re-combined to obtain the results for the original fault tree. Two decomposition methods are herewith described: the first aims at determining the minimal cut sets (MCS) and the upper and lower bounds of the top-event unavailability; the second can be applied to determine the exact value of the top-event unavailability. Potentialities, limitations and possible variations of these methods will be discussed with reference to the results of their application to some complex fault trees.

Nested grids ILU-decomposition (NGILU)

NARCIS (Netherlands)

Ploeg, A. van der; Botta, E.F.F.; Wubs, F.W.

1996-01-01

A preconditioning technique is described which shows, in many cases, grid-independent convergence. This technique only requires an ordering of the unknowns based on the different levels of multigrid, and an incomplete LU-decomposition based on a drop tolerance. The method is demonstrated on a

Effect of microbial enzyme allocation strategies on stoichiometry of soil organic matter (SOM) decomposition

Science.gov (United States)

Wutzler, Thomas

2014-05-01

We explored different strategies of soil microbial community to invest resources into extracellular enzymes by conceptual modelling. Similar to the EEZY model by Moorhead et al. (2012), microbial community can invest into two separate pools of enzymes that depolymerize two different SOM pools. We show that with assuming that a fixed fraction of substrate uptake is allocated to enzymes, the microbial dynamics decouples from decomposition dynamics. We propose an alternative formulation where investment into enzymes is proportional to microbial biomass. Next, we show that the strategy of optimizing stoichiometry of decomposition flux according to microbial biomass stoichiometry yield less microbial growth than the strategy of optimizing revenue of the currently limiting element. However, both strategies result in better usage of the resources, i.e. less C overflow or N mineralization, than the strategy of equal allocation to both enzymes. Further, we discuss effects of those strategies on decomposition of SOM and priming at different time scales and discuss several abstractions from the detailed model dynamics for usage in larger scale models.

Optimal truss and frame design from projected homogenization-based topology optimization

DEFF Research Database (Denmark)

Larsen, S. D.; Sigmund, O.; Groen, J. P.

2018-01-01

In this article, we propose a novel method to obtain a near-optimal frame structure, based on the solution of a homogenization-based topology optimization model. The presented approach exploits the equivalence between Michell’s problem of least-weight trusses and a compliance minimization problem...... using optimal rank-2 laminates in the low volume fraction limit. In a fully automated procedure, a discrete structure is extracted from the homogenization-based continuum model. This near-optimal structure is post-optimized as a frame, where the bending stiffness is continuously decreased, to allow...

Amplitude Modulated Sinusoidal Signal Decomposition for Audio Coding

DEFF Research Database (Denmark)

Christensen, M. G.; Jacobson, A.; Andersen, S. V.

2006-01-01

In this paper, we present a decomposition for sinusoidal coding of audio, based on an amplitude modulation of sinusoids via a linear combination of arbitrary basis vectors. The proposed method, which incorporates a perceptual distortion measure, is based on a relaxation of a nonlinear least......-squares minimization. Rate-distortion curves and listening tests show that, compared to a constant-amplitude sinusoidal coder, the proposed decomposition offers perceptually significant improvements in critical transient signals....

Experimental investigation of the catalytic decomposition and combustion characteristics of a non-toxic ammonium dinitramide (ADN)-based monopropellant thruster

Science.gov (United States)

Chen, Jun; Li, Guoxiu; Zhang, Tao; Wang, Meng; Yu, Yusong

2016-12-01

Low toxicity ammonium dinitramide (ADN)-based aerospace propulsion systems currently show promise with regard to applications such as controlling satellite attitude. In the present work, the decomposition and combustion processes of an ADN-based monopropellant thruster were systematically studied, using a thermally stable catalyst to promote the decomposition reaction. The performance of the ADN propulsion system was investigated using a ground test system under vacuum, and the physical properties of the ADN-based propellant were also examined. Using this system, the effects of the preheating temperature and feed pressure on the combustion characteristics and thruster performance during steady state operation were observed. The results indicate that the propellant and catalyst employed during this work, as well as the design and manufacture of the thruster, met performance requirements. Moreover, the 1 N ADN thruster generated a specific impulse of 223 s, demonstrating the efficacy of the new catalyst. The thruster operational parameters (specifically, the preheating temperature and feed pressure) were found to have a significant effect on the decomposition and combustion processes within the thruster, and the performance of the thruster was demonstrated to improve at higher feed pressures and elevated preheating temperatures. A lower temperature of 140 °C was determined to activate the catalytic decomposition and combustion processes more effectively compared with the results obtained using other conditions. The data obtained in this study should be beneficial to future systematic and in-depth investigations of the combustion mechanism and characteristics within an ADN thruster.

Reference-tracking feedforward control design for linear dynamical systems through signal decomposition

NARCIS (Netherlands)

Kasemsinsup, Y.; Romagnoli, R.; Heertjes, M.F.; Weiland, S.; Butler, H.

2017-01-01

In this work, we study a novel approach towards the reference-tracking feedforward control design for linear dynamical systems. By utilizing the superposition property and exploiting signal decomposition together with a quadratic optimization process, we obtain a feedforward design procedure for

Linear decomposition approach for a class of nonconvex programming problems.

Science.gov (United States)

Shen, Peiping; Wang, Chunfeng

2017-01-01

This paper presents a linear decomposition approach for a class of nonconvex programming problems by dividing the input space into polynomially many grids. It shows that under certain assumptions the original problem can be transformed and decomposed into a polynomial number of equivalent linear programming subproblems. Based on solving a series of liner programming subproblems corresponding to those grid points we can obtain the near-optimal solution of the original problem. Compared to existing results in the literature, the proposed algorithm does not require the assumptions of quasi-concavity and differentiability of the objective function, and it differs significantly giving an interesting approach to solving the problem with a reduced running time.

Proof of the 1-factorization and Hamilton decomposition conjectures

CERN Document Server

Csaba, Béla; Lo, Allan; Osthus, Deryk; Treglown, Andrew

2016-01-01

In this paper the authors prove the following results (via a unified approach) for all sufficiently large n: (i) [1-factorization conjecture] Suppose that n is even and D\\geq 2\\lceil n/4\\rceil -1. Then every D-regular graph G on n vertices has a decomposition into perfect matchings. Equivalently, \\chi'(G)=D. (ii) [Hamilton decomposition conjecture] Suppose that D \\ge \\lfloor n/2 \\rfloor . Then every D-regular graph G on n vertices has a decomposition into Hamilton cycles and at most one perfect matching. (iii) [Optimal packings of Hamilton cycles] Suppose that G is a graph on n vertices with minimum degree \\delta\\ge n/2. Then G contains at least {\\rm reg}_{\\rm even}(n,\\delta)/2 \\ge (n-2)/8 edge-disjoint Hamilton cycles. Here {\\rm reg}_{\\rm even}(n,\\delta) denotes the degree of the largest even-regular spanning subgraph one can guarantee in a graph on n vertices with minimum degree \\delta. (i) was first explicitly stated by Chetwynd and Hilton. (ii) and the special case \\delta= \\lceil n/2 \\rceil of (iii) answe...

Advanced Oxidation: Oxalate Decomposition Testing With Ozone

International Nuclear Information System (INIS)

Ketusky, E.; Subramanian, K.

2012-01-01

At the Savannah River Site (SRS), oxalic acid is currently considered the preferred agent for chemically cleaning the large underground Liquid Radioactive Waste Tanks. It is applied only in the final stages of emptying a tank when generally less than 5,000 kg of waste solids remain, and slurrying based removal methods are no-longer effective. The use of oxalic acid is preferred because of its combined dissolution and chelating properties, as well as the fact that corrosion to the carbon steel tank walls can be controlled. Although oxalic acid is the preferred agent, there are significant potential downstream impacts. Impacts include: (1) Degraded evaporator operation; (2) Resultant oxalate precipitates taking away critically needed operating volume; and (3) Eventual creation of significant volumes of additional feed to salt processing. As an alternative to dealing with the downstream impacts, oxalate decomposition using variations of ozone based Advanced Oxidation Process (AOP) were investigated. In general AOPs use ozone or peroxide and a catalyst to create hydroxyl radicals. Hydroxyl radicals have among the highest oxidation potentials, and are commonly used to decompose organics. Although oxalate is considered among the most difficult organic to decompose, the ability of hydroxyl radicals to decompose oxalate is considered to be well demonstrated. In addition, as AOPs are considered to be 'green' their use enables any net chemical additions to the waste to be minimized. In order to test the ability to decompose the oxalate and determine the decomposition rates, a test rig was designed, where 10 vol% ozone would be educted into a spent oxalic acid decomposition loop, with the loop maintained at 70 C and recirculated at 40L/min. Each of the spent oxalic acid streams would be created from three oxalic acid strikes of an F-area simulant (i.e., Purex = high Fe/Al concentration) and H-area simulant (i.e., H area modified Purex = high Al/Fe concentration) after nearing

ADVANCED OXIDATION: OXALATE DECOMPOSITION TESTING WITH OZONE

Energy Technology Data Exchange (ETDEWEB)

Ketusky, E.; Subramanian, K.

2012-02-29

At the Savannah River Site (SRS), oxalic acid is currently considered the preferred agent for chemically cleaning the large underground Liquid Radioactive Waste Tanks. It is applied only in the final stages of emptying a tank when generally less than 5,000 kg of waste solids remain, and slurrying based removal methods are no-longer effective. The use of oxalic acid is preferred because of its combined dissolution and chelating properties, as well as the fact that corrosion to the carbon steel tank walls can be controlled. Although oxalic acid is the preferred agent, there are significant potential downstream impacts. Impacts include: (1) Degraded evaporator operation; (2) Resultant oxalate precipitates taking away critically needed operating volume; and (3) Eventual creation of significant volumes of additional feed to salt processing. As an alternative to dealing with the downstream impacts, oxalate decomposition using variations of ozone based Advanced Oxidation Process (AOP) were investigated. In general AOPs use ozone or peroxide and a catalyst to create hydroxyl radicals. Hydroxyl radicals have among the highest oxidation potentials, and are commonly used to decompose organics. Although oxalate is considered among the most difficult organic to decompose, the ability of hydroxyl radicals to decompose oxalate is considered to be well demonstrated. In addition, as AOPs are considered to be 'green' their use enables any net chemical additions to the waste to be minimized. In order to test the ability to decompose the oxalate and determine the decomposition rates, a test rig was designed, where 10 vol% ozone would be educted into a spent oxalic acid decomposition loop, with the loop maintained at 70 C and recirculated at 40L/min. Each of the spent oxalic acid streams would be created from three oxalic acid strikes of an F-area simulant (i.e., Purex = high Fe/Al concentration) and H-area simulant (i.e., H area modified Purex = high Al/Fe concentration

First-principles calculated decomposition pathways for LiBH4 nanoclusters

Science.gov (United States)

Huang, Zhi-Quan; Chen, Wei-Chih; Chuang, Feng-Chuan; Majzoub, Eric H.; Ozoliņš, Vidvuds

2016-05-01

We analyze thermodynamic stability and decomposition pathways of LiBH4 nanoclusters using grand-canonical free-energy minimization based on total energies and vibrational frequencies obtained from density-functional theory (DFT) calculations. We consider (LiBH4)n nanoclusters with n = 2 to 12 as reactants, while the possible products include (Li)n, (B)n, (LiB)n, (LiH)n, and Li2BnHn; off-stoichiometric LinBnHm (m ≤ 4n) clusters were considered for n = 2, 3, and 6. Cluster ground-state configurations have been predicted using prototype electrostatic ground-state (PEGS) and genetic algorithm (GA) based structural optimizations. Free-energy calculations show hydrogen release pathways markedly differ from those in bulk LiBH4. While experiments have found that the bulk material decomposes into LiH and B, with Li2B12H12 as a kinetically inhibited intermediate phase, (LiBH4)n nanoclusters with n ≤ 12 are predicted to decompose into mixed LinBn clusters via a series of intermediate clusters of LinBnHm (m ≤ 4n). The calculated pressure-composition isotherms and temperature-pressure isobars exhibit sloping plateaus due to finite size effects on reaction thermodynamics. Generally, decomposition temperatures of free-standing clusters are found to increase with decreasing cluster size due to thermodynamic destabilization of reaction products.

Implementation of domain decomposition and data decomposition algorithms in RMC code

International Nuclear Information System (INIS)

Liang, J.G.; Cai, Y.; Wang, K.; She, D.

2013-01-01

The applications of Monte Carlo method in reactor physics analysis is somewhat restricted due to the excessive memory demand in solving large-scale problems. Memory demand in MC simulation is analyzed firstly, it concerns geometry data, data of nuclear cross-sections, data of particles, and data of tallies. It appears that tally data is dominant in memory cost and should be focused on in solving the memory problem. Domain decomposition and tally data decomposition algorithms are separately designed and implemented in the reactor Monte Carlo code RMC. Basically, the domain decomposition algorithm is a strategy of 'divide and rule', which means problems are divided into different sub-domains to be dealt with separately and some rules are established to make sure the whole results are correct. Tally data decomposition consists in 2 parts: data partition and data communication. Two algorithms with differential communication synchronization mechanisms are proposed. Numerical tests have been executed to evaluate performance of the new algorithms. Domain decomposition algorithm shows potentials to speed up MC simulation as a space parallel method. As for tally data decomposition algorithms, memory size is greatly reduced

Spectral decomposition of tent maps using symmetry considerations

International Nuclear Information System (INIS)

Ordonez, G.E.; Driebe, D.J.

1996-01-01

The spectral decompostion of the Frobenius-Perron operator of maps composed of many tents is determined from symmetry considerations. The eigenstates involve Euler as well as Bernoulli polynomials. The authors have introduced some new techniques, based on symmetry considerations, enabling the construction of spectral decompositions in a much simpler way than previous construction algorithms, Here we utilize these techniques to construct the spectral decomposition for one- dimensional maps of the unit interval composed of many tents. The construction uses the knowledge of the spectral decomposition of the r-adic map, which involves Bernoulli polynomials and their duals. It will be seen that the spectral decomposition of the tent maps involves both Bernoulli polynomials and Euler polynomials along with the appropriate dual states

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

Naveed ur Rehman

2015-05-01

Full Text Available A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA, discrete wavelet transform (DWT and non-subsampled contourlet transform (NCT. A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Thermal decomposition of biphenyl (1963); Decomposition thermique du biphenyle (1963)

Energy Technology Data Exchange (ETDEWEB)

Clerc, M [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

1962-06-15

The rates of formation of the decomposition products of biphenyl; hydrogen, methane, ethane, ethylene, as well as triphenyl have been measured in the vapour and liquid phases at 460 deg. C. The study of the decomposition products of biphenyl at different temperatures between 400 and 460 deg. C has provided values of the activation energies of the reactions yielding the main products of pyrolysis in the vapour phase. Product and Activation energy: Hydrogen 73 {+-} 2 kCal/Mole; Benzene 76 {+-} 2 kCal/Mole; Meta-triphenyl 53 {+-} 2 kCal/Mole; Biphenyl decomposition 64 {+-} 2 kCal/Mole; The rate of disappearance of biphenyl is only very approximately first order. These results show the major role played at the start of the decomposition by organic impurities which are not detectable by conventional physico-chemical analysis methods and the presence of which accelerates noticeably the decomposition rate. It was possible to eliminate these impurities by zone-melting carried out until the initial gradient of the formation curves for the products became constant. The composition of the high-molecular weight products (over 250) was deduced from the mean molecular weight and the dosage of the aromatic C - H bonds by infrared spectrophotometry. As a result the existence in tars of hydrogenated tetra, penta and hexaphenyl has been demonstrated. (author) [French] Les vitesses de formation des produits de decomposition du biphenyle: hydrogene, methane, ethane, ethylene, ainsi que des triphenyles, ont ete mesurees en phase vapeur et en phase liquide a 460 deg. C. L'etude des produits de decomposition du biphenyle a differentes temperatures comprises entre 400 et 460 deg. C, a fourni les valeurs des energies d'activation des reactions conduisant aux principaux produits de la pyrolyse en phase vapeur. Produit et Energie d'activation: Hydrogene 73 {+-} 2 kcal/Mole; Benzene 76 {+-} 2 kcal/Mole; Metatriphenyle, 53 {+-} 2 kcal/Mole; Decomposition du biphenyle 64 {+-} 2 kcal/Mole; La

Reliability-based optimization of engineering structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

2008-01-01

The theoretical basis for reliability-based structural optimization within the framework of Bayesian statistical decision theory is briefly described. Reliability-based cost benefit problems are formulated and exemplitied with structural optimization. The basic reliability-based optimization...... problems are generalized to the following extensions: interactive optimization, inspection and repair costs, systematic reconstruction, re-assessment of existing structures. Illustrative examples are presented including a simple introductory example, a decision problem related to bridge re...

Kinetic study of lithium-cadmium ternary amalgam decomposition

International Nuclear Information System (INIS)

Cordova, M.H.; Andrade, C.E.

1992-01-01

The effect of metals, which form stable lithium phase in binary alloys, on the formation of intermetallic species in ternary amalgams and their effect on thermal decomposition in contact with water is analyzed. Cd is selected as ternary metal, based on general experimental selection criteria. Cd (Hg) binary amalgams are prepared by direct contact Cd-Hg, whereas Li is formed by electrolysis of Li OH aq using a liquid Cd (Hg) cathodic well. The decomposition kinetic of Li C(Hg) in contact with 0.6 M Li OH is studied in function of ageing and temperature, and these results are compared with the binary amalgam Li (Hg) decomposition. The decomposition rate is constant during one hour for binary and ternary systems. Ageing does not affect the binary systems but increases the decomposition activation energy of ternary systems. A reaction mechanism that considers an intermetallic specie participating in the activated complex is proposed and a kinetic law is suggested. (author)

Quantitative material decomposition using spectral computed tomography with an energy-resolved photon-counting detector

International Nuclear Information System (INIS)

Lee, Seungwan; Choi, Yu-Na; Kim, Hee-Joung

2014-01-01

Dual-energy computed tomography (CT) techniques have been used to decompose materials and characterize tissues according to their physical and chemical compositions. However, these techniques are hampered by the limitations of conventional x-ray detectors operated in charge integrating mode. Energy-resolved photon-counting detectors provide spectral information from polychromatic x-rays using multiple energy thresholds. These detectors allow simultaneous acquisition of data in different energy ranges without spectral overlap, resulting in more efficient material decomposition and quantification for dual-energy CT. In this study, a pre-reconstruction dual-energy CT technique based on volume conservation was proposed for three-material decomposition. The technique was combined with iterative reconstruction algorithms by using a ray-driven projector in order to improve the quality of decomposition images and reduce radiation dose. A spectral CT system equipped with a CZT-based photon-counting detector was used to implement the proposed dual-energy CT technique. We obtained dual-energy images of calibration and three-material phantoms consisting of low atomic number materials from the optimal energy bins determined by Monte Carlo simulations. The material decomposition process was accomplished by both the proposed and post-reconstruction dual-energy CT techniques. Linear regression and normalized root-mean-square error (NRMSE) analyses were performed to evaluate the quantitative accuracy of decomposition images. The calibration accuracy of the proposed dual-energy CT technique was higher than that of the post-reconstruction dual-energy CT technique, with fitted slopes of 0.97–1.01 and NRMSEs of 0.20–4.50% for all basis materials. In the three-material phantom study, the proposed dual-energy CT technique decreased the NRMSEs of measured volume fractions by factors of 0.17–0.28 compared to the post-reconstruction dual-energy CT technique. It was concluded that the

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

Energy Technology Data Exchange (ETDEWEB)

Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

OPTIMIZATION OF AGGREGATION AND SEQUENTIAL-PARALLEL EXECUTION MODES OF INTERSECTING OPERATION SETS

Directory of Open Access Journals (Sweden)

G. М. Levin

2016-01-01

Full Text Available A mathematical model and a method for the problem of optimization of aggregation and of sequential- parallel execution modes of intersecting operation sets are proposed. The proposed method is based on the two-level decomposition scheme. At the top level the variant of aggregation for groups of operations is selected, and at the lower level the execution modes of operations are optimized for a fixed version of aggregation.

A New Efficient Algorithm for the 2D WLP-FDTD Method Based on Domain Decomposition Technique

Directory of Open Access Journals (Sweden)

Bo-Ao Xu

2016-01-01

Full Text Available This letter introduces a new efficient algorithm for the two-dimensional weighted Laguerre polynomials finite difference time-domain (WLP-FDTD method based on domain decomposition scheme. By using the domain decomposition finite difference technique, the whole computational domain is decomposed into several subdomains. The conventional WLP-FDTD and the efficient WLP-FDTD methods are, respectively, used to eliminate the splitting error and speed up the calculation in different subdomains. A joint calculation scheme is presented to reduce the amount of calculation. Through our work, the iteration is not essential to obtain the accurate results. Numerical example indicates that the efficiency and accuracy are improved compared with the efficient WLP-FDTD method.

Ambiguity attacks on robust blind image watermarking scheme based on redundant discrete wavelet transform and singular value decomposition

Directory of Open Access Journals (Sweden)

Khaled Loukhaoukha

2017-12-01

Full Text Available Among emergent applications of digital watermarking are copyright protection and proof of ownership. Recently, Makbol and Khoo (2013 have proposed for these applications a new robust blind image watermarking scheme based on the redundant discrete wavelet transform (RDWT and the singular value decomposition (SVD. In this paper, we present two ambiguity attacks on this algorithm that have shown that this algorithm fails when used to provide robustness applications like owner identification, proof of ownership, and transaction tracking. Keywords: Ambiguity attack, Image watermarking, Singular value decomposition, Redundant discrete wavelet transform

Cooperative Coevolution with Formula-Based Variable Grouping for Large-Scale Global Optimization.

Science.gov (United States)

Wang, Yuping; Liu, Haiyan; Wei, Fei; Zong, Tingting; Li, Xiaodong

2017-08-09

For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered an effective strategy to decompose the problem into smaller subproblems, each of which can then be solved individually. Among these decomposition methods, variable grouping is shown to be promising in recent years. Existing variable grouping methods usually assume the problem to be black-box (i.e., assuming that an analytical model of the objective function is unknown), and they attempt to learn appropriate variable grouping that would allow for a better decomposition of the problem. In such cases, these variable grouping methods do not make a direct use of the formula of the objective function. However, it can be argued that many real-world problems are white-box problems, that is, the formulas of objective functions are often known a priori. These formulas of the objective functions provide rich information which can then be used to design an effective variable group method. In this article, a formula-based grouping strategy (FBG) for white-box problems is first proposed. It groups variables directly via the formula of an objective function which usually consists of a finite number of operations (i.e., four arithmetic operations "[Formula: see text]", "[Formula: see text]", "[Formula: see text]", "[Formula: see text]" and composite operations of basic elementary functions). In FBG, the operations are classified into two classes: one resulting in nonseparable variables, and the other resulting in separable variables. In FBG, variables can be automatically grouped into a suitable number of non-interacting subcomponents, with variables in each subcomponent being interdependent. FBG can easily be applied to any white-box problem and can be integrated into a cooperative coevolution framework. Based on FBG, a novel cooperative coevolution algorithm with formula-based variable grouping (so-called CCF) is proposed in this article for decomposing a large-scale white-box problem

G-Doob-Meyer Decomposition and Its Applications in Bid-Ask Pricing for Derivatives under Knightian Uncertainty

Directory of Open Access Journals (Sweden)

Wei Chen

2015-01-01

Full Text Available The target of this paper is to establish the bid-ask pricing framework for the American contingent claims against risky assets with G-asset price systems on the financial market under Knightian uncertainty. First, we prove G-Dooby-Meyer decomposition for G-supermartingale. Furthermore, we consider bid-ask pricing American contingent claims under Knightian uncertainty, by using G-Dooby-Meyer decomposition; we construct dynamic superhedge strategies for the optimal stopping problem and prove that the value functions of the optimal stopping problems are the bid and ask prices of the American contingent claims under Knightian uncertainty. Finally, we consider a free boundary problem, prove the strong solution existence of the free boundary problem, and derive that the value function of the optimal stopping problem is equivalent to the strong solution to the free boundary problem.

Automatic classification of visual evoked potentials based on wavelet decomposition

Science.gov (United States)

Stasiakiewicz, Paweł; Dobrowolski, Andrzej P.; Tomczykiewicz, Kazimierz

2017-04-01

Diagnosis of part of the visual system, that is responsible for conducting compound action potential, is generally based on visual evoked potentials generated as a result of stimulation of the eye by external light source. The condition of patient's visual path is assessed by set of parameters that describe the time domain characteristic extremes called waves. The decision process is compound therefore diagnosis significantly depends on experience of a doctor. The authors developed a procedure - based on wavelet decomposition and linear discriminant analysis - that ensures automatic classification of visual evoked potentials. The algorithm enables to assign individual case to normal or pathological class. The proposed classifier has a 96,4% sensitivity at 10,4% probability of false alarm in a group of 220 cases and area under curve ROC equals to 0,96 which, from the medical point of view, is a very good result.

Lifecycle-Based Swarm Optimization Method for Numerical Optimization

Directory of Open Access Journals (Sweden)

Hai Shen

2014-01-01

Full Text Available Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO. Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

On the Use of Ashenhurst Decomposition Chart as an Alternative to ...

African Journals Online (AJOL)

... the Ashenhurst decomposition chart is shown to be a mapping technique which solves this selection problem and enables the design of logic circuits with desirable attributes using multiplexers. The Ashenhurst decomposition chart also serves as a bridging technique between the map based and algorithmic based digital ...

Crop residue decomposition in Minnesota biochar-amended plots

Science.gov (United States)

Weyers, S. L.; Spokas, K. A.

2014-06-01

Impacts of biochar application at laboratory scales are routinely studied, but impacts of biochar application on decomposition of crop residues at field scales have not been widely addressed. The priming or hindrance of crop residue decomposition could have a cascading impact on soil processes, particularly those influencing nutrient availability. Our objectives were to evaluate biochar effects on field decomposition of crop residue, using plots that were amended with biochars made from different plant-based feedstocks and pyrolysis platforms in the fall of 2008. Litterbags containing wheat straw material were buried in July of 2011 below the soil surface in a continuous-corn cropped field in plots that had received one of seven different biochar amendments or a uncharred wood-pellet amendment 2.5 yr prior to start of this study. Litterbags were collected over the course of 14 weeks. Microbial biomass was assessed in treatment plots the previous fall. Though first-order decomposition rate constants were positively correlated to microbial biomass, neither parameter was statistically affected by biochar or wood-pellet treatments. The findings indicated only a residual of potentially positive and negative initial impacts of biochars on residue decomposition, which fit in line with established feedstock and pyrolysis influences. Overall, these findings indicate that no significant alteration in the microbial dynamics of the soil decomposer communities occurred as a consequence of the application of plant-based biochars evaluated here.

A practical material decomposition method for x-ray dual spectral computed tomography.

Science.gov (United States)

Hu, Jingjing; Zhao, Xing

2016-03-17

X-ray dual spectral CT (DSCT) scans the measured object with two different x-ray spectra, and the acquired rawdata can be used to perform the material decomposition of the object. Direct calibration methods allow a faster material decomposition for DSCT and can be separated in two groups: image-based and rawdata-based. The image-based method is an approximative method, and beam hardening artifacts remain in the resulting material-selective images. The rawdata-based method generally obtains better image quality than the image-based method, but this method requires geometrically consistent rawdata. However, today's clinical dual energy CT scanners usually measure different rays for different energy spectra and acquire geometrically inconsistent rawdata sets, and thus cannot meet the requirement. This paper proposes a practical material decomposition method to perform rawdata-based material decomposition in the case of inconsistent measurement. This method first yields the desired consistent rawdata sets from the measured inconsistent rawdata sets, and then employs rawdata-based technique to perform material decomposition and reconstruct material-selective images. The proposed method was evaluated by use of simulated FORBILD thorax phantom rawdata and dental CT rawdata, and simulation results indicate that this method can produce highly quantitative DSCT images in the case of inconsistent DSCT measurements.

Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input–output model

International Nuclear Information System (INIS)

Zhu Qin; Peng Xizhe; Wu Kaiya

2012-01-01

Based on the input–output model and the comparable price input–output tables, the current paper investigates the indirect carbon emissions from residential consumption in China in 1992–2005, and examines the impacts on the emissions using the structural decomposition method. The results demonstrate that the rise of the residential consumption level played a dominant role in the growth of residential indirect emissions. The persistent decline of the carbon emission intensity of industrial sectors presented a significant negative effect on the emissions. The change in the intermediate demand of industrial sectors resulted in an overall positive effect, except in the initial years. The increase in population prompted the indirect emissions to a certain extent; however, population size is no longer the main reason for the growth of the emissions. The change in the consumption structure showed a weak positive effect, demonstrating the importance for China to control and slow down the increase in the emissions while in the process of optimizing the residential consumption structure. The results imply that the means for restructuring the economy and improving efficiency, rather than for lowering the consumption scale, should be adopted by China to achieve the targets of energy conservation and emission reduction. - Highlights: ► We build the input–output model of indirect carbon emissions from residential consumption. ► We calculate the indirect emissions using the comparable price input–output tables. ► We examine the impacts on the indirect emissions using the structural decomposition method. ► The change in the consumption structure showed a weak positive effect on the emissions. ► China's population size is no longer the main reason for the growth of the emissions.

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha

2013-11-25

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Systems-based decomposition schemes for the approximate solution of multi-term fractional differential equations

Science.gov (United States)

Ford, Neville J.; Connolly, Joseph A.

2009-07-01

We give a comparison of the efficiency of three alternative decomposition schemes for the approximate solution of multi-term fractional differential equations using the Caputo form of the fractional derivative. The schemes we compare are based on conversion of the original problem into a system of equations. We review alternative approaches and consider how the most appropriate numerical scheme may be chosen to solve a particular equation.

Agent-Based Optimization

CERN Document Server

Jędrzejowicz, Piotr; Kacprzyk, Janusz

2013-01-01

This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve  difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.

Regional income inequality model based on theil index decomposition and weighted variance coeficient

Science.gov (United States)

Sitepu, H. R.; Darnius, O.; Tambunan, W. N.

2018-03-01

Regional income inequality is an important issue in the study on economic development of a certain region. Rapid economic development may not in accordance with people’s per capita income. The method of measuring the regional income inequality has been suggested by many experts. This research used Theil index and weighted variance coefficient in order to measure the regional income inequality. Regional income decomposition which becomes the productivity of work force and their participation in regional income inequality, based on Theil index, can be presented in linear relation. When the economic assumption in j sector, sectoral income value, and the rate of work force are used, the work force productivity imbalance can be decomposed to become the component in sectors and in intra-sectors. Next, weighted variation coefficient is defined in the revenue and productivity of the work force. From the quadrate of the weighted variation coefficient result, it was found that decomposition of regional revenue imbalance could be analyzed by finding out how far each component contribute to regional imbalance which, in this research, was analyzed in nine sectors of economic business.

Performance-based shape optimization of continuum structures

International Nuclear Information System (INIS)

Liang Qingquan

2010-01-01

This paper presents a performance-based optimization (PBO) method for optimal shape design of continuum structures with stiffness constraints. Performance-based design concepts are incorporated in the shape optimization theory to achieve optimal designs. In the PBO method, the traditional shape optimization problem of minimizing the weight of a continuum structure with displacement or mean compliance constraints is transformed to the problem of maximizing the performance of the structure. The optimal shape of a continuum structure is obtained by gradually eliminating inefficient finite elements from the structure until its performance is maximized. Performance indices are employed to monitor the performance of optimized shapes in an optimization process. Performance-based optimality criteria are incorporated in the PBO method to identify the optimum from the optimization process. The PBO method is used to produce optimal shapes of plane stress continuum structures and plates in bending. Benchmark numerical results are provided to demonstrate the effectiveness of the PBO method for generating the maximum stiffness shape design of continuum structures. It is shown that the PBO method developed overcomes the limitations of traditional shape optimization methods in optimal design of continuum structures. Performance-based optimality criteria presented can be incorporated in any shape and topology optimization methods to obtain optimal designs of continuum structures.

QR-decomposition based SENSE reconstruction using parallel architecture.

Science.gov (United States)

Ullah, Irfan; Nisar, Habab; Raza, Haseeb; Qasim, Malik; Inam, Omair; Omer, Hammad

2018-04-01

Magnetic Resonance Imaging (MRI) is a powerful medical imaging technique that provides essential clinical information about the human body. One major limitation of MRI is its long scan time. Implementation of advance MRI algorithms on a parallel architecture (to exploit inherent parallelism) has a great potential to reduce the scan time. Sensitivity Encoding (SENSE) is a Parallel Magnetic Resonance Imaging (pMRI) algorithm that utilizes receiver coil sensitivities to reconstruct MR images from the acquired under-sampled k-space data. At the heart of SENSE lies inversion of a rectangular encoding matrix. This work presents a novel implementation of GPU based SENSE algorithm, which employs QR decomposition for the inversion of the rectangular encoding matrix. For a fair comparison, the performance of the proposed GPU based SENSE reconstruction is evaluated against single and multicore CPU using openMP. Several experiments against various acceleration factors (AFs) are performed using multichannel (8, 12 and 30) phantom and in-vivo human head and cardiac datasets. Experimental results show that GPU significantly reduces the computation time of SENSE reconstruction as compared to multi-core CPU (approximately 12x speedup) and single-core CPU (approximately 53x speedup) without any degradation in the quality of the reconstructed images. Copyright © 2018 Elsevier Ltd. All rights reserved.

Practical mathematical optimization basic optimization theory and gradient-based algorithms

CERN Document Server

Snyman, Jan A

2018-01-01

This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and dir...

Incentivized optimal advert assignment via utility decomposition

NARCIS (Netherlands)

Kelly, F.; Key, P.; Walton, N.

2014-01-01

We consider a large-scale Ad-auction where adverts are assigned over a potentially infinite number of searches. We capture the intrinsic asymmetries in information between advertisers, the advert platform and the space of searches: advertisers know and can optimize the average performance of their

Coarse-to-fine markerless gait analysis based on PCA and Gauss-Laguerre decomposition

Science.gov (United States)

Goffredo, Michela; Schmid, Maurizio; Conforto, Silvia; Carli, Marco; Neri, Alessandro; D'Alessio, Tommaso

2005-04-01

Human movement analysis is generally performed through the utilization of marker-based systems, which allow reconstructing, with high levels of accuracy, the trajectories of markers allocated on specific points of the human body. Marker based systems, however, show some drawbacks that can be overcome by the use of video systems applying markerless techniques. In this paper, a specifically designed computer vision technique for the detection and tracking of relevant body points is presented. It is based on the Gauss-Laguerre Decomposition, and a Principal Component Analysis Technique (PCA) is used to circumscribe the region of interest. Results obtained on both synthetic and experimental tests provide significant reduction of the computational costs, with no significant reduction of the tracking accuracy.

Evaluating litter decomposition and soil organic matter dynamics in earth system models: contrasting analysis of long-term litter decomposition and steady-state soil carbon

Science.gov (United States)

Bonan, G. B.; Wieder, W. R.

2012-12-01

Decomposition is a large term in the global carbon budget, but models of the earth system that simulate carbon cycle-climate feedbacks are largely untested with respect to litter decomposition. Here, we demonstrate a protocol to document model performance with respect to both long-term (10 year) litter decomposition and steady-state soil carbon stocks. First, we test the soil organic matter parameterization of the Community Land Model version 4 (CLM4), the terrestrial component of the Community Earth System Model, with data from the Long-term Intersite Decomposition Experiment Team (LIDET). The LIDET dataset is a 10-year study of litter decomposition at multiple sites across North America and Central America. We show results for 10-year litter decomposition simulations compared with LIDET for 9 litter types and 20 sites in tundra, grassland, and boreal, conifer, deciduous, and tropical forest biomes. We show additional simulations with DAYCENT, a version of the CENTURY model, to ask how well an established ecosystem model matches the observations. The results reveal large discrepancy between the laboratory microcosm studies used to parameterize the CLM4 litter decomposition and the LIDET field study. Simulated carbon loss is more rapid than the observations across all sites, despite using the LIDET-provided climatic decomposition index to constrain temperature and moisture effects on decomposition. Nitrogen immobilization is similarly biased high. Closer agreement with the observations requires much lower decomposition rates, obtained with the assumption that nitrogen severely limits decomposition. DAYCENT better replicates the observations, for both carbon mass remaining and nitrogen, without requirement for nitrogen limitation of decomposition. Second, we compare global observationally-based datasets of soil carbon with simulated steady-state soil carbon stocks for both models. The models simulations were forced with observationally-based estimates of annual

Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition

Science.gov (United States)

Ohmichi, Yuya

2017-07-01

In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal decomposition (POD) to DMD and mode selection algorithms. By performing the preconditioning step, the DMD and mode selection can be performed with low memory consumption and therefore can be applied to large datasets. Additionally, we propose a simple mode selection algorithm based on a greedy method. The proposed framework is applied to the analysis of three-dimensional flow around a circular cylinder.

DECOMPOSITION STUDY OF CALCIUM CARBONATE IN COCKLE SHELL

Directory of Open Access Journals (Sweden)

MUSTAKIMAH MOHAMED

2012-02-01

Full Text Available Calcium oxide (CaO is recognized as an efficient carbon dioxide (CO2 adsorbent and separation of CO2 from gas stream using CaO based adsorbent is widely applied in gas purification process especially at high temperature reaction. CaO is normally been produced via thermal decomposition of calcium carbonate (CaCO3 sources such as limestone which is obtained through mining and quarrying limestone hill. Yet, this study able to exploit the vast availability of waste resources in Malaysia which is cockle shell, as the potential biomass resources for CaCO3 and CaO. In addition, effect of particle size towards decomposition process is put under study using four particle sizes which are 0.125-0.25 mm, 0.25-0.5 mm, 1-2 mm, and 2-4 mm. Decomposition reactivity is conducted using Thermal Gravimetric Analyzer (TGA at heating rate of 20°C/minutes in inert (Nitrogen atmosphere. Chemical property analysis using x-ray fluorescence (XRF, shows cockle shell is made up of 97% Calcium (Ca element and CaO is produced after decomposition is conducted, as been analyzed by x-ray diffusivity (XRD analyzer. Besides, smallest particle size exhibits the highest decomposition rate and the process was observed to follow first order kinetics. Activation energy, E, of the process was found to vary from 179.38 to 232.67 kJ/mol. From Arrhenius plot, E increased when the particle size is larger. To conclude, cockle shell is a promising source for CaO and based on four different particles sizes used, sample at 0.125-0.25 mm offers the highest decomposition rate.

Risk Based Optimal Fatigue Testing

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Faber, M.H.; Kroon, I.B.

1992-01-01

Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value...

Some peculiarities of zirconium tungstate synthesis by thermal decomposition of hydrothermal precursors

International Nuclear Information System (INIS)

Gubanov, Alexander I.; Dedova, Elena S.; Plyusnin, Pavel E.; Filatov, Eugeny Y.; Kardash, Tatyana Y.; Korenev, Sergey V.; Kulkov, Sergey N.

2014-01-01

Highlights: • Synthesis of ZrW 2 O 8 using hydrothermal method. • On hydrothermal synthesis optimal conc. of HCl in the reaction mixture is 2.3 M. • Thermal decomposition of ZrW 2 O 7 ((OH) 1.5 ,Cl 0.5 )·2H 2 O begins are 200 °S. • Amorphous intermediate crystallizes into cubic single-phase ZrW 2 O 8 above 550 °S. • ZrW 2 O 8 destructed at temperatures above 700 °S. - Abstract: This article discusses some peculiarities of the synthesis of ZrW 2 O 8 (1) using thermal decomposition of the precursor ZrW 2 O 7 ((OH) 1.5 ,Cl 0.5 )·2H 2 O (2) prepared by hydrothermal method. On hydrothermal synthesis of 2 the optimal concentration of hydrochloric acid in the reaction mixture is about 2.3 M. TG approach to determine the chemical composition of the precursor was suggested. It has been found that the precursor for the synthesis of zirconium tungstate has chemical formula 2. Thermal decomposition of the precursor 2 begins at 200 °S and affords an amorphous intermediate, which crystallizes as a cubic phase 1 above 550 °S with an exoeffect. The temperature of the beginning of the transition from amorphous to the crystalline state is 350 ± 25 °S

Decompositions of manifolds

CERN Document Server

Daverman, Robert J

2007-01-01

Decomposition theory studies decompositions, or partitions, of manifolds into simple pieces, usually cell-like sets. Since its inception in 1929, the subject has become an important tool in geometric topology. The main goal of the book is to help students interested in geometric topology to bridge the gap between entry-level graduate courses and research at the frontier as well as to demonstrate interrelations of decomposition theory with other parts of geometric topology. With numerous exercises and problems, many of them quite challenging, the book continues to be strongly recommended to eve

Primary decomposition of zero-dimensional ideals over finite fields

Science.gov (United States)

Gao, Shuhong; Wan, Daqing; Wang, Mingsheng

2009-03-01

A new algorithm is presented for computing primary decomposition of zero-dimensional ideals over finite fields. Like Berlekamp's algorithm for univariate polynomials, the new method is based on the invariant subspace of the Frobenius map acting on the quotient algebra. The dimension of the invariant subspace equals the number of primary components, and a basis of the invariant subspace yields a complete decomposition. Unlike previous approaches for decomposing multivariate polynomial systems, the new method does not need primality testing nor any generic projection, instead it reduces the general decomposition problem directly to root finding of univariate polynomials over the ground field. Also, it is shown how Groebner basis structure can be used to get partial primary decomposition without any root finding.

Thermal decomposition of pyrite

International Nuclear Information System (INIS)

Music, S.; Ristic, M.; Popovic, S.

1992-01-01

Thermal decomposition of natural pyrite (cubic, FeS 2 ) has been investigated using X-ray diffraction and 57 Fe Moessbauer spectroscopy. X-ray diffraction analysis of pyrite ore from different sources showed the presence of associated minerals, such as quartz, szomolnokite, stilbite or stellerite, micas and hematite. Hematite, maghemite and pyrrhotite were detected as thermal decomposition products of natural pyrite. The phase composition of the thermal decomposition products depends on the terature, time of heating and starting size of pyrite chrystals. Hematite is the end product of the thermal decomposition of natural pyrite. (author) 24 refs.; 6 figs.; 2 tabs

Logic-based methods for optimization combining optimization and constraint satisfaction

CERN Document Server

Hooker, John

2011-01-01

A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible

Danburite decomposition by sulfuric acid

International Nuclear Information System (INIS)

Mirsaidov, U.; Mamatov, E.D.; Ashurov, N.A.

2011-01-01

Present article is devoted to decomposition of danburite of Ak-Arkhar Deposit of Tajikistan by sulfuric acid. The process of decomposition of danburite concentrate by sulfuric acid was studied. The chemical nature of decomposition process of boron containing ore was determined. The influence of temperature on the rate of extraction of boron and iron oxides was defined. The dependence of decomposition of boron and iron oxides on process duration, dosage of H 2 SO 4 , acid concentration and size of danburite particles was determined. The kinetics of danburite decomposition by sulfuric acid was studied as well. The apparent activation energy of the process of danburite decomposition by sulfuric acid was calculated. The flowsheet of danburite processing by sulfuric acid was elaborated.

A parallel domain decomposition-based implicit method for the Cahn-Hilliard-Cook phase-field equation in 3D

KAUST Repository

Zheng, Xiang

2015-03-01

We present a numerical algorithm for simulating the spinodal decomposition described by the three dimensional Cahn-Hilliard-Cook (CHC) equation, which is a fourth-order stochastic partial differential equation with a noise term. The equation is discretized in space and time based on a fully implicit, cell-centered finite difference scheme, with an adaptive time-stepping strategy designed to accelerate the progress to equilibrium. At each time step, a parallel Newton-Krylov-Schwarz algorithm is used to solve the nonlinear system. We discuss various numerical and computational challenges associated with the method. The numerical scheme is validated by a comparison with an explicit scheme of high accuracy (and unreasonably high cost). We present steady state solutions of the CHC equation in two and three dimensions. The effect of the thermal fluctuation on the spinodal decomposition process is studied. We show that the existence of the thermal fluctuation accelerates the spinodal decomposition process and that the final steady morphology is sensitive to the stochastic noise. We also show the evolution of the energies and statistical moments. In terms of the parallel performance, it is found that the implicit domain decomposition approach scales well on supercomputers with a large number of processors. © 2015 Elsevier Inc.

A parallel domain decomposition-based implicit method for the Cahn-Hilliard-Cook phase-field equation in 3D

Science.gov (United States)

Zheng, Xiang; Yang, Chao; Cai, Xiao-Chuan; Keyes, David

2015-03-01

We present a numerical algorithm for simulating the spinodal decomposition described by the three dimensional Cahn-Hilliard-Cook (CHC) equation, which is a fourth-order stochastic partial differential equation with a noise term. The equation is discretized in space and time based on a fully implicit, cell-centered finite difference scheme, with an adaptive time-stepping strategy designed to accelerate the progress to equilibrium. At each time step, a parallel Newton-Krylov-Schwarz algorithm is used to solve the nonlinear system. We discuss various numerical and computational challenges associated with the method. The numerical scheme is validated by a comparison with an explicit scheme of high accuracy (and unreasonably high cost). We present steady state solutions of the CHC equation in two and three dimensions. The effect of the thermal fluctuation on the spinodal decomposition process is studied. We show that the existence of the thermal fluctuation accelerates the spinodal decomposition process and that the final steady morphology is sensitive to the stochastic noise. We also show the evolution of the energies and statistical moments. In terms of the parallel performance, it is found that the implicit domain decomposition approach scales well on supercomputers with a large number of processors.

A parallel domain decomposition-based implicit method for the Cahn–Hilliard–Cook phase-field equation in 3D

International Nuclear Information System (INIS)

Zheng, Xiang; Yang, Chao; Cai, Xiao-Chuan; Keyes, David

2015-01-01

We present a numerical algorithm for simulating the spinodal decomposition described by the three dimensional Cahn–Hilliard–Cook (CHC) equation, which is a fourth-order stochastic partial differential equation with a noise term. The equation is discretized in space and time based on a fully implicit, cell-centered finite difference scheme, with an adaptive time-stepping strategy designed to accelerate the progress to equilibrium. At each time step, a parallel Newton–Krylov–Schwarz algorithm is used to solve the nonlinear system. We discuss various numerical and computational challenges associated with the method. The numerical scheme is validated by a comparison with an explicit scheme of high accuracy (and unreasonably high cost). We present steady state solutions of the CHC equation in two and three dimensions. The effect of the thermal fluctuation on the spinodal decomposition process is studied. We show that the existence of the thermal fluctuation accelerates the spinodal decomposition process and that the final steady morphology is sensitive to the stochastic noise. We also show the evolution of the energies and statistical moments. In terms of the parallel performance, it is found that the implicit domain decomposition approach scales well on supercomputers with a large number of processors

Vector domain decomposition schemes for parabolic equations

Science.gov (United States)

Vabishchevich, P. N.

2017-09-01

A new class of domain decomposition schemes for finding approximate solutions of timedependent problems for partial differential equations is proposed and studied. A boundary value problem for a second-order parabolic equation is used as a model problem. The general approach to the construction of domain decomposition schemes is based on partition of unity. Specifically, a vector problem is set up for solving problems in individual subdomains. Stability conditions for vector regionally additive schemes of first- and second-order accuracy are obtained.

Optimization of Decision-Making for Spatial Sampling in the North China Plain, Based on Remote-Sensing a Priori Knowledge

Science.gov (United States)

Feng, J.; Bai, L.; Liu, S.; Su, X.; Hu, H.

2012-07-01

In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.

A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik

2014-01-01

This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP...

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

Directory of Open Access Journals (Sweden)

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.

Optimization of modal filters based on arrays of piezoelectric sensors

International Nuclear Information System (INIS)

Pagani, Carlos C Jr; Trindade, Marcelo A

2009-01-01

Modal filters may be obtained by a properly designed weighted sum of the output signals of an array of sensors distributed on the host structure. Although several research groups have been interested in techniques for designing and implementing modal filters based on a given array of sensors, the effect of the array topology on the effectiveness of the modal filter has received much less attention. In particular, it is known that some parameters, such as size, shape and location of a sensor, are very important in determining the observability of a vibration mode. Hence, this paper presents a methodology for the topological optimization of an array of sensors in order to maximize the effectiveness of a set of selected modal filters. This is done using a genetic algorithm optimization technique for the selection of 12 piezoceramic sensors from an array of 36 piezoceramic sensors regularly distributed on an aluminum plate, which maximize the filtering performance, over a given frequency range, of a set of modal filters, each one aiming to isolate one of the first vibration modes. The vectors of the weighting coefficients for each modal filter are evaluated using QR decomposition of the complex frequency response function matrix. Results show that the array topology is not very important for lower frequencies but it greatly affects the filter effectiveness for higher frequencies. Therefore, it is possible to improve the effectiveness and frequency range of a set of modal filters by optimizing the topology of an array of sensors. Indeed, using 12 properly located piezoceramic sensors bonded on an aluminum plate it is shown that the frequency range of a set of modal filters may be enlarged by 25–50%

Formation and decomposition of ammoniated ammonium ions

International Nuclear Information System (INIS)

Ikezoe, Yasumasa; Suzuki, Kazuya; Nakashima, Mikio; Yokoyama, Atsushi; Shiraishi, Hirotsugu; Ohno, Shin-ichi

1998-09-01

Structures, frequencies, and chemical reactions of ammoniated ammonium ions (NH 4 + .nNH 3 ) were investigated theoretically by ab initio molecular orbital calculations and experimentally by observing their formation and decomposition in a corona discharge-jet expansion process. The ab initio calculations were carried out using a Gaussian 94 program, which gave optimized structures, binding energies and harmonic vibrational frequencies of NH 4 + .nNH 3 . Effects of discharge current, the reactant gas and the diameter of the gas expanding pinhole were examined on the size n distribution of NH 4 + .nNH 3 . The results indicated that the cluster ion, in the jet expansion process, grew in size mostly equal to or less than one unit under experimental conditions employed. Effects of discharge current, pinhole diameter, flight time in vacuum and cluster size were examined on the decomposition rate of cluster ions formed. In our experimental conditions, the internal energies of cluster ions were mainly determined through exo- and/or endo-thermic reactions involved in the cluster formation process. (author)

Growth and decomposition of Lithium and Lithium hydride on Nickel

DEFF Research Database (Denmark)

Engbæk, Jakob; Nielsen, Gunver; Nielsen, Jane Hvolbæk

2006-01-01

In this paper we have investigated the deposition, structure and decomposition of lithium and lithium-hydride films on a nickel substrate. Using surface sensitive techniques it was possible to quantify the deposited Li amount, and to optimize the deposition procedure for synthesizing lithium......-hydride films. By only making thin films of LiH it is possible to study the stability of these hydride layers and compare it directly with the stability of pure Li without having any transport phenomena or adsorbed oxygen to obscure the results. The desorption of metallic lithium takes place at a lower...... temperature than the decomposition of the lithium-hydride, confirming the high stability and sintering problems of lithium-hydride making the storage potential a challenge. (c) 2006 Elsevier B.V. All rights reserved....

Azimuthal decomposition of optical modes

CSIR Research Space (South Africa)

Dudley, Angela L

2012-07-01

Full Text Available This presentation analyses the azimuthal decomposition of optical modes. Decomposition of azimuthal modes need two steps, namely generation and decomposition. An azimuthally-varying phase (bounded by a ring-slit) placed in the spatial frequency...

Domain decomposition method for solving the neutron diffusion equation

International Nuclear Information System (INIS)

Coulomb, F.

1989-03-01

The aim of this work is to study methods for solving the neutron diffusion equation; we are interested in methods based on a classical finite element discretization and well suited for use on parallel computers. Domain decomposition methods seem to answer this preoccupation. This study deals with a decomposition of the domain. A theoretical study is carried out for Lagrange finite elements and some examples are given; in the case of mixed dual finite elements, the study is based on examples [fr

Full waveform inversion based on the optimized gradient and its spectral implementation

KAUST Repository

Wu, Zedong

2014-01-01

Full waveform inversion (FWI) despite it\\'s potential suffers from the ability to converge to the desired solution due to the high nonlinearity of the objective function at conventional seismic frequencies. Even if frequencies necessary for the convergence are available, the high number of iterations required to approach a solution renders FWI as very expensive (especially in 3D). A spectral implementation in which the wavefields are extrapolated and gradients are calculated in the wavenumber domain allows for a cleaner more efficient implementation (no finite difference dispersion errors). In addition, we use not only an up and down going wavefield decomposition of the gradient to access the smooth background update, but also a right and left propagation decomposition to allow us to do that for large dips. To insure that the extracted smooth component of the gradient has the right decent direction, we solve an optimization problem to search for the smoothest component that provides a negative (decent) gradient. Application to the Marmousi model shows that this approach works well with linear increasing initial velocity model and data with frequencies above 2Hz.

Decomposition of continuum {gamma}-ray spectra using synthesized response matrix

Energy Technology Data Exchange (ETDEWEB)

Jandel, M.; Morhac, M.; Kliman, J.; Krupa, L.; Matousek, V. E-mail: vladislav.matousek@savba.sk; Hamilton, J.H.; Ramayya, A.V

2004-01-01

The efficient methods of decomposition of {gamma}-ray spectra, based on the Gold algorithm, are presented. They use a response matrix of Gammasphere, which was obtained by synthesis of simulated and interpolated response functions using a new developed interpolation algorithm. The decomposition method has been applied to the measured spectra of {sup 152}Eu and {sup 56}Co. The results show a very effective removal of the background counts and their concentration into the corresponding photopeaks. The peak-to-total ratio in the spectra achieved after applying the decomposition method is in the interval 0.95-0.99. In addition, a new advanced algorithm of the 'boosted' decomposition has been proposed. In the spectra obtained after applying the boosted decomposition to the measured spectra, very narrow photopeaks are observed with the counts concentrated to several channels.

Generalized Fisher index or Siegel-Shapley decomposition?

International Nuclear Information System (INIS)

De Boer, Paul

2009-01-01

It is generally believed that index decomposition analysis (IDA) and input-output structural decomposition analysis (SDA) [Rose, A., Casler, S., Input-output structural decomposition analysis: a critical appraisal, Economic Systems Research 1996; 8; 33-62; Dietzenbacher, E., Los, B., Structural decomposition techniques: sense and sensitivity. Economic Systems Research 1998;10; 307-323] are different approaches in energy studies; see for instance Ang et al. [Ang, B.W., Liu, F.L., Chung, H.S., A generalized Fisher index approach to energy decomposition analysis. Energy Economics 2004; 26; 757-763]. In this paper it is shown that the generalized Fisher approach, introduced in IDA by Ang et al. [Ang, B.W., Liu, F.L., Chung, H.S., A generalized Fisher index approach to energy decomposition analysis. Energy Economics 2004; 26; 757-763] for the decomposition of an aggregate change in a variable in r = 2, 3 or 4 factors is equivalent to SDA. They base their formulae on the very complicated generic formula that Shapley [Shapley, L., A value for n-person games. In: Kuhn H.W., Tucker A.W. (Eds), Contributions to the theory of games, vol. 2. Princeton University: Princeton; 1953. p. 307-317] derived for his value of n-person games, and mention that Siegel [Siegel, I.H., The generalized 'ideal' index-number formula. Journal of the American Statistical Association 1945; 40; 520-523] gave their formulae using a different route. In this paper tables are given from which the formulae of the generalized Fisher approach can easily be derived for the cases of r = 2, 3 or 4 factors. It is shown that these tables can easily be extended to cover the cases of r = 5 and r = 6 factors. (author)

Inverse scale space decomposition

DEFF Research Database (Denmark)

Schmidt, Marie Foged; Benning, Martin; Schönlieb, Carola-Bibiane

2018-01-01

We investigate the inverse scale space flow as a decomposition method for decomposing data into generalised singular vectors. We show that the inverse scale space flow, based on convex and even and positively one-homogeneous regularisation functionals, can decompose data represented...... by the application of a forward operator to a linear combination of generalised singular vectors into its individual singular vectors. We verify that for this decomposition to hold true, two additional conditions on the singular vectors are sufficient: orthogonality in the data space and inclusion of partial sums...... of the subgradients of the singular vectors in the subdifferential of the regularisation functional at zero. We also address the converse question of when the inverse scale space flow returns a generalised singular vector given that the initial data is arbitrary (and therefore not necessarily in the range...

Thermal decomposition of lutetium propionate

DEFF Research Database (Denmark)

Grivel, Jean-Claude

2010-01-01

The thermal decomposition of lutetium(III) propionate monohydrate (Lu(C2H5CO2)3·H2O) in argon was studied by means of thermogravimetry, differential thermal analysis, IR-spectroscopy and X-ray diffraction. Dehydration takes place around 90 °C. It is followed by the decomposition of the anhydrous...... °C. Full conversion to Lu2O3 is achieved at about 1000 °C. Whereas the temperatures and solid reaction products of the first two decomposition steps are similar to those previously reported for the thermal decomposition of lanthanum(III) propionate monohydrate, the final decomposition...... of the oxycarbonate to the rare-earth oxide proceeds in a different way, which is here reminiscent of the thermal decomposition path of Lu(C3H5O2)·2CO(NH2)2·2H2O...

The Potential Role of Cache Mechanism for Complicated Design Optimization

International Nuclear Information System (INIS)

Noriyasu, Hirokawa; Fujita, Kikuo

2002-01-01

This paper discusses the potential role of cache mechanism for complicated design optimization While design optimization is an application of mathematical programming techniques to engineering design problems over numerical computation, its progress has been coevolutionary. The trend in such progress indicates that more complicated applications become the next target of design optimization beyond growth of computational resources. As the progress in the past two decades had required response surface techniques, decomposition techniques, etc., any new framework must be introduced for the future of design optimization methods. This paper proposes a possibility of what we call cache mechanism for mediating the coming challenge and briefly demonstrates some promises in the idea of Voronoi diagram based cumulative approximation as an example of its implementation, development of strict robust design, extension of design optimization for product variety

Alloyed Ni-Fe nanoparticles as catalysts for NH₃ decomposition

DEFF Research Database (Denmark)

Simonsen, Søren Bredmose; Chakraborty, Debasish; Chorkendorff, Ib

2012-01-01

A rational design approach was used to develop an alloyed Ni-Fe/Al2O3 catalyst for decomposition of ammonia. The dependence of the catalytic activity is tested as a function of the Ni-to-Fe ratio, the type of Ni-Fe alloy phase, the metal loading and the type of oxide support. In the tests with high...... temperatures and a low NH3-to-H2 ratio, the catalytic activity of the best Ni-Fe/Al2O3 catalyst was found to be comparable or even better to that of a more expensive Ru-based catalyst. Small Ni-Fe nanoparticle sizes are crucial for an optimal overall NH3 conversion because of a structural effect favoring...

Energetic contaminants inhibit plant litter decomposition in soil.

Science.gov (United States)

Kuperman, Roman G; Checkai, Ronald T; Simini, Michael; Sunahara, Geoffrey I; Hawari, Jalal

2018-05-30

Individual effects of nitrogen-based energetic materials (EMs) 2,4-dinitrotoluene (2,4-DNT), 2-amino-4,6-dinitrotoluene (2-ADNT), 4-amino-2,6-dinitrotoluene (4-ADNT), nitroglycerin (NG), and 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) on litter decomposition, an essential biologically-mediated soil process, were assessed using Orchard grass (Dactylis glomerata) straw in Sassafras sandy loam (SSL) soil, which has physicochemical characteristics that support "very high" qualitative relative bioavailability for organic chemicals. Batches of SSL soil were separately amended with individual EMs or acetone carrier control. To quantify the decomposition rates, one straw cluster was harvested from a set of randomly selected replicate containers from within each treatment, after 1, 2, 3, 4, 6, and 8 months of exposure. Results showed that soil amended with 2,4-DNT or NG inhibited litter decomposition rates based on the median effective concentration (EC50) values of 1122 mg/kg and 860 mg/kg, respectively. Exposure to 2-ADNT, 4-ADNT or CL-20 amended soil did not significantly affect litter decomposition in SSL soil at ≥ 10,000 mg/kg. These ecotoxicological data will be helpful in identifying concentrations of EMs in soil that present an acceptable ecological risk for biologically-mediated soil processes. Published by Elsevier Inc.

Variance decomposition in stochastic simulators.

Science.gov (United States)

Le Maître, O P; Knio, O M; Moraes, A

2015-06-28

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

Science.gov (United States)

Le Maître, O. P.; Knio, O. M.; Moraes, A.

2015-06-01

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

Energy Technology Data Exchange (ETDEWEB)

Le Maître, O. P., E-mail: olm@limsi.fr [LIMSI-CNRS, UPR 3251, Orsay (France); Knio, O. M., E-mail: knio@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708 (United States); Moraes, A., E-mail: alvaro.moraesgutierrez@kaust.edu.sa [King Abdullah University of Science and Technology, Thuwal (Saudi Arabia)

2015-06-28

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

KAUST Repository

Le Maî tre, O. P.; Knio, O. M.; Moraes, Alvaro

2015-01-01

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Quantum Image Encryption Algorithm Based on Image Correlation Decomposition

Science.gov (United States)

Hua, Tianxiang; Chen, Jiamin; Pei, Dongju; Zhang, Wenquan; Zhou, Nanrun

2015-02-01

A novel quantum gray-level image encryption and decryption algorithm based on image correlation decomposition is proposed. The correlation among image pixels is established by utilizing the superposition and measurement principle of quantum states. And a whole quantum image is divided into a series of sub-images. These sub-images are stored into a complete binary tree array constructed previously and then randomly performed by one of the operations of quantum random-phase gate, quantum revolving gate and Hadamard transform. The encrypted image can be obtained by superimposing the resulting sub-images with the superposition principle of quantum states. For the encryption algorithm, the keys are the parameters of random phase gate, rotation angle, binary sequence and orthonormal basis states. The security and the computational complexity of the proposed algorithm are analyzed. The proposed encryption algorithm can resist brute force attack due to its very large key space and has lower computational complexity than its classical counterparts.

WEALTH-BASED INEQUALITY IN CHILD IMMUNIZATION IN INDIA: A DECOMPOSITION APPROACH.

Science.gov (United States)

Debnath, Avijit; Bhattacharjee, Nairita

2018-05-01

SummaryDespite years of health and medical advancement, children still suffer from infectious diseases that are vaccine preventable. India reacted in 1978 by launching the Expanded Programme on Immunization in an attempt to reduce the incidence of vaccine-preventable diseases (VPDs). Although the nation has made remarkable progress over the years, there is significant variation in immunization coverage across different socioeconomic strata. This study attempted to identify the determinants of wealth-based inequality in child immunization using a new, modified method. The present study was based on 11,001 eligible ever-married women aged 15-49 and their children aged 12-23 months. Data were from the third District Level Household and Facility Survey (DLHS-3) of India, 2007-08. Using an approximation of Erreyger's decomposition technique, the study identified unequal access to antenatal care as the main factor associated with inequality in immunization coverage in India.

Factors Affecting Regional Per-Capita Carbon Emissions in China Based on an LMDI Factor Decomposition Model

Science.gov (United States)

Dong, Feng; Long, Ruyin; Chen, Hong; Li, Xiaohui; Yang, Qingliang

2013-01-01

China is considered to be the main carbon producer in the world. The per-capita carbon emissions indicator is an important measure of the regional carbon emissions situation. This study used the LMDI factor decomposition model–panel co-integration test two-step method to analyze the factors that affect per-capita carbon emissions. The main results are as follows. (1) During 1997, Eastern China, Central China, and Western China ranked first, second, and third in the per-capita carbon emissions, while in 2009 the pecking order changed to Eastern China, Western China, and Central China. (2) According to the LMDI decomposition results, the key driver boosting the per-capita carbon emissions in the three economic regions of China between 1997 and 2009 was economic development, and the energy efficiency was much greater than the energy structure after considering their effect on restraining increased per-capita carbon emissions. (3) Based on the decomposition, the factors that affected per-capita carbon emissions in the panel co-integration test showed that Central China had the best energy structure elasticity in its regional per-capita carbon emissions. Thus, Central China was ranked first for energy efficiency elasticity, while Western China was ranked first for economic development elasticity. PMID:24353753

Controllable pneumatic generator based on the catalytic decomposition of hydrogen peroxide

International Nuclear Information System (INIS)

Kim, Kyung-Rok; Kim, Kyung-Soo; Kim, Soohyun

2014-01-01

This paper presents a novel compact and controllable pneumatic generator that uses hydrogen peroxide decomposition. A fuel micro-injector using a piston-pump mechanism is devised and tested to control the chemical decomposition rate. By controlling the injection rate, the feedback controller maintains the pressure of the gas reservoir at a desired pressure level. Thermodynamic analysis and experiments are performed to demonstrate the feasibility of the proposed pneumatic generator. Using a prototype of the pneumatic generator, it takes 6 s to reach 3.5 bars with a reservoir volume of 200 ml at the room temperature, which is sufficiently rapid and effective to maintain the repetitive lifting of a 1 kg mass

Controllable pneumatic generator based on the catalytic decomposition of hydrogen peroxide

Science.gov (United States)

Kim, Kyung-Rok; Kim, Kyung-Soo; Kim, Soohyun

2014-07-01

This paper presents a novel compact and controllable pneumatic generator that uses hydrogen peroxide decomposition. A fuel micro-injector using a piston-pump mechanism is devised and tested to control the chemical decomposition rate. By controlling the injection rate, the feedback controller maintains the pressure of the gas reservoir at a desired pressure level. Thermodynamic analysis and experiments are performed to demonstrate the feasibility of the proposed pneumatic generator. Using a prototype of the pneumatic generator, it takes 6 s to reach 3.5 bars with a reservoir volume of 200 ml at the room temperature, which is sufficiently rapid and effective to maintain the repetitive lifting of a 1 kg mass.

Three-pattern decomposition of global atmospheric circulation: part I—decomposition model and theorems

Science.gov (United States)

Hu, Shujuan; Chou, Jifan; Cheng, Jianbo

2018-04-01

In order to study the interactions between the atmospheric circulations at the middle-high and low latitudes from the global perspective, the authors proposed the mathematical definition of three-pattern circulations, i.e., horizontal, meridional and zonal circulations with which the actual atmospheric circulation is expanded. This novel decomposition method is proved to accurately describe the actual atmospheric circulation dynamics. The authors used the NCEP/NCAR reanalysis data to calculate the climate characteristics of those three-pattern circulations, and found that the decomposition model agreed with the observed results. Further dynamical analysis indicates that the decomposition model is more accurate to capture the major features of global three dimensional atmospheric motions, compared to the traditional definitions of Rossby wave, Hadley circulation and Walker circulation. The decomposition model for the first time realized the decomposition of global atmospheric circulation using three orthogonal circulations within the horizontal, meridional and zonal planes, offering new opportunities to study the large-scale interactions between the middle-high latitudes and low latitudes circulations.

Genetic algorithm based separation cascade optimization

International Nuclear Information System (INIS)

Mahendra, A.K.; Sanyal, A.; Gouthaman, G.; Bera, T.K.

2008-01-01

The conventional separation cascade design procedure does not give an optimum design because of squaring-off, variation of flow rates and separation factor of the element with respect to stage location. Multi-component isotope separation further complicates the design procedure. Cascade design can be stated as a constrained multi-objective optimization. Cascade's expectation from the separating element is multi-objective i.e. overall separation factor, cut, optimum feed and separative power. Decision maker may aspire for more comprehensive multi-objective goals where optimization of cascade is coupled with the exploration of separating element optimization vector space. In real life there are many issues which make it important to understand the decision maker's perception of cost-quality-speed trade-off and consistency of preferences. Genetic algorithm (GA) is one such evolutionary technique that can be used for cascade design optimization. This paper addresses various issues involved in the GA based multi-objective optimization of the separation cascade. Reference point based optimization methodology with GA based Pareto optimality concept for separation cascade was found pragmatic and promising. This method should be explored, tested, examined and further developed for binary as well as multi-component separations. (author)

Reliability-Based Optimization in Structural Engineering

DEFF Research Database (Denmark)

Enevoldsen, I.; Sørensen, John Dalsgaard

1994-01-01

In this paper reliability-based optimization problems in structural engineering are formulated on the basis of the classical decision theory. Several formulations are presented: Reliability-based optimal design of structural systems with component or systems reliability constraints, reliability...

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Directory of Open Access Journals (Sweden)

Jianchang Lu

2015-04-01

Full Text Available Based on the international community’s analysis of the present CO2 emissions situation, a Log Mean Divisia Index (LMDI decomposition model is proposed in this paper, aiming to reflect the decomposition of carbon productivity. The model is designed by analyzing the factors that affect carbon productivity. China’s contribution to carbon productivity is analyzed from the dimensions of influencing factors, regional structure and industrial structure. It comes to the conclusions that: (a economic output, the provincial carbon productivity and energy structure are the most influential factors, which are consistent with China’s current actual policy; (b the distribution patterns of economic output, carbon productivity and energy structure in different regions have nothing to do with the Chinese traditional sense of the regional economic development patterns; (c considering the regional protectionism, regional actual situation need to be considered at the same time; (d in the study of the industrial structure, the contribution value of industry is the most prominent factor for China’s carbon productivity, while the industrial restructuring has not been done well enough.

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Chunhui Zhao

2015-09-01

Full Text Available Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-MN schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.

Thermal Plasma decomposition of fluoriated greenhouse gases

Energy Technology Data Exchange (ETDEWEB)

Choi, Soo Seok; Watanabe, Takayuki [Tokyo Institute of Technology, Yokohama (Japan); Park, Dong Wha [Inha University, Incheon (Korea, Republic of)

2012-02-15

Fluorinated compounds mainly used in the semiconductor industry are potent greenhouse gases. Recently, thermal plasma gas scrubbers have been gradually replacing conventional burn-wet type gas scrubbers which are based on the combustion of fossil fuels because high conversion efficiency and control of byproduct generation are achievable in chemically reactive high temperature thermal plasma. Chemical equilibrium composition at high temperature and numerical analysis on a complex thermal flow in the thermal plasma decomposition system are used to predict the process of thermal decomposition of fluorinated gas. In order to increase economic feasibility of the thermal plasma decomposition process, increase of thermal efficiency of the plasma torch and enhancement of gas mixing between the thermal plasma jet and waste gas are discussed. In addition, noble thermal plasma systems to be applied in the thermal plasma gas treatment are introduced in the present paper.

4.3. Decomposition of danburite concentrate of Ak-Arkar Deposit by nitric acid

International Nuclear Information System (INIS)

Mirsaidov, U.M.; Kurbonov, A.S.; Mamatov, E.D.

2015-01-01

Present article is devoted to decomposition of danburite concentrate of Ak-Arkar Deposit by nitric acid. The influence of temperature on reaction process was studied. The dependence of extraction rate of oxides (B 2 O 3 , Al 2 O 3 , Fe 2 O 3 and Ca O) at nitric acid processing on temperature ranges from 25 to 95 deg C was defined. The dependence of extraction rate of oxides (B 2 O 3 , Al 2 O 3 , Fe 2 O 3 and Ca O) at nitric acid processing on process duration (5-60 minutes) was defined as well. The optimal conditions of decomposition of danburite concentrate by nitric acid were proposed.

Some peculiarities of zirconium tungstate synthesis by thermal decomposition of hydrothermal precursors

Energy Technology Data Exchange (ETDEWEB)

Gubanov, Alexander I., E-mail: gubanov@niic.nsc.su [Nikolaev Institute of Inorganic Chemistry, Siberian Branch of the Russian Academy of Sciences, Akad. Lavrentiev Prospekt 3, 630090 Novosibirsk (Russian Federation); Novosibirsk State University, Pirogova str. 2, 630090 Novosibirsk (Russian Federation); Dedova, Elena S. [Institute of Strength Physics and Materials Science, Siberian Branch of the Russian Academy of Sciences, pr. Akademicheskii 2/4, 634021 Tomsk (Russian Federation); Tomsk Polytechnic University, Lenin Avenue 30, 634050 Tomsk (Russian Federation); Plyusnin, Pavel E.; Filatov, Eugeny Y. [Nikolaev Institute of Inorganic Chemistry, Siberian Branch of the Russian Academy of Sciences, Akad. Lavrentiev Prospekt 3, 630090 Novosibirsk (Russian Federation); Novosibirsk State University, Pirogova str. 2, 630090 Novosibirsk (Russian Federation); Kardash, Tatyana Y. [Boreskov Institute of Catalysis, Siberian Branch of the Russian Academy of Sciences, Akad. Lavrentiev Prospekt 5, 630090 Novosibirsk (Russian Federation); Korenev, Sergey V. [Nikolaev Institute of Inorganic Chemistry, Siberian Branch of the Russian Academy of Sciences, Akad. Lavrentiev Prospekt 3, 630090 Novosibirsk (Russian Federation); Novosibirsk State University, Pirogova str. 2, 630090 Novosibirsk (Russian Federation); Kulkov, Sergey N. [Institute of Strength Physics and Materials Science, Siberian Branch of the Russian Academy of Sciences, pr. Akademicheskii 2/4, 634021 Tomsk (Russian Federation); Tomsk Polytechnic University, Lenin Avenue 30, 634050 Tomsk (Russian Federation)

2014-12-10

Highlights: • Synthesis of ZrW{sub 2}O{sub 8} using hydrothermal method. • On hydrothermal synthesis optimal conc. of HCl in the reaction mixture is 2.3 M. • Thermal decomposition of ZrW{sub 2}O{sub 7}((OH){sub 1.5},Cl{sub 0.5})·2H{sub 2}O begins are 200 °S. • Amorphous intermediate crystallizes into cubic single-phase ZrW{sub 2}O{sub 8} above 550 °S. • ZrW{sub 2}O{sub 8} destructed at temperatures above 700 °S. - Abstract: This article discusses some peculiarities of the synthesis of ZrW{sub 2}O{sub 8} (1) using thermal decomposition of the precursor ZrW{sub 2}O{sub 7}((OH){sub 1.5},Cl{sub 0.5})·2H{sub 2}O (2) prepared by hydrothermal method. On hydrothermal synthesis of 2 the optimal concentration of hydrochloric acid in the reaction mixture is about 2.3 M. TG approach to determine the chemical composition of the precursor was suggested. It has been found that the precursor for the synthesis of zirconium tungstate has chemical formula 2. Thermal decomposition of the precursor 2 begins at 200 °S and affords an amorphous intermediate, which crystallizes as a cubic phase 1 above 550 °S with an exoeffect. The temperature of the beginning of the transition from amorphous to the crystalline state is 350 ± 25 °S.

The Effect of Topography on Target Decomposition of Polarimetric SAR Data

Directory of Open Access Journals (Sweden)

Sang-Eun Park

2015-04-01

Full Text Available Polarimetric target decomposition enables the interpretation of radar images more easily, mostly based on physical assumptions, i.e., fitting physically-based scattering models to the polarimetric SAR observations. However, the model-fitting result cannot be always successful. Particularly, the performance of model-fitting in sloping forests is still an open question. In this study, the effect of ground topography on the model-fitting-based polarimetric decomposition techniques is investigated. The estimation accuracy of each scattering component in the decomposition results are evaluated based on the simulated target matrix by using the incoherent vegetation scattering model that accounts for the tilted scattering surface beneath the forest canopy. Experimental results show that the surface and the double-bounce scattering components can be significantly misestimated due to the topographic slope, even when the volume scattering power is successfully estimated.

Identification of liquid-phase decomposition species and reactions for guanidinium azotetrazolate

International Nuclear Information System (INIS)

Kumbhakarna, Neeraj R.; Shah, Kaushal J.; Chowdhury, Arindrajit; Thynell, Stefan T.

2014-01-01

Highlights: • Guanidinium azotetrazolate (GzT) is a high-nitrogen energetic material. • FTIR spectroscopy and ToFMS spectrometry were used for species identification. • Quantum mechanics was used to identify transition states and decomposition pathways. • Important reactions in the GzT liquid-phase decomposition process were identified. • Initiation of decomposition occurs via ring opening, releasing N 2 . - Abstract: The objective of this work is to analyze the decomposition of guanidinium azotetrazolate (GzT) in the liquid phase by using a combined experimental and computational approach. The experimental part involves the use of Fourier transform infrared (FTIR) spectroscopy to acquire the spectral transmittance of the evolved gas-phase species from rapid thermolysis, as well as to acquire spectral transmittance of the condensate and residue formed from the decomposition. Time-of-flight mass spectrometry (ToFMS) is also used to acquire mass spectra of the evolved gas-phase species. Sub-milligram samples of GzT were heated at rates of about 2000 K/s to a set temperature (553–573 K) where decomposition occurred under isothermal conditions. N 2 , NH 3 , HCN, guanidine and melamine were identified as products of decomposition. The computational approach is based on using quantum mechanics for confirming the identity of the species observed in experiments and for identifying elementary chemical reactions that formed these species. In these ab initio techniques, various levels of theory and basis sets were used. Based on the calculated enthalpy and free energy values of various molecular structures, important reaction pathways were identified. Initiation of decomposition of GzT occurs via ring opening to release N 2

Radiation decomposition of alcohols and chloro phenols in micellar systems

International Nuclear Information System (INIS)

Moreno A, J.

1998-01-01

The effect of surfactants on the radiation decomposition yield of alcohols and chloro phenols has been studied with gamma doses of 2, 3, and 5 KGy. These compounds were used as typical pollutants in waste water, and the effect of the water solubility, chemical structure, and the nature of the surfactant, anionic or cationic, was studied. The results show that anionic surfactant like sodium dodecylsulfate (SDS), improve the radiation decomposition yield of ortho-chloro phenol, while cationic surfactant like cetyl trimethylammonium chloride (CTAC), improve the radiation decomposition yield of butyl alcohol. A similar behavior is expected for those alcohols with water solubility close to the studied ones. Surfactant concentrations below critical micellar concentration (CMC), inhibited radiation decomposition for both types of alcohols. However radiation decomposition yield increased when surfactant concentrations were bigger than the CMC. Aromatic alcohols decomposition was more marked than for linear alcohols decomposition. On a mixture of alcohols and chloro phenols in aqueous solution the radiation decomposition yield decreased with increasing surfactant concentration. Nevertheless, there were competitive reactions between the alcohols, surfactants dimers, hydroxyl radical and other reactive species formed on water radiolysis, producing a catalytic positive effect in the decomposition of alcohols. Chemical structure and the number of carbons were not important factors in the radiation decomposition. When an alcohol like ortho-chloro phenol contained an additional chlorine atom, the decomposition of this compound was almost constant. In conclusion the micellar effect depend on both, the nature of the surfactant (anionic or cationic) and the chemical structure of the alcohols. The results of this study are useful for wastewater treatment plants based on the oxidant effect of the hydroxyl radical, like in advanced oxidation processes, or in combined treatment such as

Identification of Diethyl 2,5-Dioxahexane Dicarboxylate and Polyethylene Carbonate as Decomposition Products of Ethylene Carbonate Based Electrolytes by Fourier Transform Infrared Spectroscopy

KAUST Repository

Shi, Feifei; Zhao, Hui; Liu, Gao; Ross, Philip N.; Somorjai, Gabor A.; Komvopoulos, Kyriakos

2014-01-01

The formation of passive films on electrodes due to electrolyte decomposition significantly affects the reversibility of Li-ion batteries (LIBs); however, understanding of the electrolyte decomposition process is still lacking. The decomposition products of ethylene carbonate (EC)-based electrolytes on Sn and Ni electrodes are investigated in this study by Fourier transform infrared (FTIR) spectroscopy. The reference compounds, diethyl 2,5-dioxahexane dicarboxylate (DEDOHC) and polyethylene carbonate (poly-EC), were synthesized, and their chemical structures were characterized by FTIR spectroscopy and nuclear magnetic resonance (NMR). Assignment of the vibration frequencies of these compounds was assisted by quantum chemical (Hartree-Fock) calculations. The effect of Li-ion solvation on the FTIR spectra was studied by introducing the synthesized reference compounds into the electrolyte. EC decomposition products formed on Sn and Ni electrodes were identified as DEDOHC and poly-EC by matching the features of surface species formed on the electrodes with reference spectra. The results of this study demonstrate the importance of accounting for the solvation effect in FTIR analysis of the decomposition products forming on LIB electrodes. © 2014 American Chemical Society.

Identification of Diethyl 2,5-Dioxahexane Dicarboxylate and Polyethylene Carbonate as Decomposition Products of Ethylene Carbonate Based Electrolytes by Fourier Transform Infrared Spectroscopy

KAUST Repository

Shi, Feifei

2014-07-10

The formation of passive films on electrodes due to electrolyte decomposition significantly affects the reversibility of Li-ion batteries (LIBs); however, understanding of the electrolyte decomposition process is still lacking. The decomposition products of ethylene carbonate (EC)-based electrolytes on Sn and Ni electrodes are investigated in this study by Fourier transform infrared (FTIR) spectroscopy. The reference compounds, diethyl 2,5-dioxahexane dicarboxylate (DEDOHC) and polyethylene carbonate (poly-EC), were synthesized, and their chemical structures were characterized by FTIR spectroscopy and nuclear magnetic resonance (NMR). Assignment of the vibration frequencies of these compounds was assisted by quantum chemical (Hartree-Fock) calculations. The effect of Li-ion solvation on the FTIR spectra was studied by introducing the synthesized reference compounds into the electrolyte. EC decomposition products formed on Sn and Ni electrodes were identified as DEDOHC and poly-EC by matching the features of surface species formed on the electrodes with reference spectra. The results of this study demonstrate the importance of accounting for the solvation effect in FTIR analysis of the decomposition products forming on LIB electrodes. © 2014 American Chemical Society.

A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

International Nuclear Information System (INIS)

Zhang, Chu; Zhou, Jianzhong; Li, Chaoshun; Fu, Wenlong; Peng, Tian

2017-01-01

Highlights: • A novel hybrid approach is proposed for wind speed forecasting. • The variational mode decomposition (VMD) is optimized to decompose the original wind speed series. • The input matrix and parameters of ELM are optimized simultaneously by using a hybrid BSA. • Results show that OVMD-HBSA-ELM achieves better performance in terms of prediction accuracy. - Abstract: Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the

Optimal design of compressed air energy storage systems

Energy Technology Data Exchange (ETDEWEB)

Ahrens, F. W.; Sharma, A.; Ragsdell, K. M.

1979-01-01

Compressed air energy storage (CAES) power systems are currently being considered by various electric utilities for load-leveling applications. Models of CAES systems which employ natural underground aquifer formations, and present an optimal design methodology which demonstrates their economic viability are developed. This approach is based upon a decomposition of the CAES plant and utility grid system into three partially-decoupled subsystems. Numerical results are given for a plant employing the Media, Illinois Galesville aquifer formation.

The Ontology of Knowledge Based Optimization

OpenAIRE

Nasution, Mahyuddin K. M.

2012-01-01

Optimization has been becoming a central of studies in mathematic and has many areas with different applications. However, many themes of optimization came from different area have not ties closing to origin concepts. This paper is to address some variants of optimization problems using ontology in order to building basic of knowledge about optimization, and then using it to enhance strategy to achieve knowledge based optimization.

Structural investigation of oxovanadium(IV) Schiff base complexes: X-ray crystallography, electrochemistry and kinetic of thermal decomposition

Czech Academy of Sciences Publication Activity Database

Asadi, M.; Asadi, Z.; Savaripoor, N.; Dušek, Michal; Eigner, Václav; Shorkaei, M.R.; Sedaghat, M.

2015-01-01

Roč. 136, Feb (2015), 625-634 ISSN 1386-1425 R&D Projects: GA ČR(CZ) GAP204/11/0809 Institutional support: RVO:68378271 Keywords : Oxovanadium(IV) complexes * Schiff base * Kinetic s of thermal decomposition * Electrochemistry Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 2.653, year: 2015

PRODUCT OPTIMIZATION METHOD BASED ON ANALYSIS OF OPTIMAL VALUES OF THEIR CHARACTERISTICS

Directory of Open Access Journals (Sweden)

Constantin D. STANESCU

2016-05-01

Full Text Available The paper presents an original method of optimizing products based on the analysis of optimal values of their characteristics . Optimization method comprises statistical model and analytical model . With this original method can easily and quickly obtain optimal product or material .

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

Science.gov (United States)

Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil

2016-01-01

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

An optimization-based approach for facility energy management with uncertainties, and, Power portfolio optimization in deregulated electricity markets with risk management

Science.gov (United States)

Xu, Jun

load, maximize its profit, and manage risks. In this topic, a mid-term power portfolio optimization problem with risk management is presented. Key instruments are considered, risk terms based on semi-variances of spot market transactions are introduced, and penalties on load obligation violations are added to the objective function to improve algorithm convergence and constraint satisfaction. To overcome the inseparability of the resulting problem, a surrogate optimization framework is developed enabling a decomposition and coordination approach. Numerical testing results show that our method effectively provides decisions for various instruments to maximize profit, manage risks, and is computationally efficient.

A new solar power output prediction based on hybrid forecast engine and decomposition model.

Science.gov (United States)

Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

2018-06-12

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Thermal decomposition and reaction of confined explosives

International Nuclear Information System (INIS)

Catalano, E.; McGuire, R.; Lee, E.; Wrenn, E.; Ornellas, D.; Walton, J.

1976-01-01

Some new experiments designed to accurately determine the time interval required to produce a reactive event in confined explosives subjected to temperatures which will cause decomposition are described. Geometry and boundary conditions were both well defined so that these experiments on the rapid thermal decomposition of HE are amenable to predictive modelling. Experiments have been carried out on TNT, TATB and on two plastic-bonded HMX-based high explosives, LX-04 and LX-10. When the results of these experiments are plotted as the logarithm of the time to explosion versus 1/T K (Arrhenius plot), the curves produced are remarkably linear. This is in contradiction to the results obtained by an iterative solution of the Laplace equation for a system with a first order rate heat source. Such calculations produce plots which display considerable curvature. The experiments have also shown that the time to explosion is strongly influenced by the void volume in the containment vessel. Results of the experiments with calculations based on the heat flow equations coupled with first-order models of chemical decomposition are compared. The comparisons demonstrate the need for a more realistic reaction model

Optimizing the Configuration of Sensor Networks to Detect Intruders.

Energy Technology Data Exchange (ETDEWEB)

Brown, Nathanael J. K. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Katherine A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Nozick, Linda Karen [Cornell Univ., Ithaca, NY (United States); Xu, Ningxiong [Cornell Univ., Ithaca, NY (United States)

2015-03-01

This paper focuses on optimizing the selection and configuration of detection technologies to protect a target of interest. The ability of an intruder to simply reach the target is assumed to be sufficient to consider the security system a failure. To address this problem, we develop a game theoretic model of the strategic interactions between the system owner and a knowledgeable intruder. A decomposition-based exact method is used to solve the resultant model.

Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent

Czech Academy of Sciences Publication Activity Database

Wall, D.H.; Bradford, M.A.; John, M.G.St.; Trofymow, J.A.; Behan-Pelletier, V.; Bignell, D.E.; Dangerfield, J.M.; Parton, W.J.; Rusek, Josef; Voigt, W.; Wolters, V.; Gardel, H.Z.; Ayuke, F. O.; Bashford, R.; Beljakova, O.I.; Bohlen, P.J.; Brauman, A.; Flemming, S.; Henschel, J.R.; Johnson, D.L.; Jones, T.H.; Kovářová, Marcela; Kranabetter, J.M.; Kutny, L.; Lin, K.-Ch.; Maryati, M.; Masse, D.; Pokarzhevskii, A.; Rahman, H.; Sabará, M.G.; Salamon, J.-A.; Swift, M.J.; Varela, A.; Vasconcelos, H.L.; White, D.; Zou, X.

2008-01-01

Roč. 14, č. 11 (2008), s. 2661-2677 ISSN 1354-1013 Institutional research plan: CEZ:AV0Z60660521; CEZ:AV0Z60050516 Keywords : climate decomposition index * decomposition * litter Subject RIV: EH - Ecology, Behaviour Impact factor: 5.876, year: 2008

Analysis of temporal-longitudinal-latitudinal characteristics in the global ionosphere based on tensor rank-1 decomposition

Science.gov (United States)

Lu, Shikun; Zhang, Hao; Li, Xihai; Li, Yihong; Niu, Chao; Yang, Xiaoyun; Liu, Daizhi

2018-03-01

Combining analyses of spatial and temporal characteristics of the ionosphere is of great significance for scientific research and engineering applications. Tensor decomposition is performed to explore the temporal-longitudinal-latitudinal characteristics in the ionosphere. Three-dimensional tensors are established based on the time series of ionospheric vertical total electron content maps obtained from the Centre for Orbit Determination in Europe. To obtain large-scale characteristics of the ionosphere, rank-1 decomposition is used to obtain U^{(1)}, U^{(2)}, and U^{(3)}, which are the resulting vectors for the time, longitude, and latitude modes, respectively. Our initial finding is that the correspondence between the frequency spectrum of U^{(1)} and solar variation indicates that rank-1 decomposition primarily describes large-scale temporal variations in the global ionosphere caused by the Sun. Furthermore, the time lags between the maxima of the ionospheric U^{(2)} and solar irradiation range from 1 to 3.7 h without seasonal dependence. The differences in time lags may indicate different interactions between processes in the magnetosphere-ionosphere-thermosphere system. Based on the dataset displayed in the geomagnetic coordinates, the position of the barycenter of U^{(3)} provides evidence for north-south asymmetry (NSA) in the large-scale ionospheric variations. The daily variation in such asymmetry indicates the influences of solar ionization. The diurnal geomagnetic coordinate variations in U^{(3)} show that the large-scale EIA (equatorial ionization anomaly) variations during the day and night have similar characteristics. Considering the influences of geomagnetic disturbance on ionospheric behavior, we select the geomagnetic quiet GIMs to construct the ionospheric tensor. The results indicate that the geomagnetic disturbances have little effect on large-scale ionospheric characteristics.

The Solution of Two-Phase Inverse Stefan Problem Based on a Hybrid Method with Optimization

Directory of Open Access Journals (Sweden)

Yang Yu

2015-01-01

Full Text Available The two-phase Stefan problem is widely used in industrial field. This paper focuses on solving the two-phase inverse Stefan problem when the interface moving is unknown, which is more realistic from the practical point of view. With the help of optimization method, the paper presents a hybrid method which combines the homotopy perturbation method with the improved Adomian decomposition method to solve this problem. Simulation experiment demonstrates the validity of this method. Optimization method plays a very important role in this paper, so we propose a modified spectral DY conjugate gradient method. And the convergence of this method is given. Simulation experiment illustrates the effectiveness of this modified spectral DY conjugate gradient method.

Microbial Signatures of Cadaver Gravesoil During Decomposition.

Science.gov (United States)

Finley, Sheree J; Pechal, Jennifer L; Benbow, M Eric; Robertson, B K; Javan, Gulnaz T

2016-04-01

Genomic studies have estimated there are approximately 10(3)-10(6) bacterial species per gram of soil. The microbial species found in soil associated with decomposing human remains (gravesoil) have been investigated and recognized as potential molecular determinants for estimates of time since death. The nascent era of high-throughput amplicon sequencing of the conserved 16S ribosomal RNA (rRNA) gene region of gravesoil microbes is allowing research to expand beyond more subjective empirical methods used in forensic microbiology. The goal of the present study was to evaluate microbial communities and identify taxonomic signatures associated with the gravesoil human cadavers. Using 16S rRNA gene amplicon-based sequencing, soil microbial communities were surveyed from 18 cadavers placed on the surface or buried that were allowed to decompose over a range of decomposition time periods (3-303 days). Surface soil microbial communities showed a decreasing trend in taxon richness, diversity, and evenness over decomposition, while buried cadaver-soil microbial communities demonstrated increasing taxon richness, consistent diversity, and decreasing evenness. The results show that ubiquitous Proteobacteria was confirmed as the most abundant phylum in all gravesoil samples. Surface cadaver-soil communities demonstrated a decrease in Acidobacteria and an increase in Firmicutes relative abundance over decomposition, while buried soil communities were consistent in their community composition throughout decomposition. Better understanding of microbial community structure and its shifts over time may be important for advancing general knowledge of decomposition soil ecology and its potential use during forensic investigations.

Task Decomposition Module For Telerobot Trajectory Generation

Science.gov (United States)

Wavering, Albert J.; Lumia, Ron

1988-10-01

A major consideration in the design of trajectory generation software for a Flight Telerobotic Servicer (FTS) is that the FTS will be called upon to perform tasks which require a diverse range of manipulator behaviors and capabilities. In a hierarchical control system where tasks are decomposed into simpler and simpler subtasks, the task decomposition module which performs trajectory planning and execution should therefore be able to accommodate a wide range of algorithms. In some cases, it will be desirable to plan a trajectory for an entire motion before manipulator motion commences, as when optimizing over the entire trajectory. Many FTS motions, however, will be highly sensory-interactive, such as moving to attain a desired position relative to a non-stationary object whose position is periodically updated by a vision system. In this case, the time-varying nature of the trajectory may be handled either by frequent replanning using updated sensor information, or by using an algorithm which creates a less specific state-dependent plan that determines the manipulator path as the trajectory is executed (rather than a priori). This paper discusses a number of trajectory generation techniques from these categories and how they may be implemented in a task decompo-sition module of a hierarchical control system. The structure, function, and interfaces of the proposed trajectory gener-ation module are briefly described, followed by several examples of how different algorithms may be performed by the module. The proposed task decomposition module provides a logical structure for trajectory planning and execution, and supports a large number of published trajectory generation techniques.

Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models

DEFF Research Database (Denmark)

Lanne, Markku; Nyberg, Henri

We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use of t...

Cellular decomposition in vikalloys

International Nuclear Information System (INIS)

Belyatskaya, I.S.; Vintajkin, E.Z.; Georgieva, I.Ya.; Golikov, V.A.; Udovenko, V.A.

1981-01-01

Austenite decomposition in Fe-Co-V and Fe-Co-V-Ni alloys at 475-600 deg C is investigated. The cellular decomposition in ternary alloys results in the formation of bcc (ordered) and fcc structures, and in quaternary alloys - bcc (ordered) and 12R structures. The cellular 12R structure results from the emergence of stacking faults in the fcc lattice with irregular spacing in four layers. The cellular decomposition results in a high-dispersion structure and magnetic properties approaching the level of well-known vikalloys [ru

Capturing alternative secondary structures of RNA by decomposition of base-pairing probabilities.

Science.gov (United States)

Hagio, Taichi; Sakuraba, Shun; Iwakiri, Junichi; Mori, Ryota; Asai, Kiyoshi

2018-02-19

It is known that functional RNAs often switch their functions by forming different secondary structures. Popular tools for RNA secondary structures prediction, however, predict the single 'best' structures, and do not produce alternative structures. There are bioinformatics tools to predict suboptimal structures, but it is difficult to detect which alternative secondary structures are essential. We proposed a new computational method to detect essential alternative secondary structures from RNA sequences by decomposing the base-pairing probability matrix. The decomposition is calculated by a newly implemented software tool, RintW, which efficiently computes the base-pairing probability distributions over the Hamming distance from arbitrary reference secondary structures. The proposed approach has been demonstrated on ROSE element RNA thermometer sequence and Lysine RNA ribo-switch, showing that the proposed approach captures conformational changes in secondary structures. We have shown that alternative secondary structures are captured by decomposing base-paring probabilities over Hamming distance. Source code is available from http://www.ncRNA.org/RintW .

Patched bimetallic surfaces are active catalysts for ammonia decomposition.

Science.gov (United States)

Guo, Wei; Vlachos, Dionisios G

2015-10-07

Ammonia decomposition is often used as an archetypical reaction for predicting new catalytic materials and understanding the very reason of why some reactions are sensitive on material's structure. Core-shell or surface-segregated bimetallic nanoparticles expose outstanding activity for many heterogeneously catalysed reactions but the reasons remain elusive owing to the difficulties in experimentally characterizing active sites. Here by performing multiscale simulations in ammonia decomposition on various nickel loadings on platinum (111), we show that the very high activity of core-shell structures requires patches of the guest metal to create and sustain dual active sites: nickel terraces catalyse N-H bond breaking and nickel edge sites drive atomic nitrogen association. The structure sensitivity on these active catalysts depends profoundly on reaction conditions due to kinetically competing relevant elementary reaction steps. We expose a remarkable difference in active sites between transient and steady-state studies and provide insights into optimal material design.

Calcined hydrotalcites for the catalytic decomposition of N{sub 2}O in simulated process streams

Energy Technology Data Exchange (ETDEWEB)

Armor, J.N.; Braymer, T.A.; Farris, T.S.; Li, Y.; Petrocelli, F.P.; Weist, E.L. [Air Products and Chemicals, Inc., Allentown, PA (United States); Kannan, S.; Swamy, C.S. [Department of Chemistry, Indian Institute of Technology, Madras (India)

1996-01-18

Various hydrotalcite based catalysts were prepared for testing for the catalytic decomposition of N{sub 2}O. Co-Al, Ni-Al, Co/Pd-Al, Co/Rh-Al, and Co/Mg-Al substituted hydrotalcites and Co-La-Al hydroxides offer very good activity at modest temperatures. Precalcination of these materials at ca. 450-500C, which destroys the hydrotalcite phase, is necessary for optimum activity and life. For Co substituted hydrotalcites, the optimal ratio of Co/Al is 3.0. The temperature for 50% conversion of N{sub 2}O of these calcined cobalt hydrotalcites is ca. 75C lower than for the previous highly active Co-ZSM-5. These calcined cobalt hydrotalcite materials display sustained life at temperatures in excess of 670C in an O{sub 2} rich, wet stream with high levels of N{sub 2}O (10%). Excess O{sub 2} does not seriously impact N{sub 2}O decomposition, but the combination of both water vapor and O{sub 2} does reduce activity by ca. 50%

Synthesis and in vacuo deposition of iron oxide nanoparticles by microplasma-assisted decomposition of ferrocene

International Nuclear Information System (INIS)

Schaefer, Michael; Kumar, Ajay; Mohan Sankaran, R.; Schlaf, Rudy

2014-01-01

Microplasma-assisted gas-phase nucleation has emerged as an important new approach to produce high-purity, nanometer-sized, and narrowly dispersed particles. This study aims to integrate this technique with vacuum conditions to enable synthesis and deposition in an ultrahigh vacuum compatible environment. The ultimate goal is to combine nanoparticle synthesis with photoemission spectroscopy-based electronic structure analysis. Such measurements require in vacuo deposition to prevent surface contamination from sample transfer, which can be deleterious for nanoscale materials. A homebuilt microplasma reactor was integrated into an existing atomic layer deposition system attached to a surface science multi-chamber system equipped with photoemission spectroscopy. As proof-of-concept, we studied the decomposition of ferrocene vapor in the microplasma to synthesize iron oxide nanoparticles. The injection parameters were optimized to achieve complete precursor decomposition under vacuum conditions, and nanoparticles were successfully deposited. The stoichiometry of the deposited samples was characterized in situ using X-ray photoelectron spectroscopy indicating that iron oxide was formed. Additional transmission electron spectroscopy characterization allowed the determination of the size, shape, and crystal lattice of the particles, confirming their structural properties.

Multiple image encryption scheme based on pixel exchange operation and vector decomposition

Science.gov (United States)

Xiong, Y.; Quan, C.; Tay, C. J.

2018-02-01

We propose a new multiple image encryption scheme based on a pixel exchange operation and a basic vector decomposition in Fourier domain. In this algorithm, original images are imported via a pixel exchange operator, from which scrambled images and pixel position matrices are obtained. Scrambled images encrypted into phase information are imported using the proposed algorithm and phase keys are obtained from the difference between scrambled images and synthesized vectors in a charge-coupled device (CCD) plane. The final synthesized vector is used as an input in a random phase encoding (DRPE) scheme. In the proposed encryption scheme, pixel position matrices and phase keys serve as additional private keys to enhance the security of the cryptosystem which is based on a 4-f system. Numerical simulations are presented to demonstrate the feasibility and robustness of the proposed encryption scheme.

High Performance Polar Decomposition on Distributed Memory Systems

KAUST Repository

Sukkari, Dalal E.

2016-08-08

The polar decomposition of a dense matrix is an important operation in linear algebra. It can be directly calculated through the singular value decomposition (SVD) or iteratively using the QR dynamically-weighted Halley algorithm (QDWH). The former is difficult to parallelize due to the preponderant number of memory-bound operations during the bidiagonal reduction. We investigate the latter scenario, which performs more floating-point operations but exposes at the same time more parallelism, and therefore, runs closer to the theoretical peak performance of the system, thanks to more compute-bound matrix operations. Profiling results show the performance scalability of QDWH for calculating the polar decomposition using around 9200 MPI processes on well and ill-conditioned matrices of 100K×100K problem size. We study then the performance impact of the QDWH-based polar decomposition as a pre-processing step toward calculating the SVD itself. The new distributed-memory implementation of the QDWH-SVD solver achieves up to five-fold speedup against current state-of-the-art vendor SVD implementations. © Springer International Publishing Switzerland 2016.

An Improved Interferometric Calibration Method Based on Independent Parameter Decomposition

Science.gov (United States)

Fan, J.; Zuo, X.; Li, T.; Chen, Q.; Geng, X.

2018-04-01

Interferometric SAR is sensitive to earth surface undulation. The accuracy of interferometric parameters plays a significant role in precise digital elevation model (DEM). The interferometric calibration is to obtain high-precision global DEM by calculating the interferometric parameters using ground control points (GCPs). However, interferometric parameters are always calculated jointly, making them difficult to decompose precisely. In this paper, we propose an interferometric calibration method based on independent parameter decomposition (IPD). Firstly, the parameters related to the interferometric SAR measurement are determined based on the three-dimensional reconstruction model. Secondly, the sensitivity of interferometric parameters is quantitatively analyzed after the geometric parameters are completely decomposed. Finally, each interferometric parameter is calculated based on IPD and interferometric calibration model is established. We take Weinan of Shanxi province as an example and choose 4 TerraDEM-X image pairs to carry out interferometric calibration experiment. The results show that the elevation accuracy of all SAR images is better than 2.54 m after interferometric calibration. Furthermore, the proposed method can obtain the accuracy of DEM products better than 2.43 m in the flat area and 6.97 m in the mountainous area, which can prove the correctness and effectiveness of the proposed IPD based interferometric calibration method. The results provide a technical basis for topographic mapping of 1 : 50000 and even larger scale in the flat area and mountainous area.

Dinitraminic acid (HDN) isomerization and self-decomposition revisited

International Nuclear Information System (INIS)

Rahm, Martin; Brinck, Tore

2008-01-01

Density functional theory (DFT) and the ab initio based CBS-QB3 method have been used to study possible decomposition pathways of dinitraminic acid HN(NO 2 ) 2 (HDN) in gas-phase. The proton transfer isomer of HDN, O 2 NNN(O)OH, and its conformers can be formed and converted into each other through intra- and intermolecular proton transfer. The latter has been shown to proceed substantially faster via double proton transfer. The main mechanism for HDN decomposition is found to be initiated by a dissociation reaction, splitting of nitrogen dioxide from either HDN or the HDN isomer. This reaction has an activation enthalpy of 36.5 kcal/mol at the CBS-QB3 level, which is in good agreement with experimental estimates of the decomposition barrier

Determination of knock characteristics in spark ignition engines: an approach based on ensemble empirical mode decomposition

International Nuclear Information System (INIS)

Li, Ning; Liang, Caiping; Yang, Jianguo; Zhou, Rui

2016-01-01

Knock is one of the major constraints to improve the performance and thermal efficiency of spark ignition (SI) engines. It can also result in severe permanent engine damage under certain operating conditions. Based on the ensemble empirical mode decomposition (EEMD), this paper proposes a new approach to determine the knock characteristics in SI engines. By adding a uniformly distributed and finite white Gaussian noise, the EEMD can preserve signal continuity in different scales and therefore alleviates the mode-mixing problem occurring in the classic empirical mode decomposition (EMD). The feasibilities of applying the EEMD to detect the knock signatures of a test SI engine via the pressure signal measured from combustion chamber and the vibration signal measured from cylinder head are investigated. Experimental results show that the EEMD-based method is able to detect the knock signatures from both the pressure signal and vibration signal, even in initial stage of knock. Finally, by comparing the application results with those obtained by short-time Fourier transform (STFT), Wigner–Ville distribution (WVD) and discrete wavelet transform (DWT), the superiority of the EEMD method in determining knock characteristics is demonstrated. (paper)

Coverage-based constraints for IMRT optimization

Science.gov (United States)

Mescher, H.; Ulrich, S.; Bangert, M.

2017-09-01

Radiation therapy treatment planning requires an incorporation of uncertainties in order to guarantee an adequate irradiation of the tumor volumes. In current clinical practice, uncertainties are accounted for implicitly with an expansion of the target volume according to generic margin recipes. Alternatively, it is possible to account for uncertainties by explicit minimization of objectives that describe worst-case treatment scenarios, the expectation value of the treatment or the coverage probability of the target volumes during treatment planning. In this note we show that approaches relying on objectives to induce a specific coverage of the clinical target volumes are inevitably sensitive to variation of the relative weighting of the objectives. To address this issue, we introduce coverage-based constraints for intensity-modulated radiation therapy (IMRT) treatment planning. Our implementation follows the concept of coverage-optimized planning that considers explicit error scenarios to calculate and optimize patient-specific probabilities q(\\hat{d}, \\hat{v}) of covering a specific target volume fraction \\hat{v} with a certain dose \\hat{d} . Using a constraint-based reformulation of coverage-based objectives we eliminate the trade-off between coverage and competing objectives during treatment planning. In-depth convergence tests including 324 treatment plan optimizations demonstrate the reliability of coverage-based constraints for varying levels of probability, dose and volume. General clinical applicability of coverage-based constraints is demonstrated for two cases. A sensitivity analysis regarding penalty variations within this planing study based on IMRT treatment planning using (1) coverage-based constraints, (2) coverage-based objectives, (3) probabilistic optimization, (4) robust optimization and (5) conventional margins illustrates the potential benefit of coverage-based constraints that do not require tedious adjustment of target volume objectives.

Accelerator mass spectrometry 14C determination in CO2 produced from laser decomposition of aragonite.

Science.gov (United States)

Rosenheim, Brad E; Thorrold, Simon R; Roberts, Mark L

2008-11-01

The determination of (14)C in aragonite (CaCO(3)) decomposed thermally to CO(2) using an yttrium-aluminum-garnet doped neodymium laser is reported. Laser decomposition accelerator mass spectrometry (LD-AMS) measurements reproduce AMS determinations of (14)C from the conventional reaction of aragonite with concentrated phosphoric acid. The lack of significant differences between these sets of measurements indicates that LD-AMS radiocarbon dating can overcome the significant fractionation that has been observed during stable isotope (C and O) laser decomposition analysis of different carbonate minerals. The laser regularly converted nearly 30% of material removed into CO(2) despite it being optimized for ablation, where laser energy breaks material apart rather than chemically altering it. These results illustrate promise for using laser decomposition on the front-end of AMS systems that directly measure CO(2) gas. The feasibility of such measurements depends on (1) the improvement of material removal and/or CO(2) generation efficiency of the laser decomposition system and (2) the ionization efficiency of AMS systems measuring continuously flowing CO(2).

A solution approach based on Benders decomposition for the preventive maintenance scheduling problem of a stochastic large-scale energy system

DEFF Research Database (Denmark)

Lusby, Richard Martin; Muller, Laurent Flindt; Petersen, Bjørn

2013-01-01

This paper describes a Benders decomposition-based framework for solving the large scale energy management problem that was posed for the ROADEF 2010 challenge. The problem was taken from the power industry and entailed scheduling the outage dates for a set of nuclear power plants, which need...... to be regularly taken down for refueling and maintenance, in such away that the expected cost of meeting the power demand in a number of potential scenarios is minimized. We show that the problem structure naturally lends itself to Benders decomposition; however, not all constraints can be included in the mixed...

Introduction - Acid decomposition of borosilicate ores

International Nuclear Information System (INIS)

Mirsaidov, U.M.; Kurbonov, A.S.; Mamatov, E.D.

2015-01-01

The complex processing of mineral raw materials is an effective way for the extraction of valuable components. One of these raw materials are borosilicate ores from which the boric acid, aluminium and iron salts and building materials can be obtained. In the Institute of Chemistry of the Academy of Sciences of the Republic of Tajikistan the flowsheets of the processing of borosilicate raw materials by acid and chloric methods were elaborated. The acid methods of decomposition of borosilicate ores of Ak-Arkhar Deposit were considered in present monograph. The carried out researches on elaboration of physicochemical aspects and technological acid methods allowed to define the optimal ways of extraction of valuable products from borosilicate raw materials of Tajikistan.

Classification Formula and Generation Algorithm of Cycle Decomposition Expression for Dihedral Groups

Directory of Open Access Journals (Sweden)

Dakun Zhang

2013-01-01

Full Text Available The necessary of classification research on common formula of group (dihedral group cycle decomposition expression is illustrated. It includes the reflection and rotation conversion, which derived six common formulae on cycle decomposition expressions of group; it designed the generation algorithm on the cycle decomposition expressions of group, which is based on the method of replacement conversion and the classification formula; algorithm analysis and the results of the process show that the generation algorithm which is based on the classification formula is outperformed by the general algorithm which is based on replacement conversion; it has great significance to solve the enumeration of the necklace combinational scheme, especially the structural problems of combinational scheme, by using group theory and computer.

Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

Science.gov (United States)

Peng, Chaoyong; Gao, Xiaorong; Peng, Jianping; Wang, Ai

2018-04-01

As one of the key components of railway vehicles, the operation condition of the axle box bearing has a significant effect on traffic safety. The acoustic diagnosis is more suitable than vibration diagnosis for trackside monitoring. The acoustic signal generated by the train axle box bearing is an amplitude modulation and frequency modulation signal with complex train running noise. Although empirical mode decomposition (EMD) and some improved time-frequency algorithms have proved to be useful in bearing vibration signal processing, it is hard to extract the bearing fault signal from serious trackside acoustic background noises by using those algorithms. Therefore, a kurtosis-optimization-based wavelet packet (KWP) denoising algorithm is proposed, as the kurtosis is the key indicator of bearing fault signal in time domain. Firstly, the geometry based Doppler correction is applied to signals of each sensor, and with the signal superposition of multiple sensors, random noises and impulse noises, which are the interference of the kurtosis indicator, are suppressed. Then, the KWP is conducted. At last, the EMD and Hilbert transform is applied to extract the fault feature. Experiment results indicate that the proposed method consisting of KWP and EMD is superior to the EMD.

Optimization Problems on Threshold Graphs

Directory of Open Access Journals (Sweden)

Elena Nechita

2010-06-01

Full Text Available During the last three decades, different types of decompositions have been processed in the field of graph theory. Among these we mention: decompositions based on the additivity of some characteristics of the graph, decompositions where the adjacency law between the subsets of the partition is known, decompositions where the subgraph induced by every subset of the partition must have predeterminate properties, as well as combinations of such decompositions. In this paper we characterize threshold graphs using the weakly decomposition, determine: density and stability number, Wiener index and Wiener polynomial for threshold graphs.

Decomposition and Cross-Product-Based Method for Computing the Dynamic Equation of Robots

Directory of Open Access Journals (Sweden)

Ching-Long Shih

2012-08-01

Full Text Available This paper aims to demonstrate a clear relationship between Lagrange equations and Newton-Euler equations regarding computational methods for robot dynamics, from which we derive a systematic method for using either symbolic or on-line numerical computations. Based on the decomposition approach and cross-product operation, a computing method for robot dynamics can be easily developed. The advantages of this computing framework are that: it can be used for both symbolic and on-line numeric computation purposes, and it can also be applied to biped systems, as well as some simple closed-chain robot systems.

Geometric decomposition of the conformation tensor in viscoelastic turbulence

Science.gov (United States)

Hameduddin, Ismail; Meneveau, Charles; Zaki, Tamer A.; Gayme, Dennice F.

2018-05-01

This work introduces a mathematical approach to analysing the polymer dynamics in turbulent viscoelastic flows that uses a new geometric decomposition of the conformation tensor, along with associated scalar measures of the polymer fluctuations. The approach circumvents an inherent difficulty in traditional Reynolds decompositions of the conformation tensor: the fluctuating tensor fields are not positive-definite and so do not retain the physical meaning of the tensor. The geometric decomposition of the conformation tensor yields both mean and fluctuating tensor fields that are positive-definite. The fluctuating tensor in the present decomposition has a clear physical interpretation as a polymer deformation relative to the mean configuration. Scalar measures of this fluctuating conformation tensor are developed based on the non-Euclidean geometry of the set of positive-definite tensors. Drag-reduced viscoelastic turbulent channel flow is then used an example case study. The conformation tensor field, obtained using direct numerical simulations, is analysed using the proposed framework.

Catalytic performance and durability of Ni/AC for HI decomposition in sulfur–iodine thermochemical cycle for hydrogen production

International Nuclear Information System (INIS)

Fu, Guangshi; He, Yong; Zhang, Yanwei; Zhu, Yanqun; Wang, Zhihua; Cen, Kefa

2016-01-01

Highlights: • The relation between Ni content and Ni particle dispersion were disclosed. • The effect of Ni content on the catalytic activity of Ni/AC catalyst was revealed. • The optimal content of Ni for Ni/AC catalysts in HI decomposition was found. - Abstract: This work reports the Ni content effect on the Ni/AC catalytic performance in the HI decomposition reaction of the sulfur–iodine (SI) thermochemical cycle for hydrogen production and the Ni/AC catalyst durability in a long-term test. Accordingly, five catalysts with the Ni content ranging from 5% to 15% were prepared by an incipient-wetness impregnation method. The activity of all catalysts was examined under the temperature range of 573–773 K. The catalytic performance evaluation suggests that Ni content plays a significant role in the Ni dispersion, Ni particle size, and eventually the catalytic activity in HI decomposition. 12% is the optimal Ni content for Ni/AC catalysts in HI decomposition which is balanced between poor dispersion of Ni particles and increasing active center. The results of 24 h durability test, which incorporated with BET and TEM investigations of the 12%Ni/AC catalyst before and after the reaction, indicate that establishing a better Ni particle dispersion pattern and improving the stability of Ni particles on the support should be considered in the future.

Model Reduction Using Proper Orthogonal Decomposition and Predictive Control of Distributed Reactor System

Directory of Open Access Journals (Sweden)

Alejandro Marquez

2013-01-01

Full Text Available This paper studies the application of proper orthogonal decomposition (POD to reduce the order of distributed reactor models with axial and radial diffusion and the implementation of model predictive control (MPC based on discrete-time linear time invariant (LTI reduced-order models. In this paper, the control objective is to keep the operation of the reactor at a desired operating condition in spite of the disturbances in the feed flow. This operating condition is determined by means of an optimization algorithm that provides the optimal temperature and concentration profiles for the system. Around these optimal profiles, the nonlinear partial differential equations (PDEs, that model the reactor are linearized, and afterwards the linear PDEs are discretized in space giving as a result a high-order linear model. POD and Galerkin projection are used to derive the low-order linear model that captures the dominant dynamics of the PDEs, which are subsequently used for controller design. An MPC formulation is constructed on the basis of the low-order linear model. The proposed approach is tested through simulation, and it is shown that the results are good with regard to keep the operation of the reactor.

Multiresolution signal decomposition schemes

NARCIS (Netherlands)

J. Goutsias (John); H.J.A.M. Heijmans (Henk)

1998-01-01

textabstract[PNA-R9810] Interest in multiresolution techniques for signal processing and analysis is increasing steadily. An important instance of such a technique is the so-called pyramid decomposition scheme. This report proposes a general axiomatic pyramid decomposition scheme for signal analysis

Void reactivity decomposition for the Sodium-cooled Fast Reactor in equilibrium fuel cycle

Energy Technology Data Exchange (ETDEWEB)

Sun Kaichao, E-mail: kaichao.sun@psi.ch [Paul Scherrer Institut (PSI), 5232 Villigen PSI (Switzerland); Ecole Polytechnique Federale de Lausanne (EPFL), 1015 Lausanne (Switzerland); Krepel, Jiri; Mikityuk, Konstantin; Pelloni, Sandro [Paul Scherrer Institut (PSI), 5232 Villigen PSI (Switzerland); Chawla, Rakesh [Paul Scherrer Institut (PSI), 5232 Villigen PSI (Switzerland); Ecole Polytechnique Federale de Lausanne (EPFL), 1015 Lausanne (Switzerland)

2011-07-15

Highlights: > We analyze the void reactivity effect for three ESFR core fuel cycle states. > The void reactivity effect is decomposed by neutron balance method. > Novelly, the normalization to the integral flux in the active core is applied. > The decomposition is compared with the perturbation theory based results. > The mechanism and the differences of the void reactivity effect are explained. - Abstract: The Sodium-cooled Fast Reactor (SFR) is one of the most promising Generation IV systems with many advantages, but has one dominating neutronic drawback - a positive sodium void reactivity. The aim of this study is to develop and apply a methodology, which should help better understand the causes and consequences of the sodium void effect. It focuses not only on the beginning-of-life (BOL) state of the core, but also on the beginning of open and closed equilibrium (BOC and BEC, respectively) fuel cycle conditions. The deeper understanding of the principal phenomena involved may subsequently lead to appropriate optimization studies. Various voiding scenarios, corresponding to different spatial zones, e.g. node or assembly, have been analyzed, and the most conservative case - the voiding of both inner and outer fuel zones - has been selected as the reference scenario. On the basis of the neutron balance method, the corresponding SFR void reactivity has been decomposed reaction-, isotope-, and energy-group-wise. Complementary results, based on generalized perturbation theory and sensitivity analysis, are also presented. The numerical analysis for both neutron balance and perturbation theory methods has been carried out using appropriate modules of the ERANOS code system. A strong correlation between the flux worth, i.e. the product of flux and adjoint flux, and the void reactivity importance distributions has been found for the node- and assembly-wise voiding scenarios. The neutron balance based decomposition has shown that the void effect is caused mainly by the

Void reactivity decomposition for the Sodium-cooled Fast Reactor in equilibrium fuel cycle

International Nuclear Information System (INIS)

Sun Kaichao; Krepel, Jiri; Mikityuk, Konstantin; Pelloni, Sandro; Chawla, Rakesh

2011-01-01

Highlights: → We analyze the void reactivity effect for three ESFR core fuel cycle states. → The void reactivity effect is decomposed by neutron balance method. → Novelly, the normalization to the integral flux in the active core is applied. → The decomposition is compared with the perturbation theory based results. → The mechanism and the differences of the void reactivity effect are explained. - Abstract: The Sodium-cooled Fast Reactor (SFR) is one of the most promising Generation IV systems with many advantages, but has one dominating neutronic drawback - a positive sodium void reactivity. The aim of this study is to develop and apply a methodology, which should help better understand the causes and consequences of the sodium void effect. It focuses not only on the beginning-of-life (BOL) state of the core, but also on the beginning of open and closed equilibrium (BOC and BEC, respectively) fuel cycle conditions. The deeper understanding of the principal phenomena involved may subsequently lead to appropriate optimization studies. Various voiding scenarios, corresponding to different spatial zones, e.g. node or assembly, have been analyzed, and the most conservative case - the voiding of both inner and outer fuel zones - has been selected as the reference scenario. On the basis of the neutron balance method, the corresponding SFR void reactivity has been decomposed reaction-, isotope-, and energy-group-wise. Complementary results, based on generalized perturbation theory and sensitivity analysis, are also presented. The numerical analysis for both neutron balance and perturbation theory methods has been carried out using appropriate modules of the ERANOS code system. A strong correlation between the flux worth, i.e. the product of flux and adjoint flux, and the void reactivity importance distributions has been found for the node- and assembly-wise voiding scenarios. The neutron balance based decomposition has shown that the void effect is caused mainly

Thermal decomposition of beryllium perchlorate tetrahydrate

International Nuclear Information System (INIS)

Berezkina, L.G.; Borisova, S.I.; Tamm, N.S.; Novoselova, A.V.

1975-01-01

Thermal decomposition of Be(ClO 4 ) 2 x4H 2 O was studied by the differential flow technique in the helium stream. The kinetics was followed by an exchange reaction of the perchloric acid appearing by the decomposition with potassium carbonate. The rate of CO 2 liberation in this process was recorded by a heat conductivity detector. The exchange reaction yielding CO 2 is quantitative, it is not the limiting one and it does not distort the kinetics of the process of perchlorate decomposition. The solid products of decomposition were studied by infrared and NMR spectroscopy, roentgenography, thermography and chemical analysis. A mechanism suggested for the decomposition involves intermediate formation of hydroxyperchlorate: Be(ClO 4 ) 2 x4H 2 O → Be(OH)ClO 4 +HClO 4 +3H 2 O; Be(OH)ClO 4 → BeO+HClO 4 . Decomposition is accompained by melting of the sample. The mechanism of decomposition is hydrolytic. At room temperature the hydroxyperchlorate is a thick syrup-like compound crystallizing after long storing

Dual decomposition for parsing with non-projective head automata

OpenAIRE

Koo, Terry; Rush, Alexander Matthew; Collins, Michael; Jaakkola, Tommi S.; Sontag, David Alexander

2010-01-01

This paper introduces algorithms for non-projective parsing based on dual decomposition. We focus on parsing algorithms for non-projective head automata, a generalization of head-automata models to non-projective structures. The dual decomposition algorithms are simple and efficient, relying on standard dynamic programming and minimum spanning tree algorithms. They provably solve an LP relaxation of the non-projective parsing problem. Empirically the LP relaxation is very often tight: for man...

Java-Based Coupling for Parallel Predictive-Adaptive Domain Decomposition

Directory of Open Access Journals (Sweden)

Cécile Germain‐Renaud

1999-01-01

Full Text Available Adaptive domain decomposition exemplifies the problem of integrating heterogeneous software components with intermediate coupling granularity. This paper describes an experiment where a data‐parallel (HPF client interfaces with a sequential computation server through Java. We show that seamless integration of data‐parallelism is possible, but requires most of the tools from the Java palette: Java Native Interface (JNI, Remote Method Invocation (RMI, callbacks and threads.

Reactive Goal Decomposition Hierarchies for On-Board Autonomy

Science.gov (United States)

Hartmann, L.

2002-01-01

to state and environment and in general can terminate the execution of a decomposition and attempt a new decomposition at any level in the hierarchy. This goal decomposition system is suitable for workstation, microprocessor and fpga implementation and thus is able to support the full range of prototyping activities, from mission design in the laboratory to development of the fpga firmware for the flight system. This approach is based on previous artificial intelligence work including (1) Brooks' subsumption architecture for robot control, (2) Firby's Reactive Action Package System (RAPS) for mediating between high level automated planning and low level execution and (3) hierarchical task networks for automated planning. Reactive goal decomposition hierarchies can be used for a wide variety of on-board autonomy applications including automating low level operation sequences (such as scheduling prerequisite operations, e.g., heaters, warm-up periods, monitoring power constraints), coordinating multiple spacecraft as in formation flying and constellations, robot manipulator operations, rendez-vous, docking, servicing, assembly, on-orbit maintenance, planetary rover operations, solar system and interstellar probes, intelligent science data gathering and disaster early warning. Goal decomposition hierarchies can support high level fault tolerance. Given models of on-board resources and goals to accomplish, the decomposition hierarchy could allocate resources to goals taking into account existing faults and in real-time reallocating resources as new faults arise. Resources to be modeled include memory (e.g., ROM, FPGA configuration memory, processor memory, payload instrument memory), processors, on-board and interspacecraft network nodes and links, sensors, actuators (e.g., attitude determination and control, guidance and navigation) and payload instruments. A goal decomposition hierarchy could be defined to map mission goals and tasks to available on-board resources. As

Optimal Active Power Control of A Wind Farm Equipped with Energy Storage System based on Distributed Model Predictive Control

DEFF Research Database (Denmark)

Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

2016-01-01

This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm...... is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads...

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

Pixel-based OPC optimization based on conjugate gradients.

Science.gov (United States)

Ma, Xu; Arce, Gonzalo R

2011-01-31

Optical proximity correction (OPC) methods are resolution enhancement techniques (RET) used extensively in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the mask is divided into small pixels, each of which is modified during the optimization process. Two critical issues in PBOPC are the required computational complexity of the optimization process, and the manufacturability of the optimized mask. Most current OPC optimization methods apply the steepest descent (SD) algorithm to improve image fidelity augmented by regularization penalties to reduce the complexity of the mask. Although simple to implement, the SD algorithm converges slowly. The existing regularization penalties, however, fall short in meeting the mask rule check (MRC) requirements often used in semiconductor manufacturing. This paper focuses on developing OPC optimization algorithms based on the conjugate gradient (CG) method which exhibits much faster convergence than the SD algorithm. The imaging formation process is represented by the Fourier series expansion model which approximates the partially coherent system as a sum of coherent systems. In order to obtain more desirable manufacturability properties of the mask pattern, a MRC penalty is proposed to enlarge the linear size of the sub-resolution assistant features (SRAFs), as well as the distances between the SRAFs and the main body of the mask. Finally, a projection method is developed to further reduce the complexity of the optimized mask pattern.

Spatial and Inter-temporal Sources of Poverty, Inequality and Gender Disparities in Cameroon: a Regression-Based Decomposition Analysis

OpenAIRE

Boniface Ngah Epo; Francis Menjo Baye; Nadine Teme Angele Manga

2011-01-01

This study applies the regression-based inequality decomposition technique to explain poverty and inequality trends in Cameroon. We also identify gender related factors which explain income disparities and discrimination based on the 2001 and 2007 Cameroon household consumption surveys. The results show that education, health, employment in the formal sector, age cohorts, household size, gender, ownership of farmland and urban versus rural residence explain household economic wellbeing; dispa...

Development of GPT-based optimization algorithm

International Nuclear Information System (INIS)

White, J.R.; Chapman, D.M.; Biswas, D.

1985-01-01

The University of Lowell and Westinghouse Electric Corporation are involved in a joint effort to evaluate the potential benefits of generalized/depletion perturbation theory (GPT/DTP) methods for a variety of light water reactor (LWR) physics applications. One part of that work has focused on the development of a GPT-based optimization algorithm for the overall design, analysis, and optimization of LWR reload cores. The use of GPT sensitivity data in formulating the fuel management optimization problem is conceptually straightforward; it is the actual execution of the concept that is challenging. Thus, the purpose of this paper is to address some of the major difficulties, to outline our approach to these problems, and to present some illustrative examples of an efficient GTP-based optimization scheme

Multi-country comparisons of energy performance: The index decomposition analysis approach

International Nuclear Information System (INIS)

Ang, B.W.; Xu, X.Y.; Su, Bin

2015-01-01

Index decomposition analysis (IDA) is a popular tool for studying changes in energy consumption over time in a country or region. This specific application of IDA, which may be called temporal decomposition analysis, has been extended by researchers and analysts to study variations in energy consumption or energy efficiency between countries or regions, i.e. spatial decomposition analysis. In spatial decomposition analysis, the main objective is often to understand the relative contributions of overall activity level, activity structure, and energy intensity in explaining differences in total energy consumption between two countries or regions. We review the literature of spatial decomposition analysis, investigate the methodological issues, and propose a spatial decomposition analysis framework for multi-region comparisons. A key feature of the proposed framework is that it passes the circularity test and provides consistent results for multi-region comparisons. A case study in which 30 regions in China are compared and ranked based on their performance in energy consumption is presented. - Highlights: • We conducted cross-regional comparisons of energy consumption using IDA. • We proposed two criteria for IDA method selection in spatial decomposition analysis. • We proposed a new model for regional comparison that passes the circularity test. • Features of the new model are illustrated using the data of 30 regions in China

Amplitude-cyclic frequency decomposition of vibration signals for bearing fault diagnosis based on phase editing

Science.gov (United States)

Barbini, L.; Eltabach, M.; Hillis, A. J.; du Bois, J. L.

2018-03-01

In rotating machine diagnosis different spectral tools are used to analyse vibration signals. Despite the good diagnostic performance such tools are usually refined, computationally complex to implement and require oversight of an expert user. This paper introduces an intuitive and easy to implement method for vibration analysis: amplitude cyclic frequency decomposition. This method firstly separates vibration signals accordingly to their spectral amplitudes and secondly uses the squared envelope spectrum to reveal the presence of cyclostationarity in each amplitude level. The intuitive idea is that in a rotating machine different components contribute vibrations at different amplitudes, for instance defective bearings contribute a very weak signal in contrast to gears. This paper also introduces a new quantity, the decomposition squared envelope spectrum, which enables separation between the components of a rotating machine. The amplitude cyclic frequency decomposition and the decomposition squared envelope spectrum are tested on real word signals, both at stationary and varying speeds, using data from a wind turbine gearbox and an aircraft engine. In addition a benchmark comparison to the spectral correlation method is presented.

Hyperspectral chemical plume detection algorithms based on multidimensional iterative filtering decomposition.

Science.gov (United States)

Cicone, A; Liu, J; Zhou, H

2016-04-13

Chemicals released in the air can be extremely dangerous for human beings and the environment. Hyperspectral images can be used to identify chemical plumes, however the task can be extremely challenging. Assuming we know a priori that some chemical plume, with a known frequency spectrum, has been photographed using a hyperspectral sensor, we can use standard techniques such as the so-called matched filter or adaptive cosine estimator, plus a properly chosen threshold value, to identify the position of the chemical plume. However, due to noise and inadequate sensing, the accurate identification of chemical pixels is not easy even in this apparently simple situation. In this paper, we present a post-processing tool that, in a completely adaptive and data-driven fashion, allows us to improve the performance of any classification methods in identifying the boundaries of a plume. This is done using the multidimensional iterative filtering (MIF) algorithm (Cicone et al. 2014 (http://arxiv.org/abs/1411.6051); Cicone & Zhou 2015 (http://arxiv.org/abs/1507.07173)), which is a non-stationary signal decomposition method like the pioneering empirical mode decomposition method (Huang et al. 1998 Proc. R. Soc. Lond. A 454, 903. (doi:10.1098/rspa.1998.0193)). Moreover, based on the MIF technique, we propose also a pre-processing method that allows us to decorrelate and mean-centre a hyperspectral dataset. The cosine similarity measure, which often fails in practice, appears to become a successful and outperforming classifier when equipped with such a pre-processing method. We show some examples of the proposed methods when applied to real-life problems. © 2016 The Author(s).

Bare-Bones Teaching-Learning-Based Optimization

Directory of Open Access Journals (Sweden)

Feng Zou

2014-01-01

Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Some nonlinear space decomposition algorithms

Energy Technology Data Exchange (ETDEWEB)

Tai, Xue-Cheng; Espedal, M. [Univ. of Bergen (Norway)

1996-12-31

Convergence of a space decomposition method is proved for a general convex programming problem. The space decomposition refers to methods that decompose a space into sums of subspaces, which could be a domain decomposition or a multigrid method for partial differential equations. Two algorithms are proposed. Both can be used for linear as well as nonlinear elliptic problems and they reduce to the standard additive and multiplicative Schwarz methods for linear elliptic problems. Two {open_quotes}hybrid{close_quotes} algorithms are also presented. They converge faster than the additive one and have better parallelism than the multiplicative method. Numerical tests with a two level domain decomposition for linear, nonlinear and interface elliptic problems are presented for the proposed algorithms.

Optimally growing boundary layer disturbances in a convergent nozzle preceded by a circular pipe

Science.gov (United States)

Uzun, Ali; Davis, Timothy B.; Alvi, Farrukh S.; Hussaini, M. Yousuff

2017-06-01

We report the findings from a theoretical analysis of optimally growing disturbances in an initially turbulent boundary layer. The motivation behind this study originates from the desire to generate organized structures in an initially turbulent boundary layer via excitation by disturbances that are tailored to be preferentially amplified. Such optimally growing disturbances are of interest for implementation in an active flow control strategy that is investigated for effective jet noise control. Details of the optimal perturbation theory implemented in this study are discussed. The relevant stability equations are derived using both the standard decomposition and the triple decomposition. The chosen test case geometry contains a convergent nozzle, which generates a Mach 0.9 round jet, preceded by a circular pipe. Optimally growing disturbances are introduced at various stations within the circular pipe section to facilitate disturbance energy amplification upstream of the favorable pressure gradient zone within the convergent nozzle, which has a stabilizing effect on disturbance growth. Effects of temporal frequency, disturbance input and output plane locations as well as separation distance between output and input planes are investigated. The results indicate that optimally growing disturbances appear in the form of longitudinal counter-rotating vortex pairs, whose size can be on the order of several times the input plane mean boundary layer thickness. The azimuthal wavenumber, which represents the number of counter-rotating vortex pairs, is found to generally decrease with increasing separation distance. Compared to the standard decomposition, the triple decomposition analysis generally predicts relatively lower azimuthal wavenumbers and significantly reduced energy amplification ratios for the optimal disturbances.

Exploring an optimal wavelet-based filter for cryo-ET imaging.

Science.gov (United States)

Huang, Xinrui; Li, Sha; Gao, Song

2018-02-07

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages-low dose and low image contrast-which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.

Non-linear scalable TFETI domain decomposition based contact algorithm

Czech Academy of Sciences Publication Activity Database

Dobiáš, Jiří; Pták, Svatopluk; Dostál, Z.; Vondrák, V.; Kozubek, T.

2010-01-01

Roč. 10, č. 1 (2010), s. 1-10 ISSN 1757-8981. [World Congress on Computational Mechanics/9./. Sydney, 19.07.2010 - 23.07.2010] R&D Projects: GA ČR GA101/08/0574 Institutional research plan: CEZ:AV0Z20760514 Keywords : finite element method * domain decomposition method * contact Subject RIV: BA - General Mathematics http://iopscience.iop.org/1757-899X/10/1/012161/pdf/1757-899X_10_1_012161.pdf

A PARALLEL NONOVERLAPPING DOMAIN DECOMPOSITION METHOD FOR STOKES PROBLEMS

Institute of Scientific and Technical Information of China (English)

Mei-qun Jiang; Pei-liang Dai

2006-01-01

A nonoverlapping domain decomposition iterative procedure is developed and analyzed for generalized Stokes problems and their finite element approximate problems in RN(N=2,3). The method is based on a mixed-type consistency condition with two parameters as a transmission condition together with a derivative-free transmission data updating technique on the artificial interfaces. The method can be applied to a general multi-subdomain decomposition and implemented on parallel machines with local simple communications naturally.

Multidimensional Decomposition of the Sen Index: Some Further Thoughts

OpenAIRE

Stéphane Mussard; Kuan Xu

2006-01-01

Given the multiplicative decomposition of the Sen index into three commonly used poverty statistics – the poverty rate (poverty incidence), poverty gap ratio (poverty depth) and 1 plus the Gini index of poverty gap ratios of the poor (inequality of poverty) – the index becomes much easier to use and to interpret for economists, policy analysts and decision makers. Based on the recent findings on simultaneous subgroup and source decomposition of the Gini index, we examine possible further deco...

Fringe-projection profilometry based on two-dimensional empirical mode decomposition.

Science.gov (United States)

Zheng, Suzhen; Cao, Yiping

2013-11-01

In 3D shape measurement, because deformed fringes often contain low-frequency information degraded with random noise and background intensity information, a new fringe-projection profilometry is proposed based on 2D empirical mode decomposition (2D-EMD). The fringe pattern is first decomposed into numbers of intrinsic mode functions by 2D-EMD. Because the method has partial noise reduction, the background components can be removed to obtain the fundamental components needed to perform Hilbert transformation to retrieve the phase information. The 2D-EMD can effectively extract the modulation phase of a single direction fringe and an inclined fringe pattern because it is a full 2D analysis method and considers the relationship between adjacent lines of a fringe patterns. In addition, as the method does not add noise repeatedly, as does ensemble EMD, the data processing time is shortened. Computer simulations and experiments prove the feasibility of this method.

Interactive Reliability-Based Optimal Design

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle; Siemaszko, A.

1994-01-01

Interactive design/optimization of large, complex structural systems is considered. The objective function is assumed to model the expected costs. The constraints are reliability-based and/or related to deterministic code requirements. Solution of this optimization problem is divided in four main...... tasks, namely finite element analyses, sensitivity analyses, reliability analyses and application of an optimization algorithm. In the paper it is shown how these four tasks can be linked effectively and how existing information on design variables, Lagrange multipliers and the Hessian matrix can...

Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine

Energy Technology Data Exchange (ETDEWEB)

Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)

2017-02-15

The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.

Arthropod and decomposition studies at the WIPP site, October-November, 1980

International Nuclear Information System (INIS)

Anon.

1981-01-01

These studies were conducted to address topics related to potential environmental impacts resulting from WIPP construction at the Los Medanos site. Studies include the effect of salt piles on soil microarthropod faunas and soil community respiration, quantities of soil moved from deep in the soil column to the surface by termites and ants, and development and testing of a multiple regression model for predicting decomposition. Soil cores were taken at increasing distances from the twenty-year-old salt dump at the Nash Draw Potash facility and examined for microarthropod content and tested for other biological activity. Although there were some reductions in densities of most taxa at the base of the salt pile and at 100 m from the base, all of the dominant soil taxa from the area were found at the base of the pile. If a substantial adverse impact occurred soon after emplacement of the salt, recovery has been relatively complete. Substantial changes in these baseline population densities during or after construction could be indicators of impact to the ecology of the Los Medanos site. A model was developed to predict rates of decomposition of shinnery oak litter based on evapotranspiration and lignin content. The model was less successful in predicting the decomposition rate of creosotebush litter, as removal of litter by termites produced large variances in apparent decomposition rates

Decomposition of Multi-player Games

Science.gov (United States)

Zhao, Dengji; Schiffel, Stephan; Thielscher, Michael

Research in General Game Playing aims at building systems that learn to play unknown games without human intervention. We contribute to this endeavour by generalising the established technique of decomposition from AI Planning to multi-player games. To this end, we present a method for the automatic decomposition of previously unknown games into independent subgames, and we show how a general game player can exploit a successful decomposition for game tree search.

AUTONOMOUS GAUSSIAN DECOMPOSITION

International Nuclear Information System (INIS)

Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John

2015-01-01

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes

AUTONOMOUS GAUSSIAN DECOMPOSITION

Energy Technology Data Exchange (ETDEWEB)

Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian [Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States); Heiles, Carl [Radio Astronomy Lab, UC Berkeley, 601 Campbell Hall, Berkeley, CA 94720 (United States); Hennebelle, Patrick [Laboratoire AIM, Paris-Saclay, CEA/IRFU/SAp-CNRS-Université Paris Diderot, F-91191 Gif-sur Yvette Cedex (France); Goss, W. M. [National Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville, Socorro, NM 87801 (United States); Dickey, John, E-mail: rlindner@astro.wisc.edu [University of Tasmania, School of Maths and Physics, Private Bag 37, Hobart, TAS 7001 (Australia)

2015-04-15

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.

Efficient morse decompositions of vector fields.

Science.gov (United States)

Chen, Guoning; Mischaikow, Konstantin; Laramee, Robert S; Zhang, Eugene

2008-01-01

Existing topology-based vector field analysis techniques rely on the ability to extract the individual trajectories such as fixed points, periodic orbits, and separatrices that are sensitive to noise and errors introduced by simulation and interpolation. This can make such vector field analysis unsuitable for rigorous interpretations. We advocate the use of Morse decompositions, which are robust with respect to perturbations, to encode the topological structures of a vector field in the form of a directed graph, called a Morse connection graph (MCG). While an MCG exists for every vector field, it need not be unique. Previous techniques for computing MCG's, while fast, are overly conservative and usually results in MCG's that are too coarse to be useful for the applications. To address this issue, we present a new technique for performing Morse decomposition based on the concept of tau-maps, which typically provides finer MCG's than existing techniques. Furthermore, the choice of tau provides a natural tradeoff between the fineness of the MCG's and the computational costs. We provide efficient implementations of Morse decomposition based on tau-maps, which include the use of forward and backward mapping techniques and an adaptive approach in constructing better approximations of the images of the triangles in the meshes used for simulation.. Furthermore, we propose the use of spatial tau-maps in addition to the original temporal tau-maps. These techniques provide additional trade-offs between the quality of the MCGs and the speed of computation. We demonstrate the utility of our technique with various examples in the plane and on surfaces including engine simulation data sets.

Identifying key nodes in multilayer networks based on tensor decomposition.

Science.gov (United States)

Wang, Dingjie; Wang, Haitao; Zou, Xiufen

2017-06-01

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

AN IMPROVED INTERFEROMETRIC CALIBRATION METHOD BASED ON INDEPENDENT PARAMETER DECOMPOSITION

Directory of Open Access Journals (Sweden)

J. Fan

2018-04-01

Full Text Available Interferometric SAR is sensitive to earth surface undulation. The accuracy of interferometric parameters plays a significant role in precise digital elevation model (DEM. The interferometric calibration is to obtain high-precision global DEM by calculating the interferometric parameters using ground control points (GCPs. However, interferometric parameters are always calculated jointly, making them difficult to decompose precisely. In this paper, we propose an interferometric calibration method based on independent parameter decomposition (IPD. Firstly, the parameters related to the interferometric SAR measurement are determined based on the three-dimensional reconstruction model. Secondly, the sensitivity of interferometric parameters is quantitatively analyzed after the geometric parameters are completely decomposed. Finally, each interferometric parameter is calculated based on IPD and interferometric calibration model is established. We take Weinan of Shanxi province as an example and choose 4 TerraDEM-X image pairs to carry out interferometric calibration experiment. The results show that the elevation accuracy of all SAR images is better than 2.54 m after interferometric calibration. Furthermore, the proposed method can obtain the accuracy of DEM products better than 2.43 m in the flat area and 6.97 m in the mountainous area, which can prove the correctness and effectiveness of the proposed IPD based interferometric calibration method. The results provide a technical basis for topographic mapping of 1 : 50000 and even larger scale in the flat area and mountainous area.

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Energy Technology Data Exchange (ETDEWEB)

Harms, Joseph; Wang, Tonghe; Petrongolo, Michael; Zhu, Lei, E-mail: leizhu@gatech.edu [Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Niu, Tianye [Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (China); Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, 310016 (China)

2016-05-15

Purpose: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). Methods: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high- and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. Results: On the line-pair slice of the Catphan{sup ©}600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise

Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

International Nuclear Information System (INIS)

Han, G.; Lin, B.; Xu, Z.

2017-01-01

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.

Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

Science.gov (United States)

Han, G.; Lin, B.; Xu, Z.

2017-03-01

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.

Enzyme activities at different stages of plant biomass decomposition in three species of fungus-growing termites

DEFF Research Database (Denmark)

da Costa, Rafael R.; Hu, Haofu; Pilgaard, Bo

2018-01-01

contributing to the success of the termites as the main plant decomposers in the Old World. Here we evaluate which plant polymers are decomposed and which enzymes are active during the decomposition process in two major genera of fungus-growing termites. We find a diversity of active enzymes at different...... stages of decomposition and a consistent decrease in plant components during the decomposition process. Furthermore, our findings are consistent with the hypothesis that termites transport enzymes from the older mature parts of the fungus comb through young worker guts to freshly inoculated plant...... substrate. However, preliminary fungal RNAseq analyses suggest that this likely transport is supplemented with enzymes produced in situ Our findings support that the maintenance of an external fungus comb, inoculated with an optimal mix of plant material, fungal spores, and enzymes, is likely the key...

Identification of Successive ``Unobservable'' Cyber Data Attacks in Power Systems Through Matrix Decomposition

Science.gov (United States)

Gao, Pengzhi; Wang, Meng; Chow, Joe H.; Ghiocel, Scott G.; Fardanesh, Bruce; Stefopoulos, George; Razanousky, Michael P.

2016-11-01

This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting "unobservable" cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. We propose a convex-optimization-based method and provide its theoretical guarantee in the data identification. Numerical experiments on actual PMU data from the Central New York power system and synthetic data are conducted to verify the effectiveness of the proposed method.

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

Science.gov (United States)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

Bivariate empirical mode decomposition for ECG-based biometric identification with emotional data.

Science.gov (United States)

Ferdinando, Hany; Seppanen, Tapio; Alasaarela, Esko

2017-07-01

Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.

Theory and Algorithms for Global/Local Design Optimization

National Research Council Canada - National Science Library

Haftka, Raphael T

2004-01-01

... the component and overall design as well as on exploration of global optimization algorithms. In the former category, heuristic decomposition was followed with proof that it solves the original problem...

Torque decomposition and control in an iron core linear permanent magnet motor.

NARCIS (Netherlands)

Overboom, T.T.; Smeets, J.P.C.; Stassen, J.M.; Jansen, J.W.; Lomonova, E.

2012-01-01

Abstract—This paper concerns the decomposition and control of the torque produced by an iron core linear permanent magnet motor. The proposed method is based on the dq0-decomposition of the three-phase currents using Park’s transformation. The torque is decomposed into a reluctance component and two

Gas Sensing Analysis of Ag-Decorated Graphene for Sulfur Hexafluoride Decomposition Products Based on the Density Functional Theory

Directory of Open Access Journals (Sweden)

Xiaoxing Zhang

2016-11-01

Full Text Available Detection of decomposition products of sulfur hexafluoride (SF6 is one of the best ways to diagnose early latent insulation faults in gas-insulated equipment, and the occurrence of sudden accidents can be avoided effectively by finding early latent faults. Recently, functionalized graphene, a kind of gas sensing material, has been reported to show good application prospects in the gas sensor field. Therefore, calculations were performed to analyze the gas sensing properties of intrinsic graphene (Int-graphene and functionalized graphene-based material, Ag-decorated graphene (Ag-graphene, for decomposition products of SF6, including SO2F2, SOF2, and SO2, based on density functional theory (DFT. We thoroughly investigated a series of parameters presenting gas-sensing properties of adsorbing process about gas molecule (SO2F2, SOF2, SO2 and double gas molecules (2SO2F2, 2SOF2, 2SO2 on Ag-graphene, including adsorption energy, net charge transfer, electronic state density, and the highest and lowest unoccupied molecular orbital. The results showed that the Ag atom significantly enhances the electrochemical reactivity of graphene, reflected in the change of conductivity during the adsorption process. SO2F2 and SO2 gas molecules on Ag-graphene presented chemisorption, and the adsorption strength was SO2F2 > SO2, while SOF2 absorption on Ag-graphene was physical adsorption. Thus, we concluded that Ag-graphene showed good selectivity and high sensitivity to SO2F2. The results can provide a helpful guide in exploring Ag-graphene material in experiments for monitoring the insulation status of SF6-insulated equipment based on detecting decomposition products of SF6.

Effective and efficient FPGA synthesis through general functional decomposition

NARCIS (Netherlands)

Jozwiak, L.; Slusarczyk, A.S.; Chojnacki, A.

2003-01-01

In this paper, a new information-driven circuit synthesis method is discussed that targets LUT-based FPGAs and FPGA-based reconfigurable system-on-a-chip platforms. The method is based on the bottom–up general functional decomposition and theory of information relationship measures that we

Decomposition of diesel oil by various microorganisms

Energy Technology Data Exchange (ETDEWEB)

Suess, A; Netzsch-Lehner, A

1969-01-01

Previous experiments demonstrated the decomposition of diesel oil in different soils. In this experiment the decomposition of /sup 14/C-n-Hexadecane labelled diesel oil by special microorganisms was studied. The results were as follows: (1) In the experimental soils the microorganisms Mycoccus ruber, Mycobacterium luteum and Trichoderma hamatum are responsible for the diesel oil decomposition. (2) By adding microorganisms to the soil an increase of the decomposition rate was found only in the beginning of the experiments. (3) Maximum decomposition of diesel oil was reached 2-3 weeks after incubation.

CFD-based optimization in plastics extrusion

Science.gov (United States)

Eusterholz, Sebastian; Elgeti, Stefanie

2018-05-01

This paper presents novel ideas in numerical design of mixing elements in single-screw extruders. The actual design process is reformulated as a shape optimization problem, given some functional, but possibly inefficient initial design. Thereby automatic optimization can be incorporated and the design process is advanced, beyond the simulation-supported, but still experience-based approach. This paper proposes concepts to extend a method which has been developed and validated for die design to the design of mixing-elements. For simplicity, it focuses on single-phase flows only. The developed method conducts forward-simulations to predict the quasi-steady melt behavior in the relevant part of the extruder. The result of each simulation is used in a black-box optimization procedure based on an efficient low-order parameterization of the geometry. To minimize user interaction, an objective function is formulated that quantifies the products' quality based on the forward simulation. This paper covers two aspects: (1) It reviews the set-up of the optimization framework as discussed in [1], and (2) it details the necessary extensions for the optimization of mixing elements in single-screw extruders. It concludes with a presentation of first advances in the unsteady flow simulation of a metering and mixing section with the SSMUM [2] using the Carreau material model.

Optical colour image watermarking based on phase-truncated linear canonical transform and image decomposition

Science.gov (United States)

Su, Yonggang; Tang, Chen; Li, Biyuan; Lei, Zhenkun

2018-05-01

This paper presents a novel optical colour image watermarking scheme based on phase-truncated linear canonical transform (PT-LCT) and image decomposition (ID). In this proposed scheme, a PT-LCT-based asymmetric cryptography is designed to encode the colour watermark into a noise-like pattern, and an ID-based multilevel embedding method is constructed to embed the encoded colour watermark into a colour host image. The PT-LCT-based asymmetric cryptography, which can be optically implemented by double random phase encoding with a quadratic phase system, can provide a higher security to resist various common cryptographic attacks. And the ID-based multilevel embedding method, which can be digitally implemented by a computer, can make the information of the colour watermark disperse better in the colour host image. The proposed colour image watermarking scheme possesses high security and can achieve a higher robustness while preserving the watermark’s invisibility. The good performance of the proposed scheme has been demonstrated by extensive experiments and comparison with other relevant schemes.

Canonical decomposition of magnetotelluric responses: Experiment on 1D anisotropic structures

Science.gov (United States)

Guo, Ze-qiu; Wei, Wen-bo; Ye, Gao-feng; Jin, Sheng; Jing, Jian-en

2015-08-01

Horizontal electrical heterogeneity of subsurface earth is mostly originated from structural complexity and electrical anisotropy, and local near-surface electrical heterogeneity will severely distort regional electromagnetic responses. Conventional distortion analyses for magnetotelluric soundings are primarily physical decomposition methods with respect to isotropic models, which mostly presume that the geoelectric distribution of geological structures is of local and regional patterns represented by 3D/2D models. Due to the widespread anisotropy of earth media, the confusion between 1D anisotropic responses and 2D isotropic responses, and the defects of physical decomposition methods, we propose to conduct modeling experiments with canonical decomposition in terms of 1D layered anisotropic models, and the method is one of the mathematical decomposition methods based on eigenstate analyses differentiated from distortion analyses, which can be used to recover electrical information such as strike directions, and maximum and minimum conductivity. We tested this method with numerical simulation experiments on several 1D synthetic models, which turned out that canonical decomposition is quite effective to reveal geological anisotropic information. Finally, for the background of anisotropy from previous study by geological and seismological methods, canonical decomposition is applied to real data acquired in North China Craton for 1D anisotropy analyses, and the result shows that, with effective modeling and cautious interpretation, canonical decomposition could be another good method to detect anisotropy of geological media.

Protection from wintertime rainfall reduces nutrient losses and greenhouse gas emissions during the decomposition of poultry and horse manure-based amendments.

Science.gov (United States)

Maltais-Landry, Gabriel; Neufeld, Katarina; Poon, David; Grant, Nicholas; Nesic, Zoran; Smukler, Sean

2018-04-01

Manure-based soil amendments (herein "amendments") are important fertility sources, but differences among amendment types and management can significantly affect their nutrient value and environmental impacts. A 6-month in situ decomposition experiment was conducted to determine how protection from wintertime rainfall affected nutrient losses and greenhouse gas (GHG) emissions in poultry (broiler chicken and turkey) and horse amendments. Changes in total nutrient concentration were measured every 3 months, changes in ammonium (NH 4 + ) and nitrate (NO 3 - ) concentrations every month, and GHG emissions of carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) every 7-14 days. Poultry amendments maintained higher nutrient concentrations (except for K), higher emissions of CO 2 and N 2 O, and lower CH 4 emissions than horse amendments. Exposing amendments to rainfall increased total N and NH 4 + losses in poultry amendments, P losses in turkey and horse amendments, and K losses and cumulative N 2 O emissions for all amendments. However, it did not affect CO 2 or CH 4 emissions. Overall, rainfall exposure would decrease total N inputs by 37% (horse), 59% (broiler chicken), or 74% (turkey) for a given application rate (wet weight basis) after 6 months of decomposition, with similar losses for NH 4 + (69-96%), P (41-73%), and K (91-97%). This study confirms the benefits of facilities protected from rainfall to reduce nutrient losses and GHG emissions during amendment decomposition. The impact of rainfall protection on nutrient losses and GHG emissions was monitored during the decomposition of broiler chicken, turkey, and horse manure-based soil amendments. Amendments exposed to rainfall had large ammonium and potassium losses, resulting in a 37-74% decrease in N inputs when compared with amendments protected from rainfall. Nitrous oxide emissions were also higher with rainfall exposure, although it had no effect on carbon dioxide and methane emissions

Multilinear operators for higher-order decompositions.

Energy Technology Data Exchange (ETDEWEB)

Kolda, Tamara Gibson

2006-04-01

We propose two new multilinear operators for expressing the matrix compositions that are needed in the Tucker and PARAFAC (CANDECOMP) decompositions. The first operator, which we call the Tucker operator, is shorthand for performing an n-mode matrix multiplication for every mode of a given tensor and can be employed to concisely express the Tucker decomposition. The second operator, which we call the Kruskal operator, is shorthand for the sum of the outer-products of the columns of N matrices and allows a divorce from a matricized representation and a very concise expression of the PARAFAC decomposition. We explore the properties of the Tucker and Kruskal operators independently of the related decompositions. Additionally, we provide a review of the matrix and tensor operations that are frequently used in the context of tensor decompositions.

Low-rank canonical-tensor decomposition of potential energy surfaces: application to grid-based diagrammatic vibrational Green's function theory

Science.gov (United States)

Rai, Prashant; Sargsyan, Khachik; Najm, Habib; Hermes, Matthew R.; Hirata, So

2017-09-01

A new method is proposed for a fast evaluation of high-dimensional integrals of potential energy surfaces (PES) that arise in many areas of quantum dynamics. It decomposes a PES into a canonical low-rank tensor format, reducing its integral into a relatively short sum of products of low-dimensional integrals. The decomposition is achieved by the alternating least squares (ALS) algorithm, requiring only a small number of single-point energy evaluations. Therefore, it eradicates a force-constant evaluation as the hotspot of many quantum dynamics simulations and also possibly lifts the curse of dimensionality. This general method is applied to the anharmonic vibrational zero-point and transition energy calculations of molecules using the second-order diagrammatic vibrational many-body Green's function (XVH2) theory with a harmonic-approximation reference. In this application, high dimensional PES and Green's functions are both subjected to a low-rank decomposition. Evaluating the molecular integrals over a low-rank PES and Green's functions as sums of low-dimensional integrals using the Gauss-Hermite quadrature, this canonical-tensor-decomposition-based XVH2 (CT-XVH2) achieves an accuracy of 0.1 cm-1 or higher and nearly an order of magnitude speedup as compared with the original algorithm using force constants for water and formaldehyde.

Reliability Based Optimization of Fire Protection

DEFF Research Database (Denmark)

Thoft-Christensen, Palle

fire protection (PFP) of firewalls and structural members. The paper is partly based on research performed within the EU supported research project B/E-4359 "Optimized Fire Safety of Offshore Structures" and partly on research supported by the Danish Technical Research Council (see Thoft-Christensen [1......]). Special emphasis is put on the optimization software developed within the project.......It is well known that fire is one of the major risks of serious damage or total loss of several types of structures such as nuclear installations, buildings, offshore platforms/topsides etc. This paper presents a methodology and software for reliability based optimization of the layout of passive...

Optimal design of RTCs in digital circuit fault self-repair based on global signal optimization

Institute of Scientific and Technical Information of China (English)

Zhang Junbin; Cai Jinyan; Meng Yafeng

2016-01-01

Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in elec-tronic systems in complicated environments. If immediate field repairs are not made on the faults, elec-tronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of (evolvable hardware)-based and (reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits (RTCs) in electronic circuit fault self-repair based on global sig-nal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the elec-tronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.

Canonical Polyadic Decomposition With Auxiliary Information for Brain-Computer Interface.

Science.gov (United States)

Li, Junhua; Li, Chao; Cichocki, Andrzej

2017-01-01

Physiological signals are often organized in the form of multiple dimensions (e.g., channel, time, task, and 3-D voxel), so it is better to preserve original organization structure when processing. Unlike vector-based methods that destroy data structure, canonical polyadic decomposition (CPD) aims to process physiological signals in the form of multiway array, which considers relationships between dimensions and preserves structure information contained by the physiological signal. Nowadays, CPD is utilized as an unsupervised method for feature extraction in a classification problem. After that, a classifier, such as support vector machine, is required to classify those features. In this manner, classification task is achieved in two isolated steps. We proposed supervised CPD by directly incorporating auxiliary label information during decomposition, by which a classification task can be achieved without an extra step of classifier training. The proposed method merges the decomposition and classifier learning together, so it reduces procedure of classification task compared with that of respective decomposition and classification. In order to evaluate the performance of the proposed method, three different kinds of signals, synthetic signal, EEG signal, and MEG signal, were used. The results based on evaluations of synthetic and real signals demonstrated that the proposed method is effective and efficient.

Decomposition of tetrachloroethylene by ionizing radiation

International Nuclear Information System (INIS)

Hakoda, T.; Hirota, K.; Hashimoto, S.

1998-01-01

Decomposition of tetrachloroethylene and other chloroethenes by ionizing radiation were examined to get information on treatment of industrial off-gas. Model gases, airs containing chloroethenes, were confined in batch reactors and irradiated with electron beam and gamma ray. The G-values of decomposition were larger in the order of tetrachloro- > trichloro- > trans-dichloro- > cis-dichloro- > monochloroethylene in electron beam irradiation and tetrachloro-, trichloro-, trans-dichloro- > cis-dichloro- > monochloroethylene in gamma ray irradiation. For tetrachloro-, trichloro- and trans-dichloroethylene, G-values of decomposition in EB irradiation increased with increase of chlorine atom in a molecule, while those in gamma ray irradiation were almost kept constant. The G-value of decomposition for tetrachloroethylene in EB irradiation was the largest of those for all chloroethenes. In order to examine the effect of the initial concentration on G-value of decomposition, airs containing 300 to 1,800 ppm of tetrachloroethylene were irradiated with electron beam and gamma ray. The G-values of decomposition in both irradiation increased with the initial concentration. Those in electron beam irradiation were two times larger than those in gamma ray irradiation

Identification method for gas-liquid two-phase flow regime based on singular value decomposition and least square support vector machine

International Nuclear Information System (INIS)

Sun Bin; Zhou Yunlong; Zhao Peng; Guan Yuebo

2007-01-01

Aiming at the non-stationary characteristics of differential pressure fluctuation signals of gas-liquid two-phase flow, and the slow convergence of learning and liability of dropping into local minima for BP neural networks, flow regime identification method based on Singular Value Decomposition (SVD) and Least Square Support Vector Machine (LS-SVM) is presented. First of all, the Empirical Mode Decomposition (EMD) method is used to decompose the differential pressure fluctuation signals of gas-liquid two-phase flow into a number of stationary Intrinsic Mode Functions (IMFs) components from which the initial feature vector matrix is formed. By applying the singular vale decomposition technique to the initial feature vector matrixes, the singular values are obtained. Finally, the singular values serve as the flow regime characteristic vector to be LS-SVM classifier and flow regimes are identified by the output of the classifier. The identification result of four typical flow regimes of air-water two-phase flow in horizontal pipe has shown that this method achieves a higher identification rate. (authors)

An optimal FFT-based anisotropic power spectrum estimator

Energy Technology Data Exchange (ETDEWEB)

Hand, Nick; Seljak, Uroš [Department of Astronomy, University of California, Berkeley, CA 94720 (United States); Li, Yin; Slepian, Zachary, E-mail: nhand@berkeley.edu, E-mail: yin.li@berkeley.edu, E-mail: zslepian@lbl.gov, E-mail: useljak@berkeley.edu [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)

2017-07-01

Measurements of line-of-sight dependent clustering via the galaxy power spectrum's multipole moments constitute a powerful tool for testing theoretical models in large-scale structure. Recent work shows that this measurement, including a moving line-of-sight, can be accelerated using Fast Fourier Transforms (FFTs) by decomposing the Legendre polynomials into products of Cartesian vectors. Here, we present a faster, optimal means of using FFTs for this measurement. We avoid redundancy present in the Cartesian decomposition by using a spherical harmonic decomposition of the Legendre polynomials. With this method, a given multipole of order ℓ requires only 2ℓ+1 FFTs rather than the (ℓ+1)(ℓ+2)/2 FFTs of the Cartesian approach. For the hexadecapole (ℓ = 4), this translates to 40% fewer FFTs, with increased savings for higher ℓ. The reduction in wall-clock time enables the calculation of finely-binned wedges in P ( k ,μ), obtained by computing multipoles up to a large ℓ{sub max} and combining them. This transformation has a number of advantages. We demonstrate that by using non-uniform bins in μ, we can isolate plane-of-sky (angular) systematics to a narrow bin at 0μ ≅ while eliminating the contamination from all other bins. We also show that the covariance matrix of clustering wedges binned uniformly in μ becomes ill-conditioned when combining multipoles up to large values of ℓ{sub max}, but that the problem can be avoided with non-uniform binning. As an example, we present results using ℓ{sub max}=16, for which our procedure requires a factor of 3.4 fewer FFTs than the Cartesian method, while removing the first μ bin leads only to a 7% increase in statistical error on f σ{sub 8}, as compared to a 54% increase with ℓ{sub max}=4.

An optimal FFT-based anisotropic power spectrum estimator

Science.gov (United States)

Hand, Nick; Li, Yin; Slepian, Zachary; Seljak, Uroš

2017-07-01

Measurements of line-of-sight dependent clustering via the galaxy power spectrum's multipole moments constitute a powerful tool for testing theoretical models in large-scale structure. Recent work shows that this measurement, including a moving line-of-sight, can be accelerated using Fast Fourier Transforms (FFTs) by decomposing the Legendre polynomials into products of Cartesian vectors. Here, we present a faster, optimal means of using FFTs for this measurement. We avoid redundancy present in the Cartesian decomposition by using a spherical harmonic decomposition of the Legendre polynomials. With this method, a given multipole of order l requires only 2l+1 FFTs rather than the (l+1)(l+2)/2 FFTs of the Cartesian approach. For the hexadecapole (l = 4), this translates to 40% fewer FFTs, with increased savings for higher l. The reduction in wall-clock time enables the calculation of finely-binned wedges in P(k,μ), obtained by computing multipoles up to a large lmax and combining them. This transformation has a number of advantages. We demonstrate that by using non-uniform bins in μ, we can isolate plane-of-sky (angular) systematics to a narrow bin at 0μ simeq while eliminating the contamination from all other bins. We also show that the covariance matrix of clustering wedges binned uniformly in μ becomes ill-conditioned when combining multipoles up to large values of lmax, but that the problem can be avoided with non-uniform binning. As an example, we present results using lmax=16, for which our procedure requires a factor of 3.4 fewer FFTs than the Cartesian method, while removing the first μ bin leads only to a 7% increase in statistical error on f σ8, as compared to a 54% increase with lmax=4.

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.

Iterative optimization of performance libraries by hierarchical division of codes

International Nuclear Information System (INIS)

Donadio, S.

2007-09-01

The increasing complexity of hardware features incorporated in modern processors makes high performance code generation very challenging. Library generators such as ATLAS, FFTW and SPIRAL overcome this issue by empirically searching in the space of possible program versions for the one that performs the best. This thesis explores fully automatic solution to adapt a compute-intensive application to the target architecture. By mimicking complex sequences of transformations useful to optimize real codes, we show that generative programming is a practical tool to implement a new hierarchical compilation approach for the generation of high performance code relying on the use of state-of-the-art compilers. As opposed to ATLAS, this approach is not application-dependant but can be applied to fairly generic loop structures. Our approach relies on the decomposition of the original loop nest into simpler kernels. These kernels are much simpler to optimize and furthermore, using such codes makes the performance trade off problem much simpler to express and to solve. Finally, we propose a new approach for the generation of performance libraries based on this decomposition method. We show that our method generates high-performance libraries, in particular for BLAS. (author)

Tensor renormalization group with randomized singular value decomposition

Science.gov (United States)

Morita, Satoshi; Igarashi, Ryo; Zhao, Hui-Hai; Kawashima, Naoki

2018-03-01

An algorithm of the tensor renormalization group is proposed based on a randomized algorithm for singular value decomposition. Our algorithm is applicable to a broad range of two-dimensional classical models. In the case of a square lattice, its computational complexity and memory usage are proportional to the fifth and the third power of the bond dimension, respectively, whereas those of the conventional implementation are of the sixth and the fourth power. The oversampling parameter larger than the bond dimension is sufficient to reproduce the same result as full singular value decomposition even at the critical point of the two-dimensional Ising model.

Decomposition of Sodium Tetraphenylborate

International Nuclear Information System (INIS)

Barnes, M.J.

1998-01-01

The chemical decomposition of aqueous alkaline solutions of sodium tetraphenylborate (NaTPB) has been investigated. The focus of the investigation is on the determination of additives and/or variables which influence NaTBP decomposition. This document describes work aimed at providing better understanding into the relationship of copper (II), solution temperature, and solution pH to NaTPB stability

Long-term litter decomposition controlled by manganese redox cycling.

Science.gov (United States)

Keiluweit, Marco; Nico, Peter; Harmon, Mark E; Mao, Jingdong; Pett-Ridge, Jennifer; Kleber, Markus

2015-09-22

Litter decomposition is a keystone ecosystem process impacting nutrient cycling and productivity, soil properties, and the terrestrial carbon (C) balance, but the factors regulating decomposition rate are still poorly understood. Traditional models assume that the rate is controlled by litter quality, relying on parameters such as lignin content as predictors. However, a strong correlation has been observed between the manganese (Mn) content of litter and decomposition rates across a variety of forest ecosystems. Here, we show that long-term litter decomposition in forest ecosystems is tightly coupled to Mn redox cycling. Over 7 years of litter decomposition, microbial transformation of litter was paralleled by variations in Mn oxidation state and concentration. A detailed chemical imaging analysis of the litter revealed that fungi recruit and redistribute unreactive Mn(2+) provided by fresh plant litter to produce oxidative Mn(3+) species at sites of active decay, with Mn eventually accumulating as insoluble Mn(3+/4+) oxides. Formation of reactive Mn(3+) species coincided with the generation of aromatic oxidation products, providing direct proof of the previously posited role of Mn(3+)-based oxidizers in the breakdown of litter. Our results suggest that the litter-decomposing machinery at our coniferous forest site depends on the ability of plants and microbes to supply, accumulate, and regenerate short-lived Mn(3+) species in the litter layer. This observation indicates that biogeochemical constraints on bioavailability, mobility, and reactivity of Mn in the plant-soil system may have a profound impact on litter decomposition rates.

Thermal decomposition of γ-irradiated lead nitrate

International Nuclear Information System (INIS)

Nair, S.M.K.; Kumar, T.S.S.

1990-01-01

The thermal decomposition of unirradiated and γ-irradiated lead nitrate was studied by the gas evolution method. The decomposition proceeds through initial gas evolution, a short induction period, an acceleratory stage and a decay stage. The acceleratory and decay stages follow the Avrami-Erofeev equation. Irradiation enhances the decomposition but does not affect the shape of the decomposition curve. (author) 10 refs.; 7 figs.; 2 tabs

Decomposing Nekrasov decomposition

International Nuclear Information System (INIS)

Morozov, A.; Zenkevich, Y.

2016-01-01

AGT relations imply that the four-point conformal block admits a decomposition into a sum over pairs of Young diagrams of essentially rational Nekrasov functions — this is immediately seen when conformal block is represented in the form of a matrix model. However, the q-deformation of the same block has a deeper decomposition — into a sum over a quadruple of Young diagrams of a product of four topological vertices. We analyze the interplay between these two decompositions, their properties and their generalization to multi-point conformal blocks. In the latter case we explain how Dotsenko-Fateev all-with-all (star) pair “interaction” is reduced to the quiver model nearest-neighbor (chain) one. We give new identities for q-Selberg averages of pairs of generalized Macdonald polynomials. We also translate the slicing invariance of refined topological strings into the language of conformal blocks and interpret it as abelianization of generalized Macdonald polynomials.

Decomposing Nekrasov decomposition

Energy Technology Data Exchange (ETDEWEB)

Morozov, A. [ITEP,25 Bolshaya Cheremushkinskaya, Moscow, 117218 (Russian Federation); Institute for Information Transmission Problems,19-1 Bolshoy Karetniy, Moscow, 127051 (Russian Federation); National Research Nuclear University MEPhI,31 Kashirskoe highway, Moscow, 115409 (Russian Federation); Zenkevich, Y. [ITEP,25 Bolshaya Cheremushkinskaya, Moscow, 117218 (Russian Federation); National Research Nuclear University MEPhI,31 Kashirskoe highway, Moscow, 115409 (Russian Federation); Institute for Nuclear Research of Russian Academy of Sciences,6a Prospekt 60-letiya Oktyabrya, Moscow, 117312 (Russian Federation)

2016-02-16

AGT relations imply that the four-point conformal block admits a decomposition into a sum over pairs of Young diagrams of essentially rational Nekrasov functions — this is immediately seen when conformal block is represented in the form of a matrix model. However, the q-deformation of the same block has a deeper decomposition — into a sum over a quadruple of Young diagrams of a product of four topological vertices. We analyze the interplay between these two decompositions, their properties and their generalization to multi-point conformal blocks. In the latter case we explain how Dotsenko-Fateev all-with-all (star) pair “interaction” is reduced to the quiver model nearest-neighbor (chain) one. We give new identities for q-Selberg averages of pairs of generalized Macdonald polynomials. We also translate the slicing invariance of refined topological strings into the language of conformal blocks and interpret it as abelianization of generalized Macdonald polynomials.

Freeman-Durden Decomposition with Oriented Dihedral Scattering

Directory of Open Access Journals (Sweden)

Yan Jian

2014-10-01

Full Text Available In this paper, when the azimuth direction of polarimetric Synthetic Aperature Radars (SAR differs from the planting direction of crops, the double bounce of the incident electromagnetic waves from the terrain surface to the growing crops is investigated and compared with the normal double bounce. Oriented dihedral scattering model is developed to explain the investigated double bounce and is introduced into the Freeman-Durden decomposition. The decomposition algorithm corresponding to the improved decomposition is then proposed. The airborne polarimetric SAR data for agricultural land covering two flight tracks are chosen to validate the algorithm; the decomposition results show that for agricultural vegetated land, the improved Freeman-Durden decomposition has the advantage of increasing the decomposition coherency among the polarimetric SAR data along the different flight tracks.

Electrolytic decomposition of N-nitrosodimethylamine in water

Energy Technology Data Exchange (ETDEWEB)

Volchek, K; Ladanowski, C; Somers, A; Whittaker, H [Environment Canada, Edmonton, AB (Canada). Emergencies Science Div.; Anantaraman, A [Ottawa Univ., ON (Canada). Electrochemical Science and Technology Centre

1996-12-31

The feasibility of electrically reducing N-nitrosodimethylamine (NDMA) in aqueous solutions was studied in a series of bench-scale experiments. The presence of nitrosamines in soil and groundwater is largely associated with missile fuels, but also with pesticides and other chemicals. Inexpensive carbon, stainless steel and nickel electrodes were used to perform the experiments. The electrodes, voltage and solution pH were the variables studied. Results showed that a higher rate of decomposition of NDMA occurred in acidic conditions using a relatively high potential applied to the electrodes. Further studies were suggested to optimize treatment conditions and evaluate the technical and economical feasibility of the process. 9 refs., 2 tabs., 2 figs.

Influence of nitrogen dioxide on the thermal decomposition of ammonium nitrate

Directory of Open Access Journals (Sweden)

Igor L. Kovalenko

2015-06-01

Full Text Available In this paper results of experimental studies of ammonium nitrate thermal decomposition in an open system under normal conditions and in NO2 atmosphere are presented. It is shown that nitrogen dioxide is the initiator of ammonium nitrate self-accelerating exothermic cyclic decomposition process. The insertion of NO2 from outside under the conditions of nonisothermal experiment reduces the characteristic temperature of the beginning of self-accelerating decomposition by 50...70 °C. Using method of isothermal exposures it is proved that thermal decomposition of ammonium nitrate in nitrogen dioxide atmosphere at 210 °C is autocatalytic (zero-order reaction. It was suggested that there is possibility of increasing the sensitivity and detonation characteristics of energy condensed systems based on ammonium nitrate by the insertion of additives which provide an earlier appearance of NO2 in the system.

Interacting effects of insects and flooding on wood decomposition.

Directory of Open Access Journals (Sweden)

Michael D Ulyshen

Full Text Available Saproxylic arthropods are thought to play an important role in wood decomposition but very few efforts have been made to quantify their contributions to the process and the factors controlling their activities are not well understood. In the current study, mesh exclusion bags were used to quantify how arthropods affect loblolly pine (Pinus taeda L. decomposition rates in both seasonally flooded and unflooded forests over a 31-month period in the southeastern United States. Wood specific gravity (based on initial wood volume was significantly lower in bolts placed in unflooded forests and for those unprotected from insects. Approximately 20.5% and 13.7% of specific gravity loss after 31 months was attributable to insect activity in flooded and unflooded forests, respectively. Importantly, minimal between-treatment differences in water content and the results from a novel test carried out separately suggest the mesh bags had no significant impact on wood mass loss beyond the exclusion of insects. Subterranean termites (Isoptera: Rhinotermitidae: Reticulitermes spp. were 5-6 times more active below-ground in unflooded forests compared to flooded forests based on wooden monitoring stakes. They were also slightly more active above-ground in unflooded forests but these differences were not statistically significant. Similarly, seasonal flooding had no detectable effect on above-ground beetle (Coleoptera richness or abundance. Although seasonal flooding strongly reduced Reticulitermes activity below-ground, it can be concluded from an insignificant interaction between forest type and exclusion treatment that reduced above-ground decomposition rates in seasonally flooded forests were due largely to suppressed microbial activity at those locations. The findings from this study indicate that southeastern U.S. arthropod communities accelerate above-ground wood decomposition significantly and to a similar extent in both flooded and unflooded forests

Double Bounce Component in Cross-Polarimetric SAR from a New Scattering Target Decomposition

Science.gov (United States)

Hong, Sang-Hoon; Wdowinski, Shimon

2013-08-01

Common vegetation scattering theories assume that the Synthetic Aperture Radar (SAR) cross-polarization (cross-pol) signal represents solely volume scattering. We found this assumption incorrect based on SAR phase measurements acquired over the south Florida Everglades wetlands indicating that the cross-pol radar signal often samples the water surface beneath the vegetation. Based on these new observations, we propose that the cross-pol measurement consists of both volume scattering and double bounce components. The simplest multi-bounce scattering mechanism that generates cross-pol signal occurs by rotated dihedrals. Thus, we use the rotated dihedral mechanism with probability density function to revise some of the vegetation scattering theories and develop a three- component decomposition algorithm with single bounce, double bounce from both co-pol and cross-pol, and volume scattering components. We applied the new decomposition analysis to both urban and rural environments using Radarsat-2 quad-pol datasets. The decomposition of the San Francisco's urban area shows higher double bounce scattering and reduced volume scattering compared to other common three-component decomposition. The decomposition of the rural Everglades area shows that the relations between volume and cross-pol double bounce depend on the vegetation density. The new decomposition can be useful to better understand vegetation scattering behavior over the various surfaces and the estimation of above ground biomass using SAR observations.

Do soil organisms affect aboveground litter decomposition in the semiarid Patagonian steppe, Argentina?

Science.gov (United States)

Araujo, Patricia I; Yahdjian, Laura; Austin, Amy T

2012-01-01

Surface litter decomposition in arid and semiarid ecosystems is often faster than predicted by climatic parameters such as annual precipitation or evapotranspiration, or based on standard indices of litter quality such as lignin or nitrogen concentrations. Abiotic photodegradation has been demonstrated to be an important factor controlling aboveground litter decomposition in aridland ecosystems, but soil fauna, particularly macrofauna such as termites and ants, have also been identified as key players affecting litter mass loss in warm deserts. Our objective was to quantify the importance of soil organisms on surface litter decomposition in the Patagonian steppe in the absence of photodegradative effects, to establish the relative importance of soil organisms on rates of mass loss and nitrogen release. We estimated the relative contribution of soil fauna and microbes to litter decomposition of a dominant grass using litterboxes with variable mesh sizes that excluded groups of soil fauna based on size class (10, 2, and 0.01 mm), which were placed beneath shrub canopies. We also employed chemical repellents (naphthalene and fungicide). The exclusion of macro- and mesofauna had no effect on litter mass loss over 3 years (P = 0.36), as litter decomposition was similar in all soil fauna exclusions and naphthalene-treated litter. In contrast, reduction of fungal activity significantly inhibited litter decomposition (P soil fauna have been mentioned as a key control of litter decomposition in warm deserts, biogeographic legacies and temperature limitation may constrain the importance of these organisms in temperate aridlands, particularly in the southern hemisphere.

Chatter identification in milling of Inconel 625 based on recurrence plot technique and Hilbert vibration decomposition

Directory of Open Access Journals (Sweden)

Lajmert Paweł

2018-01-01

Full Text Available In the paper a cutting stability in the milling process of nickel based alloy Inconel 625 is analysed. This problem is often considered theoretically, but the theoretical finding do not always agree with experimental results. For this reason, the paper presents different methods for instability identification during real machining process. A stability lobe diagram is created based on data obtained in impact test of an end mill. Next, the cutting tests were conducted in which the axial cutting depth of cut was gradually increased in order to find a stability limit. Finally, based on the cutting force measurements the stability estimation problem is investigated using the recurrence plot technique and Hilbert vibration decomposition method.

LMDI decomposition approach: A guide for implementation

International Nuclear Information System (INIS)

Ang, B.W.

2015-01-01

Since it was first used by researchers to analyze industrial electricity consumption in the early 1980s, index decomposition analysis (IDA) has been widely adopted in energy and emission studies. Lately its use as the analytical component of accounting frameworks for tracking economy-wide energy efficiency trends has attracted considerable attention and interest among policy makers. The last comprehensive literature review of IDA was reported in 2000 which is some years back. After giving an update and presenting the key trends in the last 15 years, this study focuses on the implementation issues of the logarithmic mean Divisia index (LMDI) decomposition methods in view of their dominance in IDA in recent years. Eight LMDI models are presented and their origin, decomposition formulae, and strengths and weaknesses are summarized. Guidelines on the choice among these models are provided to assist users in implementation. - Highlights: • Guidelines for implementing LMDI decomposition approach are provided. • Eight LMDI decomposition models are summarized and compared. • The development of the LMDI decomposition approach is presented. • The latest developments of index decomposition analysis are briefly reviewed.

Massively Parallel Polar Decomposition on Distributed-Memory Systems

KAUST Repository

Ltaief, Hatem; Sukkari, Dalal E.; Esposito, Aniello; Nakatsukasa, Yuji; Keyes, David E.

2018-01-01

We present a high-performance implementation of the Polar Decomposition (PD) on distributed-memory systems. Building upon on the QR-based Dynamically Weighted Halley (QDWH) algorithm, the key idea lies in finding the best rational approximation

Task decomposition for multilimbed robots to work in the reachable-but-unorientable space

Science.gov (United States)

Su, Chao; Zheng, Yuan F.

1990-01-01

Multilimbed industrial robots that have at least one arm and two or more legs are suggested for enlarging robot workspace in industrial automation. To plan the motion of a multilimbed robot, the arm-leg motion-coordination problem is raised and task decomposition is proposed to solve the problem; that is, a given task described by the destination position and orientation of the end-effector is decomposed into subtasks for arm manipulation and for leg locomotion, respectively. The former is defined as the end-effector position and orientation with respect to the legged main body, and the latter as the main-body position and orientation in the world coordinates. Three approaches are proposed for the task decomposition. The approaches are further evaluated in terms of energy consumption, from which an optimal approach can be selected.

Reliability Based Optimization of Structural Systems

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

1987-01-01

The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability...... is estimated using first. order reliability methods ( FORM ). The design problem is formulated as the optimization problem to minimize a given cost function such that the reliability of the single elements satisfies given requirements or such that the systems reliability satisfies a given requirement....... For these optimization problems it is described how a sensitivity analysis can be performed. Next, new optimization procedures to solve the optimization problems are presented. Two of these procedures solve the system reliability based optimization problem sequentially using quasi-analytical derivatives. Finally...

Application of the whole powder pattern decomposition procedure in the residual stress analysis of layers and coatings

International Nuclear Information System (INIS)

Schoderböck, Peter; Brechbühl, Jens

2015-01-01

The X-ray investigation of stress states in materials, based on the determination of elastic lattice strains which are converted to stresses by means of theory of elasticity, is a necessity in quality control of thin layers and coatings for optimizing manufacturing steps and process parameters. This work introduces the evaluation of residual stress from complex and overlapping diffraction patterns using a whole-powder pattern decomposition procedure defining a 2θ-offset caused by residual stresses. Furthermore corrections for sample displacement and refraction are directly implemented in the calculation procedure. The correlation matrices of the least square fitting routines have been analyzed for parameter interactions and obvious interdependencies have been decoupled by the introduction of an internal standard within the diffraction experiment. This decomposition based evaluation has been developed on tungsten as a model material system and its efficiency was demonstrated by X-ray diffraction analysis of a solid oxide fuel cell multilayer system. The results are compared with those obtained by the classical sin 2 Ψ-method. - Highlights: • Analysis of complex multiphase diffraction patterns with respect to residual stress • Stress-gradient determination with in situ correction of displacement and refraction • Consideration of the elastic anisotropy within the refinement

Robust optimization-based DC optimal power flow for managing wind generation uncertainty

Science.gov (United States)

Boonchuay, Chanwit; Tomsovic, Kevin; Li, Fangxing; Ongsakul, Weerakorn

2012-11-01

Integrating wind generation into the wider grid causes a number of challenges to traditional power system operation. Given the relatively large wind forecast errors, congestion management tools based on optimal power flow (OPF) need to be improved. In this paper, a robust optimization (RO)-based DCOPF is proposed to determine the optimal generation dispatch and locational marginal prices (LMPs) for a day-ahead competitive electricity market considering the risk of dispatch cost variation. The basic concept is to use the dispatch to hedge against the possibility of reduced or increased wind generation. The proposed RO-based DCOPF is compared with a stochastic non-linear programming (SNP) approach on a modified PJM 5-bus system. Primary test results show that the proposed DCOPF model can provide lower dispatch cost than the SNP approach.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

Science.gov (United States)

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

Directory of Open Access Journals (Sweden)

Xiang-ming Gao

2017-01-01

Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

FDG decomposition products

International Nuclear Information System (INIS)

Macasek, F.; Buriova, E.

2004-01-01

In this presentation authors present the results of analysis of decomposition products of [ 18 ]fluorodexyglucose. It is concluded that the coupling of liquid chromatography - mass spectrometry with electrospray ionisation is a suitable tool for quantitative analysis of FDG radiopharmaceutical, i.e. assay of basic components (FDG, glucose), impurities (Kryptofix) and decomposition products (gluconic and glucuronic acids etc.); 2-[ 18 F]fluoro-deoxyglucose (FDG) is sufficiently stable and resistant towards autoradiolysis; the content of radiochemical impurities (2-[ 18 F]fluoro-gluconic and 2-[ 18 F]fluoro-glucuronic acids in expired FDG did not exceed 1%

Integrated Reliability-Based Optimal Design of Structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

1987-01-01

In conventional optimal design of structural systems the weight or the initial cost of the structure is usually used as objective function. Further, the constraints require that the stresses and/or strains at some critical points have to be less than some given values. Finally, all variables......-based optimal design is discussed. Next, an optimal inspection and repair strategy for existing structural systems is presented. An optimization problem is formulated , where the objective is to minimize the expected total future cost of inspection and repair subject to the constraint that the reliability...... value. The reliability can be measured from an element and/or a systems point of view. A number of methods to solve reliability-based optimization problems has been suggested, see e.g. Frangopol [I]. Murotsu et al. (2], Thoft-Christensen & Sørensen (3] and Sørensen (4). For structures where...

Canonical polyadic decomposition of third-order semi-nonnegative semi-symmetric tensors using LU and QR matrix factorizations

Science.gov (United States)

Wang, Lu; Albera, Laurent; Kachenoura, Amar; Shu, Huazhong; Senhadji, Lotfi

2014-12-01

Semi-symmetric three-way arrays are essential tools in blind source separation (BSS) particularly in independent component analysis (ICA). These arrays can be built by resorting to higher order statistics of the data. The canonical polyadic (CP) decomposition of such semi-symmetric three-way arrays allows us to identify the so-called mixing matrix, which contains the information about the intensities of some latent source signals present in the observation channels. In addition, in many applications, such as the magnetic resonance spectroscopy (MRS), the columns of the mixing matrix are viewed as relative concentrations of the spectra of the chemical components. Therefore, the two loading matrices of the three-way array, which are equal to the mixing matrix, are nonnegative. Most existing CP algorithms handle the symmetry and the nonnegativity separately. Up to now, very few of them consider both the semi-nonnegativity and the semi-symmetry structure of the three-way array. Nevertheless, like all the methods based on line search, trust region strategies, and alternating optimization, they appear to be dependent on initialization, requiring in practice a multi-initialization procedure. In order to overcome this drawback, we propose two new methods, called [InlineEquation not available: see fulltext.] and [InlineEquation not available: see fulltext.], to solve the problem of CP decomposition of semi-nonnegative semi-symmetric three-way arrays. Firstly, we rewrite the constrained optimization problem as an unconstrained one. In fact, the nonnegativity constraint of the two symmetric modes is ensured by means of a square change of variable. Secondly, a Jacobi-like optimization procedure is adopted because of its good convergence property. More precisely, the two new methods use LU and QR matrix factorizations, respectively, which consist in formulating high-dimensional optimization problems into several sequential polynomial and rational subproblems. By using both LU

An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

Science.gov (United States)

Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun

2017-09-01

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

Real-time tumor ablation simulation based on the dynamic mode decomposition method

KAUST Repository

Bourantas, George C.; Ghommem, Mehdi; Kagadis, George C.; Katsanos, Konstantinos H.; Loukopoulos, Vassilios C.; Burganos, Vasilis N.; Nikiforidis, George C.

2014-01-01

Purpose: The dynamic mode decomposition (DMD) method is used to provide a reliable forecasting of tumor ablation treatment simulation in real time, which is quite needed in medical practice. To achieve this, an extended Pennes bioheat model must

Catalytic effects of inorganic acids on the decomposition of ammonium nitrate.

Science.gov (United States)

Sun, Jinhua; Sun, Zhanhui; Wang, Qingsong; Ding, Hui; Wang, Tong; Jiang, Chuansheng

2005-12-09

In order to evaluate the catalytic effects of inorganic acids on the decomposition of ammonium nitrate (AN), the heat releases of decomposition or reaction of pure AN and its mixtures with inorganic acids were analyzed by a heat flux calorimeter C80. Through the experiments, the different reaction mechanisms of AN and its mixtures were analyzed. The chemical reaction kinetic parameters such as reaction order, activation energy and frequency factor were calculated with the C80 experimental results for different samples. Based on these parameters and the thermal runaway models (Semenov and Frank-Kamenestkii model), the self-accelerating decomposition temperatures (SADTs) of AN and its mixtures were calculated and compared. The results show that the mixtures of AN with acid are more unsteady than pure AN. The AN decomposition reaction is catalyzed by acid. The calculated SADTs of AN mixtures with acid are much lower than that of pure AN.

Management intensity alters decomposition via biological pathways

Science.gov (United States)

Wickings, Kyle; Grandy, A. Stuart; Reed, Sasha; Cleveland, Cory

2011-01-01

Current conceptual models predict that changes in plant litter chemistry during decomposition are primarily regulated by both initial litter chemistry and the stage-or extent-of mass loss. Far less is known about how variations in decomposer community structure (e.g., resulting from different ecosystem management types) could influence litter chemistry during decomposition. Given the recent agricultural intensification occurring globally and the importance of litter chemistry in regulating soil organic matter storage, our objectives were to determine the potential effects of agricultural management on plant litter chemistry and decomposition rates, and to investigate possible links between ecosystem management, litter chemistry and decomposition, and decomposer community composition and activity. We measured decomposition rates, changes in litter chemistry, extracellular enzyme activity, microarthropod communities, and bacterial versus fungal relative abundance in replicated conventional-till, no-till, and old field agricultural sites for both corn and grass litter. After one growing season, litter decomposition under conventional-till was 20% greater than in old field communities. However, decomposition rates in no-till were not significantly different from those in old field or conventional-till sites. After decomposition, grass residue in both conventional- and no-till systems was enriched in total polysaccharides relative to initial litter, while grass litter decomposed in old fields was enriched in nitrogen-bearing compounds and lipids. These differences corresponded with differences in decomposer communities, which also exhibited strong responses to both litter and management type. Overall, our results indicate that agricultural intensification can increase litter decomposition rates, alter decomposer communities, and influence litter chemistry in ways that could have important and long-term effects on soil organic matter dynamics. We suggest that future

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

Science.gov (United States)

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

An Improved Real-Coded Population-Based Extremal Optimization Method for Continuous Unconstrained Optimization Problems

Directory of Open Access Journals (Sweden)

Guo-Qiang Zeng

2014-01-01

Full Text Available As a novel evolutionary optimization method, extremal optimization (EO has been successfully applied to a variety of combinatorial optimization problems. However, the applications of EO in continuous optimization problems are relatively rare. This paper proposes an improved real-coded population-based EO method (IRPEO for continuous unconstrained optimization problems. The key operations of IRPEO include generation of real-coded random initial population, evaluation of individual and population fitness, selection of bad elements according to power-law probability distribution, generation of new population based on uniform random mutation, and updating the population by accepting the new population unconditionally. The experimental results on 10 benchmark test functions with the dimension N=30 have shown that IRPEO is competitive or even better than the recently reported various genetic algorithm (GA versions with different mutation operations in terms of simplicity, effectiveness, and efficiency. Furthermore, the superiority of IRPEO to other evolutionary algorithms such as original population-based EO, particle swarm optimization (PSO, and the hybrid PSO-EO is also demonstrated by the experimental results on some benchmark functions.

Optimal perturbations for nonlinear systems using graph-based optimal transport

Science.gov (United States)

Grover, Piyush; Elamvazhuthi, Karthik

2018-06-01

We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.

Spectral Tensor-Train Decomposition

DEFF Research Database (Denmark)

Bigoni, Daniele; Engsig-Karup, Allan Peter; Marzouk, Youssef M.

2016-01-01

The accurate approximation of high-dimensional functions is an essential task in uncertainty quantification and many other fields. We propose a new function approximation scheme based on a spectral extension of the tensor-train (TT) decomposition. We first define a functional version of the TT...... adaptive Smolyak approach. The method is also used to approximate the solution of an elliptic PDE with random input data. The open source software and examples presented in this work are available online (http://pypi.python.org/pypi/TensorToolbox/)....

The Optimal Wavelengths for Light Absorption Spectroscopy Measurements Based on Genetic Algorithm-Particle Swarm Optimization

Science.gov (United States)

Tang, Ge; Wei, Biao; Wu, Decao; Feng, Peng; Liu, Juan; Tang, Yuan; Xiong, Shuangfei; Zhang, Zheng

2018-03-01

To select the optimal wavelengths in the light extinction spectroscopy measurement, genetic algorithm-particle swarm optimization (GAPSO) based on genetic algorithm (GA) and particle swarm optimization (PSO) is adopted. The change of the optimal wavelength positions in different feature size parameters and distribution parameters is evaluated. Moreover, the Monte Carlo method based on random probability is used to identify the number of optimal wavelengths, and good inversion effects of the particle size distribution are obtained. The method proved to have the advantage of resisting noise. In order to verify the feasibility of the algorithm, spectra with bands ranging from 200 to 1000 nm are computed. Based on this, the measured data of standard particles are used to verify the algorithm.