System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

International Nuclear Information System (INIS)

Uriarte, A Goienetxea; Zúñiga, E Ruiz; Moris, M Urenda; Ng, A H C

2015-01-01

Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process. (paper)

Evaluation and improvement of dynamic optimality in electrochemical reactors

International Nuclear Information System (INIS)

Vijayasekaran, B.; Basha, C. Ahmed

2005-01-01

A systematic approach for the dynamic optimization problem statement to improve the dynamic optimality in electrochemical reactors is presented in this paper. The formulation takes an account of the diffusion phenomenon in the electrode/electrolyte interface. To demonstrate the present methodology, the optimal time-varying electrode potential for a coupled chemical-electrochemical reaction scheme, that maximizes the production of the desired product in a batch electrochemical reactor with/without recirculation are determined. The dynamic optimization problem statement, based upon this approach, is a nonlinear differential algebraic system, and its solution provides information about the optimal policy. Optimal control policy at different conditions is evaluated using the best-known Pontryagin's maximum principle. The two-point boundary value problem resulting from the application of the maximum principle is then solved using the control vector iteration technique. These optimal time-varying profiles of electrode potential are then compared to the best uniform operation through the relative improvements of the performance index. The application of the proposed approach to two electrochemical systems, described by ordinary differential equations, shows that the existing electrochemical process control strategy could be improved considerably when the proposed method is incorporated

Parameter Optimization and Electrode Improvement of Rotary Stepper Micromotor

Science.gov (United States)

Sone, Junji; Mizuma, Toshinari; Mochizuki, Shunsuke; Sarajlic, Edin; Yamahata, Christophe; Fujita, Hiroyuki

We developed a three-phase electrostatic stepper micromotor and performed a numerical simulation to improve its performance for practical use and to optimize its design. We conducted its circuit simulation by simplifying its structure, and the effect of springback force generated by supported mechanism using flexures was considered. And we considered new improvement method for electrodes. This improvement and other parameter optimizations achieved the low voltage drive of micromotor.

Integrated Medical Model (IMM) Optimization Version 4.0 Functional Improvements

Science.gov (United States)

Arellano, John; Young, M.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Goodenow, D. A.; Myers, J. G.

2016-01-01

The IMMs ability to assess mission outcome risk levels relative to available resources provides a unique capability to provide guidance on optimal operational medical kit and vehicle resources. Post-processing optimization allows IMM to optimize essential resources to improve a specific model outcome such as maximization of the Crew Health Index (CHI), or minimization of the probability of evacuation (EVAC) or the loss of crew life (LOCL). Mass and or volume constrain the optimized resource set. The IMMs probabilistic simulation uses input data on one hundred medical conditions to simulate medical events that may occur in spaceflight, the resources required to treat those events, and the resulting impact to the mission based on specific crew and mission characteristics. Because IMM version 4.0 provides for partial treatment for medical events, IMM Optimization 4.0 scores resources at the individual resource unit increment level as opposed to the full condition-specific treatment set level, as done in version 3.0. This allows the inclusion of as many resources as possible in the event that an entire set of resources called out for treatment cannot satisfy the constraints. IMM Optimization version 4.0 adds capabilities that increase efficiency by creating multiple resource sets based on differing constraints and priorities, CHI, EVAC, or LOCL. It also provides sets of resources that improve mission-related IMM v4.0 outputs with improved performance compared to the prior optimization. The new optimization represents much improved fidelity that will improve the utility of the IMM 4.0 for decision support.

An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

Directory of Open Access Journals (Sweden)

Xiaofeng Lv

2018-01-01

Full Text Available Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR and fault isolation rate (FIR. From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.

Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Xun Zhang

2014-01-01

Full Text Available Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

Directory of Open Access Journals (Sweden)

Guoxing Li

2015-01-01

Full Text Available The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious.

Improved particle swarm optimization combined with chaos

International Nuclear Information System (INIS)

Liu Bo; Wang Ling; Jin Yihui; Tang Fang; Huang Dexian

2005-01-01

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality

The Improvement of Particle Swarm Optimization: a Case Study of Optimal Operation in Goupitan Reservoir

Science.gov (United States)

Li, Haichen; Qin, Tao; Wang, Weiping; Lei, Xiaohui; Wu, Wenhui

2018-02-01

Due to the weakness in holding diversity and reaching global optimum, the standard particle swarm optimization has not performed well in reservoir optimal operation. To solve this problem, this paper introduces downhill simplex method to work together with the standard particle swarm optimization. The application of this approach in Goupitan reservoir optimal operation proves that the improved method had better accuracy and higher reliability with small investment.

Chain alignment for improved properties - Optimization of PLA and PHB-V by crystallization and reinforcement

Science.gov (United States)

Moser, K.; Bergmann, B.; Diemert, J.; Elsner, P.

2014-05-01

In this paper two promising ways to improve the material characteristics of PLA and PHB-V are presented by showing their positive effects on mechanical, optical, and thermal properties. The optimization is achieved by increasing the crystallization from the melt of the polymer chains and the other by means of a reinforcement of the matrices by bio-based materials. In the case of crystallization specific nucleating agents and optimized process parameters promote optimized crystallization conditions and lead particularly in toughness to significant improvements. In addition to crystallization the introduction of cellulose-based reinforcing materials is also a good alternative to improve the ductility of a biopolymer matrix considerably. Due to their polar surface structure cellulose fibres are favouring a very good interaction to the also polar biopolymers. In addition, the polar surfaces of both materials results in very homogeneous dispersion within the compound.

Setting value optimization method in integration for relay protection based on improved quantum particle swarm optimization algorithm

Science.gov (United States)

Yang, Guo Sheng; Wang, Xiao Yang; Li, Xue Dong

2018-03-01

With the establishment of the integrated model of relay protection and the scale of the power system expanding, the global setting and optimization of relay protection is an extremely difficult task. This paper presents a kind of application in relay protection of global optimization improved particle swarm optimization algorithm and the inverse time current protection as an example, selecting reliability of the relay protection, selectivity, quick action and flexibility as the four requires to establish the optimization targets, and optimizing protection setting values of the whole system. Finally, in the case of actual power system, the optimized setting value results of the proposed method in this paper are compared with the particle swarm algorithm. The results show that the improved quantum particle swarm optimization algorithm has strong search ability, good robustness, and it is suitable for optimizing setting value in the relay protection of the whole power system.

Improved genetic algorithm in optimization of beam orientation in intensity modulated radiotherapy

International Nuclear Information System (INIS)

Ni Xinye; Yang Jianhua; Sun Suping; Yao Yi

2009-01-01

Objective: At present beam orientation selection in intensity-modulated radiotherapy (IMRT) is mainly based on empiric knowledge. This study is to evaluate the feasibility of automated beam angle selection. Methods: Genetic algorithm technique which based on beam eye view dose measurement (BEVD-GA) was tested on two clinical cases, including a spine column cancer and a lung cancer. Three plans were obtained under the following different beam configurations: five equiangular-spaced beams, five beams with GA-selected, and five beams with BEVD-GA-selected beams. Then the dose distribution was compared among the three plans. Results: The method, restricting the range of genetic algorithm followed by carrying through genetic operations, not only shortened the optimization time, but also improved the optimization effect. For spine column cancer and lung cancer, the best IMRT plans were obtained with BEVD-GA-selected beams, which used automated beam orientation selection. Conclusions: Comparing with the conventional manual beam orientation selection, beam orientation optimization which is feasible in IMRT planning may significantly improve the efficiency and result. (authors)

Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm

Science.gov (United States)

Cui, Xue; Gao, Jian; Feng, Yunbin; Zou, Chenlu; Liu, Huanlei

2018-01-01

In this paper, an optimization model with the minimum active power loss and minimum voltage deviation of node and maximum static voltage stability margin as the optimization objective is proposed for the reactive power optimization problems. By defining the index value of reactive power compensation, the optimal reactive power compensation node was selected. The particle swarm optimization algorithm was improved, and the selection pool of global optimal and the global optimal of probability (p-gbest) were introduced. A set of Pareto optimal solution sets is obtained by this algorithm. And by calculating the fuzzy membership value of the pareto optimal solution sets, individuals with the smallest fuzzy membership value were selected as the final optimization results. The above improved algorithm is used to optimize the reactive power of IEEE14 standard node system. Through the comparison and analysis of the results, it has been proven that the optimization effect of this algorithm was very good.

Optimization of the tumor microenvironment and nanomedicine properties simultaneously to improve tumor therapy.

Science.gov (United States)

Zhang, Bo; Shi, Wei; Jiang, Ting; Wang, Lanting; Mei, Heng; Lu, Heng; Hu, Yu; Pang, Zhiqing

2016-09-20

Effective delivery of nanomedicines to tumor tissues depends on both the tumor microenvironment and nanomedicine properties. Accordingly, tumor microenvironment modification or advanced design of nanomedicine was emerging to improve nanomedicine delivery to tumors. However, few studies have emphasized the necessity to optimize the tumor microenvironment and nanomedicine properties simultaneously to improve tumor treatment. In the present study, imatinib mesylate (IMA) was used to normalize the tumor microenvironment including platelet-derived growth factor receptor-β expression inhibition, tumor vessel normalization, and tumor perfusion improvement as demonstrated by immunofluorescence staining. In addition, the effect of tumor microenvironment normalization on tumor delivery of nanomedicines with different sizes was carefully investigated. It was shown that IMA treatment significantly reduced the accumulation of nanoparticles (NPs) around 110 nm but enhanced the accumulation of micelles around 23 nm by in vivo fluorescence imaging experiment. Furthermore, IMA treatment limited the distribution of NPs inside tumors but increased that of micelles with a more homogeneous pattern. Finally, the anti-tumor efficacy study displayed that IMA pretreatment could significantly increase the therapeutic effects of paclitaxel-loaded micelles. All-together, a new strategy to improve nanomedicine delivery to tumor was provided by optimizing both nanomedicine size and the tumor microenvironment simultaneously, and it will have great potential in clinics for tumor treatment.

Improved Optimization for Wastewater Treatment and Reuse System Using Computational Intelligence

Directory of Open Access Journals (Sweden)

Zong Woo Geem

2018-01-01

Full Text Available River water pollution by wastewater can cause significant negative impact on the aquatic sustainability. Hence, accurate modeling of this complicated system and its cost-effective treatment and reuse decision is very important because this optimization process is related to economic expenditure, societal health, and environmental deterioration. In order to optimize this complex system, we may consider three treatment or reuse options such as microscreening filtration, nitrification, and fertilization-oriented irrigation on top of two existing options such as settling and biological oxidation. The objective of this environmental optimization is to minimize the economic expenditure of life cycle costs while satisfying the public health standard in terms of groundwater quality and the environmental standard in terms of river water quality. Particularly, this study improves existing optimization model by pinpointing the critical deficit location of dissolved oxygen sag curve by using analytic differentiation. Also, the proposed formulation considers more practical constraints such as maximal size of irrigation area and minimal amount of filtration treatment process. The results obtained by using an evolutionary algorithm, named a parameter-setting-free harmony search algorithm, show that the proposed model successfully finds optimal solutions while conveniently locating the critical deficit point.

CASTING IMPROVEMENT BASED ON METAHEURISTIC OPTIMIZATION AND NUMERICAL SIMULATION

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Radomir Radiša

2017-12-01

Full Text Available This paper presents the use of metaheuristic optimization techniques to support the improvement of casting process. Genetic algorithm (GA, Ant Colony Optimization (ACO, Simulated annealing (SA and Particle Swarm Optimization (PSO have been considered as optimization tools to define the geometry of the casting part’s feeder. The proposed methodology has been demonstrated in the design of the feeder for casting Pelton turbine bucket. The results of the optimization are dimensional characteristics of the feeder, and the best result from all the implemented optimization processes has been adopted. Numerical simulation has been used to verify the validity of the presented design methodology and the feeding system optimization in the casting system of the Pelton turbine bucket.

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

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R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

Science.gov (United States)

Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

2017-12-01

Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design

Directory of Open Access Journals (Sweden)

Yu Wang

2015-01-01

Full Text Available A new reliability-based design optimization (RBDO method based on support vector machines (SVM and the Most Probable Point (MPP is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.

Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

OpenAIRE

Jing Wang; Yourui Huang

2013-01-01

In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...

An improved version of Inverse Distance Weighting metamodel assisted Harmony Search algorithm for truss design optimization

Directory of Open Access Journals (Sweden)

Y. Gholipour

Full Text Available This paper focuses on a metamodel-based design optimization algorithm. The intention is to improve its computational cost and convergence rate. Metamodel-based optimization method introduced here, provides the necessary means to reduce the computational cost and convergence rate of the optimization through a surrogate. This algorithm is a combination of a high quality approximation technique called Inverse Distance Weighting and a meta-heuristic algorithm called Harmony Search. The outcome is then polished by a semi-tabu search algorithm. This algorithm adopts a filtering system and determines solution vectors where exact simulation should be applied. The performance of the algorithm is evaluated by standard truss design problems and there has been a significant decrease in the computational effort and improvement of convergence rate.

Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

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Feifei Dong

2014-01-01

Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.

Propositional Optimal Trajectory Programming for Improving Stability ...

African Journals Online (AJOL)

Propositional Optimal Trajectory Programming for Improving Stability of Hermite Definite Control System. ... PROMOTING ACCESS TO AFRICAN RESEARCH. AFRICAN JOURNALS ONLINE (AJOL) ... Knowledge of systems operation subjected to heat diffusion constraints is required of systems analysts. In an instance that ...

Improving Battery Reactor Core Design Using Optimization Method

International Nuclear Information System (INIS)

Son, Hyung M.; Suh, Kune Y.

2011-01-01

The Battery Omnibus Reactor Integral System (BORIS) is a small modular fast reactor being designed at Seoul National University to satisfy various energy demands, to maintain inherent safety by liquid-metal coolant lead for natural circulation heat transport, and to improve power conversion efficiency with the Modular Optimal Balance Integral System (MOBIS) using the supercritical carbon dioxide as working fluid. This study is focused on developing the Neutronics Optimized Reactor Analysis (NORA) method that can quickly generate conceptual design of a battery reactor core by means of first principle calculations, which is part of the optimization process for reactor assembly design of BORIS

Optimal improvement of graphs related to nuclear safeguards problems

International Nuclear Information System (INIS)

Jacobsen, S.E.

1977-08-01

This report develops the methodology for optimally improving graphs related to nuclear safeguards issues. In particular, given a fixed number of dollars, the report provides a method for optimally allocating such dollars over the arcs of a weighted graph (the weights vary as a function of dollars spent on arcs) so as to improve the system effectiveness measure which is the shortest of all shortest paths to several targets. Arc weights can be either clock times or detection probabilities and the algorithm does not explicitly consider all paths to the targets

Improved hybrid optimization algorithm for 3D protein structure prediction.

Science.gov (United States)

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

Science.gov (United States)

Jiang, Shouyong; Yang, Shengxiang

2016-02-01

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

Optimization of the reflux ratio for a stage distillation column based on an improved particle swarm algorithm

DEFF Research Database (Denmark)

Ren, Jingzheng; Tan, Shiyu; Dong, Lichun

2010-01-01

A mathematical model relating operation profits with reflux ratio of a stage distillation column was established. In order to optimize the reflux ratio by solving the nonlinear objective function, an improved particle swarm algorithm was developed and has been proved to be able to enhance...... the searching ability of basic particle swarm algorithm significantly. An example of utilizing the improved algorithm to solve the mathematical model was demonstrated; the result showed that it is efficient and convenient to optimize the reflux ratio for a distillation column by using the mathematical model...

Design and optimization of self-nanoemulsifying drug delivery systems for improved bioavailability of cyclovirobuxine D.

Science.gov (United States)

Ke, Zhongcheng; Hou, Xuefeng; Jia, Xiao-Bin

2016-01-01

The main purpose of this research was to design a self-nanoemulsifying drug delivery system (SNEDDS) for improving the bioavailability of cyclovirobuxine D as a poorly water-soluble drug. Solubility trials, emulsifying studies, and pseudo-ternary phase diagrams were used to screen the SNEDDS formulations. The optimized drug-loaded SNEDDS was prepared at a mass ratio of 3:24:38:38 for cyclovirobuxine D, oleic acid, Solutol SH15, and propylene glycol, respectively. The optimized formulation was characterized in terms of physicochemical and pharmacokinetic parameters compared with marketed cyclovirobuxine D tablets. The optimized cyclovirobuxine-D-loaded SNEDDS was spontaneously dispersed to form a nanoemulsion with a globule size of 64.80±3.58 nm, which exhibited significant improvement of drug solubility, rapid absorption rate, and enhanced area under the curve, together with increased permeation and decreased efflux. Fortunately, there was a nonsignificant cytotoxic effect toward Caco-2 cells. The relative bioavailability of SNEDDS was 200.22% in comparison with market tablets, in rabbits. SNEDDS could be a potential candidate for an oral dosage form of cyclovirobuxine D with improved bioavailability.

Improving scanner wafer alignment performance by target optimization

Science.gov (United States)

Leray, Philippe; Jehoul, Christiane; Socha, Robert; Menchtchikov, Boris; Raghunathan, Sudhar; Kent, Eric; Schoonewelle, Hielke; Tinnemans, Patrick; Tuffy, Paul; Belen, Jun; Wise, Rich

2016-03-01

In the process nodes of 10nm and below, the patterning complexity along with the processing and materials required has resulted in a need to optimize alignment targets in order to achieve the required precision, accuracy and throughput performance. Recent industry publications on the metrology target optimization process have shown a move from the expensive and time consuming empirical methodologies, towards a faster computational approach. ASML's Design for Control (D4C) application, which is currently used to optimize YieldStar diffraction based overlay (DBO) metrology targets, has been extended to support the optimization of scanner wafer alignment targets. This allows the necessary process information and design methodology, used for DBO target designs, to be leveraged for the optimization of alignment targets. In this paper, we show how we applied this computational approach to wafer alignment target design. We verify the correlation between predictions and measurements for the key alignment performance metrics and finally show the potential alignment and overlay performance improvements that an optimized alignment target could achieve.

Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimization Model for Stand-Alone Microgrid Operation

Directory of Open Access Journals (Sweden)

Fei Wang

2017-11-01

Full Text Available The optimal dispatching model for a stand-alone microgrid (MG is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditions as storms or abnormally low or high temperatures. A new two-time scale multi-objective optimization model, including day-ahead cursory scheduling and real-time scheduling for finer adjustments, is proposed to optimize the operational cost, load shedding compensation and environmental benefit of stand-alone MG through controllable load (CL and multi-distributed generations (DGs. The main novelty of the proposed model is that the synergetic response of CL and energy storage system (ESS in real-time scheduling offset the operation uncertainty quickly. And the improved dispatch strategy for combined cooling-heating-power (CCHP enhanced the system economy while the comfort is guaranteed. An improved algorithm, Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy (SIP-CO-PSO-ERS algorithm with strong searching capability and fast convergence speed, was presented to deal with the problem brought by the increased errors between actual renewable generation and load and prior predictions. Four typical scenarios are designed according to the combinations of day types (work day or weekend and weather categories (sunny or rainy to verify the performance of the presented dispatch strategy. The simulation results show that the proposed two-time scale model and SIP-CO-PSO-ERS algorithm exhibit better performance in adaptability, convergence speed and search ability than conventional methods for the stand-alone MG’s operation.

An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

Directory of Open Access Journals (Sweden)

Tao Zhang

2012-01-01

Full Text Available An improved particle swarm optimization (PSO algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP. For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

Optimization Techniques for Improving the Performance of Silicone-Based Dielectric Elastomers

DEFF Research Database (Denmark)

Skov, Anne Ladegaard; Yu, Liyun

2017-01-01

the electro-mechanical performance of dielectric elastomers are highlighted. Various optimization methods for improved energy transduction are investigated and discussed, with special emphasis placed on the promise each method holds. The compositing and blending of elastomers are shown to be simple, versatile...... methods that can solve a number of optimization issues. More complicated methods, involving chemical modification of the silicone backbone as well as controlling the network structure for improved mechanical properties, are shown to solve yet more issues. From the analysis, it is obvious...... that there is not a single optimization technique that will lead to the universal optimization of dielectric elastomer films, though each method may lead to elastomers with certain features, and thus certain potentials....

Improvement in pulmonary functions and clinical parameters due to addition of breathing exercises in asthma patients receiving optimal treatment

Directory of Open Access Journals (Sweden)

Dipti Agarwal

2017-01-01

Conclusions: Breathing exercises provided significant improvements in spirometric parameters and significant reduction in breathlessness, wheezing, and nocturnal symptoms as well as requirements of rescue medicines in asthma patients who were receiving optimal asthma treatment.

Partial Transmit Sequence Optimization Using Improved Harmony Search Algorithm for PAPR Reduction in OFDM

Directory of Open Access Journals (Sweden)

Mangal Singh

2017-12-01

Full Text Available This paper considers the use of the Partial Transmit Sequence (PTS technique to reduce the Peak‐to‐Average Power Ratio (PAPR of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search‐based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

Application of improved AHP method to radiation protection optimization

International Nuclear Information System (INIS)

Wang Chuan; Zhang Jianguo; Yu Lei

2014-01-01

Aimed at the deficiency of traditional AHP method, a hierarchy model for optimum project selection of radiation protection was established with the improved AHP method. The result of comparison between the improved AHP method and the traditional AHP method shows that the improved AHP method can reduce personal judgment subjectivity, and its calculation process is compact and reasonable. The improved AHP method can provide scientific basis for radiation protection optimization. (authors)

The optimal monochromatic spectral computed tomographic imaging plus adaptive statistical iterative reconstruction algorithm can improve the superior mesenteric vessel image quality

Energy Technology Data Exchange (ETDEWEB)

Yin, Xiao-Ping; Zuo, Zi-Wei; Xu, Ying-Jin; Wang, Jia-Ning [CT/MRI room, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000 (China); Liu, Huai-Jun, E-mail: hebeiliu@outlook.com [Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000 (China); Liang, Guang-Lu [CT/MRI room, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000 (China); Gao, Bu-Lang, E-mail: browngao@163.com [Department of Medical Research, Shijiazhuang First Hospital, Shijiazhuang, Hebei, 050011 (China)

2017-04-15

Objective: To investigate the effect of the optimal monochromatic spectral computed tomography (CT) plus adaptive statistical iterative reconstruction on the improvement of the image quality of the superior mesenteric artery and vein. Materials and methods: The gemstone spectral CT angiographic data of 25 patients were reconstructed in the following three groups: 70 KeV, the optimal monochromatic imaging, and the optimal monochromatic plus 40%iterative reconstruction mode. The CT value, image noises (IN), background CT value and noises, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image scores of the vessels and surrounding tissues were analyzed. Results: In the 70 KeV, the optimal monochromatic and the optimal monochromatic images plus 40% iterative reconstruction group, the mean scores of image quality were 3.86, 4.24 and 4.25 for the superior mesenteric artery and 3.46, 3.78 and 3.81 for the superior mesenteric vein, respectively. The image quality scores for the optimal monochromatic and the optimal monochromatic plus 40% iterative reconstruction groups were significantly greater than for the 70 KeV group (P < 0.05). The vascular CT value, image noise, background noise, CNR and SNR were significantly (P < 0.001) greater in the optimal monochromatic and the optimal monochromatic images plus 40% iterative reconstruction group than in the 70 KeV group. The optimal monochromatic plus 40% iterative reconstruction group had significantly (P < 0.05) lower image and background noise but higher CNR and SNR than the other two groups. Conclusion: The optimal monochromatic imaging combined with 40% iterative reconstruction using low-contrast agent dosage and low injection rate can significantly improve the image quality of the superior mesenteric artery and vein.

Optimization and Improvement of Test Processes on a Production Line

Science.gov (United States)

Sujová, Erika; Čierna, Helena

2018-06-01

The paper deals with increasing processes efficiency at a production line of cylinder heads of engines in a production company operating in the automotive industry. The goal is to achieve improvement and optimization of test processes on a production line. It analyzes options for improving capacity, availability and productivity of processes of an output test by using modern technology available on the market. We have focused on analysis of operation times before and after optimization of test processes at specific production sections. By analyzing measured results we have determined differences in time before and after improvement of the process. We have determined a coefficient of efficiency OEE and by comparing outputs we have confirmed real improvement of the process of the output test of cylinder heads.

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.

Interleaved segment correction achieves higher improvement factors in using genetic algorithm to optimize light focusing through scattering media

Science.gov (United States)

Li, Runze; Peng, Tong; Liang, Yansheng; Yang, Yanlong; Yao, Baoli; Yu, Xianghua; Min, Junwei; Lei, Ming; Yan, Shaohui; Zhang, Chunmin; Ye, Tong

2017-10-01

Focusing and imaging through scattering media has been proved possible with high resolution wavefront shaping. A completely scrambled scattering field can be corrected by applying a correction phase mask on a phase only spatial light modulator (SLM) and thereby the focusing quality can be improved. The correction phase is often found by global searching algorithms, among which Genetic Algorithm (GA) stands out for its parallel optimization process and high performance in noisy environment. However, the convergence of GA slows down gradually with the progression of optimization, causing the improvement factor of optimization to reach a plateau eventually. In this report, we propose an interleaved segment correction (ISC) method that can significantly boost the improvement factor with the same number of iterations comparing with the conventional all segment correction method. In the ISC method, all the phase segments are divided into a number of interleaved groups; GA optimization procedures are performed individually and sequentially among each group of segments. The final correction phase mask is formed by applying correction phases of all interleaved groups together on the SLM. The ISC method has been proved significantly useful in practice because of its ability to achieve better improvement factors when noise is present in the system. We have also demonstrated that the imaging quality is improved as better correction phases are found and applied on the SLM. Additionally, the ISC method lowers the demand of dynamic ranges of detection devices. The proposed method holds potential in applications, such as high-resolution imaging in deep tissue.

Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

Science.gov (United States)

Ma, Xiaoqi

2015-01-01

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

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

Science.gov (United States)

Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein

2018-05-01

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

Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics

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Ghanshyam G. Tejani

2018-04-01

Full Text Available In this study, simultaneous size, shape, and topology optimization of planar and space trusses are investigated. Moreover, the trusses are subjected to constraints for element stresses, nodal displacements, and kinematic stability conditions. Truss Topology Optimization (TTO removes the superfluous elements and nodes from the ground structure. In this method, the difficulties arise due to unacceptable and singular topologies; therefore, the Grubler’s criterion and the positive definiteness are used to handle such issue. Moreover, the TTO is challenging due to its search space, which is implicit, non-convex, non-linear, and often leading to divergence. Therefore, mutation-based metaheuristics are proposed to investigate them. This study compares the performance of four improved metaheuristics (viz. Improved Teaching–Learning-Based Optimization (ITLBO, Improved Heat Transfer Search (IHTS, Improved Water Wave Optimization (IWWO, and Improved Passing Vehicle Search (IPVS and four basic metaheuristics (viz. TLBO, HTS, WWO, and PVS in order to solve structural optimization problems. Keywords: Structural optimization, Mutation operator, Improved metaheuristics, Modified algorithms, Truss topology optimization

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

Science.gov (United States)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

Collaborative Project: Building improved optimized parameter estimation algorithms to improve methane and nitrogen fluxes in a climate model

Energy Technology Data Exchange (ETDEWEB)

Mahowald, Natalie [Cornell Univ., Ithaca, NY (United States)

2016-11-29

earth science with limited numbers of simulations; and, c) will be (as part of the proposed research) significantly improved both by adding asynchronous parallelism, early truncation of unsuccessful simulations, and the improvement of both serial and parallel performance by the use of derivative and sensitivity information from global and local surrogate approximations ^S(x). The algorithm development and testing will be focused on the CLM-ME/N model application, but the methods are general and are expected to also perform well on optimization for parameter estimation of other climate models and other classes of continuous multimodal optimization problems arising from complex simulation models. In addition, this proposal will compile available datasets of emissions of methane, nitrous oxides and reactive nitrogen species and develop protocols for site level comparisons with the CLM-ME/N. Once the model parameters are optimized against site level data, the model will be simulated at the global level and compared to atmospheric concentration measurements for the current climate, and future emissions will be estimated using climate change as simulated by the CESM. This proposal combines experts in earth system modeling, optimization, computer science, and process level understanding of soil gas emissions in an interdisciplinary team in order to improve the modeling of methane and nitrogen gas emissions. This proposal thus meets the requirements of the SciDAC RFP, by integrating state-of-the-art computer science and earth system to build an improved earth system model.

Improvement of Low-Frequency Sound Field Obtained by an Optimized Boundary

Institute of Scientific and Technical Information of China (English)

JING Lu; ZHU Xiao-tian

2006-01-01

An approach based on the finite element analysis was introduced to improve low-frequency sound field. The optimized scatters on the wall redistribute the modes of the room and provide effective diffusion of sound field. The frequency response, eigenfrequency, spatial distribution and transient response were calculated. Experimental data were obtained through a 1:5 scaled set up. The results show that the optimized treatment has a positive effect on sound field and the improvement is obvious.

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization.

Science.gov (United States)

Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing

2015-01-01

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.

Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

Directory of Open Access Journals (Sweden)

Jude Hemanth Duraisamy

2016-01-01

Full Text Available Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA and Particle Swarm Optimization (PSO have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT and Finite Ridgelet Transform (FRIT are used in combination with GA and PSO to improve the efficiency of the image steganography system.

Improved detection of multiple environmental antibiotics through an optimized sample extraction strategy in liquid chromatography-mass spectrometry analysis.

Science.gov (United States)

Yi, Xinzhu; Bayen, Stéphane; Kelly, Barry C; Li, Xu; Zhou, Zhi

2015-12-01

A solid-phase extraction/liquid chromatography/electrospray ionization/multi-stage mass spectrometry (SPE-LC-ESI-MS/MS) method was optimized in this study for sensitive and simultaneous detection of multiple antibiotics in urban surface waters and soils. Among the seven classes of tested antibiotics, extraction efficiencies of macrolides, lincosamide, chloramphenicol, and polyether antibiotics were significantly improved under optimized sample extraction pH. Instead of only using acidic extraction in many existing studies, the results indicated that antibiotics with low pK a values (antibiotics with high pK a values (>7) were extracted more efficiently under neutral conditions. The effects of pH were more obvious on polar compounds than those on non-polar compounds. Optimization of extraction pH resulted in significantly improved sample recovery and better detection limits. Compared with reported values in the literature, the average reduction of minimal detection limits obtained in this study was 87.6% in surface waters (0.06-2.28 ng/L) and 67.1% in soils (0.01-18.16 ng/g dry wt). This method was subsequently applied to detect antibiotics in environmental samples in a heavily populated urban city, and macrolides, sulfonamides, and lincomycin were frequently detected. Antibiotics with highest detected concentrations were sulfamethazine (82.5 ng/L) in surface waters and erythromycin (6.6 ng/g dry wt) in soils. The optimized sample extraction strategy can be used to improve the detection of a variety of antibiotics in environmental surface waters and soils.

Towards improving searches for optimal phylogenies.

Science.gov (United States)

Ford, Eric; St John, Katherine; Wheeler, Ward C

2015-01-01

Finding the optimal evolutionary history for a set of taxa is a challenging computational problem, even when restricting possible solutions to be "tree-like" and focusing on the maximum-parsimony optimality criterion. This has led to much work on using heuristic tree searches to find approximate solutions. We present an approach for finding exact optimal solutions that employs and complements the current heuristic methods for finding optimal trees. Given a set of taxa and a set of aligned sequences of characters, there may be subsets of characters that are compatible, and for each such subset there is an associated (possibly partially resolved) phylogeny with edges corresponding to each character state change. These perfect phylogenies serve as anchor trees for our constrained search space. We show that, for sequences with compatible sites, the parsimony score of any tree [Formula: see text] is at least the parsimony score of the anchor trees plus the number of inferred changes between [Formula: see text] and the anchor trees. As the maximum-parsimony optimality score is additive, the sum of the lower bounds on compatible character partitions provides a lower bound on the complete alignment of characters. This yields a region in the space of trees within which the best tree is guaranteed to be found; limiting the search for the optimal tree to this region can significantly reduce the number of trees that must be examined in a search of the space of trees. We analyze this method empirically using four different biological data sets as well as surveying 400 data sets from the TreeBASE repository, demonstrating the effectiveness of our technique in reducing the number of steps in exact heuristic searches for trees under the maximum-parsimony optimality criterion. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An optimization model for improving highway safety

Directory of Open Access Journals (Sweden)

Promothes Saha

2016-12-01

Full Text Available This paper developed a traffic safety management system (TSMS for improving safety on county paved roads in Wyoming. TSMS is a strategic and systematic process to improve safety of roadway network. When funding is limited, it is important to identify the best combination of safety improvement projects to provide the most benefits to society in terms of crash reduction. The factors included in the proposed optimization model are annual safety budget, roadway inventory, roadway functional classification, historical crashes, safety improvement countermeasures, cost and crash reduction factors (CRFs associated with safety improvement countermeasures, and average daily traffics (ADTs. This paper demonstrated how the proposed model can identify the best combination of safety improvement projects to maximize the safety benefits in terms of reducing overall crash frequency. Although the proposed methodology was implemented on the county paved road network of Wyoming, it could be easily modified for potential implementation on the Wyoming state highway system. Other states can also benefit by implementing a similar program within their jurisdictions.

Optimized Clustering Estimators for BAO Measurements Accounting for Significant Redshift Uncertainty

Energy Technology Data Exchange (ETDEWEB)

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

2017-05-15

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

Beam optimization: improving methodology

International Nuclear Information System (INIS)

Quinteiro, Guillermo F.

2004-01-01

Different optimization techniques commonly used in biology and food technology allow a systematic and complete analysis of response functions. In spite of the great interest in medical and nuclear physics in the problem of optimizing mixed beams, little attention has been given to sophisticate mathematical tools. Indeed, many techniques are perfectly suited to the typical problem of beam optimization. This article is intended as a guide to the use of two methods, namely Response Surface Methodology and Simplex, that are expected to fasten the optimization process and, meanwhile give more insight into the relationships among the dependent variables controlling the response

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

Science.gov (United States)

Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

2016-01-01

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

Low Complexity Models to improve Incomplete Sensitivities for Shape Optimization

Science.gov (United States)

Stanciu, Mugurel; Mohammadi, Bijan; Moreau, Stéphane

2003-01-01

The present global platform for simulation and design of multi-model configurations treat shape optimization problems in aerodynamics. Flow solvers are coupled with optimization algorithms based on CAD-free and CAD-connected frameworks. Newton methods together with incomplete expressions of gradients are used. Such incomplete sensitivities are improved using reduced models based on physical assumptions. The validity and the application of this approach in real-life problems are presented. The numerical examples concern shape optimization for an airfoil, a business jet and a car engine cooling axial fan.

Optimization of ship inner shell to improve the safety of seagoing transport ship

Directory of Open Access Journals (Sweden)

Yan-Yun YU

2013-09-01

Full Text Available A practical Ship Inner Shell Optimization Method (SISOM, the purpose of which is to improve the safety of the seagoing transport ship by decreasing the maximum Still Water Bending Moment (SWBM of the hull girder under all typical loading conditions, is presented in this paper. The objective of SISOM is to make the maximum SWBM minimum, and the section areas of the inner shell are taken as optimization variables. The main requirements of the ship performances, such as cargo hold capacity, propeller and rudder immersion, bridge visibility, damage stability and prevention of pollution etc., are taken as constraints. The penalty function method is used in SISOM to change the above nonlinear constraint problem into an unconstrained one, which is then solved by applying the steepest descent method. After optimization, the optimal section area distribution of the inner shell is obtained, and the shape of inner shell is adjusted according to the optimal section area. SISOM is applied to a product oil tanker and a bulk carrier, and the maximum SWBM of the two ships is significantly decreased by changing the shape of inner shell plate slightly. The two examples prove that SISOM is highly efficient and valuable to engineering practice.

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.

An Improved Chaos Genetic Algorithm for T-Shaped MIMO Radar Antenna Array Optimization

Directory of Open Access Journals (Sweden)

Xin Fu

2014-01-01

Full Text Available In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

Directory of Open Access Journals (Sweden)

Feng Zou

2016-01-01

Full Text Available An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO, which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

Optimal implementation of best management practices to improve agricultural hydrology and water quality

Science.gov (United States)

Liu, Y.; Engel, B.; Collingsworth, P.; Pijanowski, B. C.

2017-12-01

Nutrient loading from the Maumee River watershed is a significant reason for the harmful algal blooms (HABs) problem in Lake Erie. Strategies to reduce nutrient loading from agricultural areas in the Maumee River watershed need to be explored. Best management practices (BMPs) are popular approaches for improving hydrology and water quality. Various scenarios of BMP implementation were simulated in the AXL watershed (an agricultural watershed in Maumee River watershed) using Soil and Water Assessment Tool (SWAT) and a new BMP cost tool to explore the cost-effectiveness of the practices. BMPs of interest included vegetative filter strips, grassed waterways, blind inlets, grade stabilization structures, wetlands, no-till, nutrient management, residue management, and cover crops. The following environmental concerns were considered: streamflow, Total Phosphorous (TP), Dissolved Reactive Phosphorus (DRP), Total Kjeldahl Nitrogen (TKN), and Nitrate+Nitrite (NOx). To obtain maximum hydrological and water quality benefits with minimum cost, an optimization tool was developed to optimally select and place BMPs by connecting SWAT, the BMP cost tool, and optimization algorithms. The optimization tool was then applied in AXL watershed to explore optimization focusing on critical areas (top 25% of areas with highest runoff volume/pollutant loads per area) vs. all areas of the watershed, optimization using weather data for spring (March to July, due to the goal of reducing spring phosphorus in watershed management plan) vs. full year, and optimization results of implementing BMPs to achieve the watershed management plan goal (reducing 2008 TP levels by 40%). The optimization tool and BMP optimization results can be used by watershed groups and communities to solve hydrology and water quality problems.

An improved model for the oPtImal Measurement Probes Allocation tool

International Nuclear Information System (INIS)

Sterle, C.; Neto, A.C.; De Tommasi, G.

2015-01-01

Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

An improved model for the oPtImal Measurement Probes Allocation tool

Energy Technology Data Exchange (ETDEWEB)

Sterle, C., E-mail: claudio.sterle@unina.it [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy); Neto, A.C. [Fusion for Energy, 08019 Barcelona (Spain); De Tommasi, G. [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy)

2015-10-15

Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Wanxing Sheng

2013-01-01

Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

Muon tomography imaging improvement using optimized limited angle data

Science.gov (United States)

Bai, Chuanyong; Simon, Sean; Kindem, Joel; Luo, Weidong; Sossong, Michael J.; Steiger, Matthew

2014-05-01

Image resolution of muon tomography is limited by the range of zenith angles of cosmic ray muons and the flux rate at sea level. Low flux rate limits the use of advanced data rebinning and processing techniques to improve image quality. By optimizing the limited angle data, however, image resolution can be improved. To demonstrate the idea, physical data of tungsten blocks were acquired on a muon tomography system. The angular distribution and energy spectrum of muons measured on the system was also used to generate simulation data of tungsten blocks of different arrangement (geometry). The data were grouped into subsets using the zenith angle and volume images were reconstructed from the data subsets using two algorithms. One was a distributed PoCA (point of closest approach) algorithm and the other was an accelerated iterative maximal likelihood/expectation maximization (MLEM) algorithm. Image resolution was compared for different subsets. Results showed that image resolution was better in the vertical direction for subsets with greater zenith angles and better in the horizontal plane for subsets with smaller zenith angles. The overall image resolution appeared to be the compromise of that of different subsets. This work suggests that the acquired data can be grouped into different limited angle data subsets for optimized image resolution in desired directions. Use of multiple images with resolution optimized in different directions can improve overall imaging fidelity and the intended applications.

An improved hierarchical A * algorithm in the optimization of parking lots

Science.gov (United States)

Wang, Yong; Wu, Junjuan; Wang, Ying

2017-08-01

In the parking lot parking path optimization, the traditional evaluation index is the shortest distance as the best index and it does not consider the actual road conditions. Now, the introduction of a more practical evaluation index can not only simplify the hardware design of the boot system but also save the software overhead. Firstly, we establish the parking lot network graph RPCDV mathematical model and all nodes in the network is divided into two layers which were constructed using different evaluation function base on the improved hierarchical A * algorithm which improves the time optimal path search efficiency and search precision of the evaluation index. The final results show that for different sections of the program attribute parameter algorithm always faster the time to find the optimal path.

Improved Taguchi method based contract capacity optimization for industrial consumer with self-owned generating units

International Nuclear Information System (INIS)

Yang, Hong-Tzer; Peng, Pai-Chun

2012-01-01

Highlights: ► We propose an improved Taguchi method to determine the optimal contract capacities with SOGUs. ► We solve the highly discrete and nonlinear optimization problem for the contract capacities with SOGUs. ► The proposed improved Taguchi method integrates PSO in Taguchi method. ► The customer using the proposed optimization approach may save up to 12.18% of power expenses. ► The improved Taguchi method can also be well applied to the other similar problems. - Abstract: Contract capacity setting for industrial consumer with self-owned generating units (SOGUs) is a highly discrete and nonlinear optimization problem considering expenditure on the electricity from the utility and operation costs of the SOGUs. This paper proposes an improved Taguchi method that combines existing Taguchi method and particle swarm optimization (PSO) algorithm to solve this problem. Taguchi method provides fast converging characteristics in searching the optimal solution through quality analysis in orthogonal matrices. The integrated PSO algorithm generates new solutions in the orthogonal matrices based on the searching experiences during the evolution process to further improve the quality of solution. To verify feasibility of the proposed method, the paper uses the real data obtained from a large optoelectronics factory in Taiwan. In comparison with the existing optimization methods, the proposed improved Taguchi method has superior performance as revealed in the numerical results in terms of the convergence process and the quality of solution obtained.

Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

Science.gov (United States)

Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka

2014-01-01

A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants. PMID:24723827

Improving intensity-modulated radiation therapy using the anatomic beam orientation optimization algorithm

International Nuclear Information System (INIS)

Potrebko, Peter S.; McCurdy, Boyd M. C.; Butler, James B.; El-Gubtan, Adel S.

2008-01-01

A novel, anatomic beam orientation optimization (A-BOO) algorithm is proposed to significantly improve conventional intensity-modulated radiation therapy (IMRT). The A-BOO algorithm vectorially analyses polygonal surface mesh data of contoured patient anatomy. Five optimal (5-opt) deliverable beam orientations are selected based on (1) tangential orientation bisecting the target and adjacent organ's-at-risk (OARs) to produce precipitous dose gradients between them and (2) parallel incidence with polygon features of the target volume to facilitate conformal coverage. The 5-opt plans were compared to standard five, seven, and nine equiangular-spaced beam plans (5-equi, 7-equi, 9-equi) for: (1) gastric, (2) Radiation Therapy Oncology Group (RTOG) P-0126 prostate, and (3) RTOG H-0022 oropharyngeal (stage-III, IV) cancer patients. In the gastric case, the noncoplanar 5-opt plan reduced the right kidney V 20 Gy by 32.2%, 23.2%, and 20.6% compared to plans with five, seven, and nine equiangular-spaced beams. In the prostate case, the coplanar 5-opt plan produced similar rectal sparing as the 7-equi and 9-equi plans with a reduction of the V 75, V 70, V 65, and V 60 Gy of 2.4%, 5.3%, 7.0%, and 9.5% compared to the 5-equi plan. In the stage-III and IV oropharyngeal cases, the noncoplanar 5-opt plan substantially reduced the V 30 Gy and mean dose to the contralateral parotid compared to plans with five, seven, and nine equiangular-spaced beams: (stage-III) 7.1%, 5.2%, 6.8%, and 5.1, 3.5, 3.7 Gy and (stage-IV) 10.2%, 10.2%, 9.8% and 7.0, 7.1, 7.2 Gy. The geometry-based A-BOO algorithm has been demonstrated to be robust for application to a variety of IMRT treatment sites. Beam orientations producing significant improvements in OAR sparing over conventional IMRT can be automatically produced in minutes compared to hours with existing dose-based beam orientation optimization methods

Improved helicopter aeromechanical stability analysis using segmented constrained layer damping and hybrid optimization

Science.gov (United States)

Liu, Qiang; Chattopadhyay, Aditi

2000-06-01

Aeromechanical stability plays a critical role in helicopter design and lead-lag damping is crucial to this design. In this paper, the use of segmented constrained damping layer (SCL) treatment and composite tailoring is investigated for improved rotor aeromechanical stability using formal optimization technique. The principal load-carrying member in the rotor blade is represented by a composite box beam, of arbitrary thickness, with surface bonded SCLs. A comprehensive theory is used to model the smart box beam. A ground resonance analysis model and an air resonance analysis model are implemented in the rotor blade built around the composite box beam with SCLs. The Pitt-Peters dynamic inflow model is used in air resonance analysis under hover condition. A hybrid optimization technique is used to investigate the optimum design of the composite box beam with surface bonded SCLs for improved damping characteristics. Parameters such as stacking sequence of the composite laminates and placement of SCLs are used as design variables. Detailed numerical studies are presented for aeromechanical stability analysis. It is shown that optimum blade design yields significant increase in rotor lead-lag regressive modal damping compared to the initial system.

Cast Off expansion plan by rapid improvement through Optimization tool design, Tool Parameters and using Six Sigma’s ECRS Technique

Science.gov (United States)

Gopalakrishnan, T.; Saravanan, R.

2017-03-01

Powerful management concepts step-up the quality of the product, time saving in producing the product thereby increase the production rate, improves tools and techniques, work culture, work place and employee motivation and morale. In this paper discussed about the case study of optimizing the tool design, tool parameters to cast off expansion plan according ECRS technique. The proposed designs and optimal tool parameters yielded best results and meet the customer demand without expansion plan. Hence the work yielded huge savings of money (direct and indirect cost), time and improved the motivation and more of employees significantly.

An Improved Bacterial-Foraging Optimization-Based Machine Learning Framework for Predicting the Severity of Somatization Disorder

Directory of Open Access Journals (Sweden)

Xinen Lv

2018-02-01

Full Text Available It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO to optimize two key parameters (penalty coefficient and the kernel width of a kernel extreme learning machine (KELM and build an IBFO-based KELM (IBFO-KELM for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90 is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model, GA-KELM (model based on genetic algorithm, PSO-KELM (model based on particle swarm optimization algorithm and Grid-KELM (model based on grid search method. The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC, sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.

On-line optimal control improves gas processing

International Nuclear Information System (INIS)

Berkowitz, P.N.; Papadopoulos, M.N.

1992-01-01

This paper reports that the authors' companies jointly funded the first phase of a gas processing liquids optimization project that has the specific purposes to: Improve the return of processing natural gas liquids, Develop sets of control algorithms, Make available a low-cost solution suitable for small to medium-sized gas processing plants, Test and demonstrate the feasibility of line control. The ARCO Willard CO 2 gas recovery processing plant was chosen as the initial test site to demonstrate the application of multivariable on-line optimal control. One objective of this project is to support an R ampersand D effort to provide a standardized solution to the various types of gas processing plants in the U.S. Processes involved in these gas plants include cryogenic separations, demethanization, lean oil absorption, fractionation and gas treating. Next, the proposed solutions had to be simple yet comprehensive enough to allow an operator to maintain product specifications while operating over a wide range of gas input flow and composition. This had to be a supervisors system that remained on-line more than 95% of the time, and achieved reduced plant operating variability and improved variable cost control. It took more than a year to study various gas processes and to develop a control approach before a real application was finally exercised. An initial process for C 2 and CO 2 recoveries was chosen

Improved quantum-behaved particle swarm optimization with local search strategy

Directory of Open Access Journals (Sweden)

Maolong Xi

2017-03-01

Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization

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Xing Xu

2014-04-01

Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.

An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism

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Chuncai Xiao

2015-01-01

Full Text Available Fruit fly optimization algorithm (FOA invented recently is a new swarm intelligence method based on fruit fly’s foraging behaviors and has been shown to be competitive with existing evolutionary algorithms, such as particle swarm optimization (PSO algorithm. However, there are still some disadvantages in the FOA, such as low convergence precision, easily trapped in a local optimum value at the later evolution stage. This paper presents an improved FOA based on the cell communication mechanism (CFOA, by considering the information of the global worst, mean, and best solutions into the search strategy to improve the exploitation. The results from a set of numerical benchmark functions show that the CFOA outperforms the FOA and the PSO in most of the experiments. Further, the CFOA is applied to optimize the controller for preoxidation furnaces in carbon fibers production. Simulation results demonstrate the effectiveness of the CFOA.

Optimization of photonic crystal cavities

DEFF Research Database (Denmark)

Wang, Fengwen; Sigmund, Ole

2017-01-01

We present optimization of photonic crystal cavities. The optimization problem is formulated to maximize the Purcell factor of a photonic crystal cavity. Both topology optimization and air-hole-based shape optimization are utilized for the design process. Numerical results demonstrate...... that the Purcell factor of the photonic crystal cavity can be significantly improved through optimization....

An Improved Fruit Fly Optimization Algorithm and Its Application in Heat Exchange Fouling Ultrasonic Detection

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Xia Li

2018-01-01

Full Text Available Inspired by the basic theory of Fruit Fly Optimization Algorithm, in this paper, cat mapping was added to the original algorithm, and the individual distribution and evolution mechanism of fruit fly population were improved in order to increase the search speed and accuracy. The flowchart of the improved algorithm was drawn to show its procedure. Using classical test functions, simulation optimization results show that the improved algorithm has faster and more reliable optimization ability. The algorithm was then combined with sparse decomposition theory and used in processing fouling detection ultrasonic signals to verify the validity and practicability of the improved algorithm.

Performance improvement of optical CDMA networks with stochastic artificial bee colony optimization technique

Science.gov (United States)

Panda, Satyasen

2018-05-01

This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.

Structural optimization of Au–Pd bimetallic nanoparticles with improved particle swarm optimization method

International Nuclear Information System (INIS)

Shao Gui-Fang; Zhu Meng; Shangguan Ya-Li; Li Wen-Ran; Zhang Can; Wang Wei-Wei; Li Ling

2017-01-01

Due to the dependence of the chemical and physical properties of the bimetallic nanoparticles (NPs) on their structures, a fundamental understanding of their structural characteristics is crucial for their syntheses and wide applications. In this article, a systematical atomic-level investigation of Au–Pd bimetallic NPs is conducted by using the improved particle swarm optimization (IPSO) with quantum correction Sutton–Chen potentials (Q-SC) at different Au/Pd ratios and different sizes. In the IPSO, the simulated annealing is introduced into the classical particle swarm optimization (PSO) to improve the effectiveness and reliability. In addition, the influences of initial structure, particle size and composition on structural stability and structural features are also studied. The simulation results reveal that the initial structures have little effects on the stable structures, but influence the converging rate greatly, and the convergence rate of the mixing initial structure is clearly faster than those of the core-shell and phase structures. We find that the Au–Pd NPs prefer the structures with Au-rich in the outer layers while Pd-rich in the inner ones. Especially, when the Au/Pd ratio is 6:4, the structure of the nanoparticle (NP) presents a standardized Pd core Au shell structure. (paper)

Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

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Lei Wang

2017-01-01

Full Text Available As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

An improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

Energy Technology Data Exchange (ETDEWEB)

Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S. [Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

2011-02-15

Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems. (author)

An Improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

International Nuclear Information System (INIS)

Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S.

2011-01-01

Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems.

A study on the improvement of shape optimization associated with the modification of a finite element

International Nuclear Information System (INIS)

Sung, Jin Il; Yoo, Jeong Hoon

2002-01-01

In this paper, we investigate the effect and the importance of the accuracy of finite element analysis in the shape optimization based on the finite element method and improve the existing finite element which has inaccuracy in some cases. And then, the shape optimization is performed by using the improved finite element. One of the main stream to improve finite element is the prevention of locking phenomenon. In case of bending dominant problems, finite element solutions cannot be reliable because of shear locking phenomenon. In the process of shape optimization, the mesh distortion is large due to the change of the structure outline. So, we have to raise the accuracy of finite element analysis for the large mesh distortion. We cannot guarantee the accurate result unless the finite element itself is accurate or the finite elements are remeshed. So, we approach to more accurate shape optimization to diminish these inaccuracies by improving the existing finite element. The shape optimization using the modified finite element is applied to a two and three dimensional simple beam. Results show that the modified finite element has improved the optimization results

Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization

International Nuclear Information System (INIS)

Chen, Syuan-Yi; Hung, Yi-Hsuan; Wu, Chien-Hsun; Huang, Siang-Ting

2015-01-01

Highlights: • Online sub-optimal energy management using IPSO. • A second-order HEV model with 5 major segments was built. • IPSO with equivalent-fuel fitness function using 5 particles. • Engine, rule-based control, PSO, IPSO and ECMS are compared. • Max. 31+% fuel economy and 56+% energy consumption improved. - Abstract: This study developed an online suboptimal energy management system by using improved particle swarm optimization (IPSO) for engine/motor hybrid electric vehicles. The vehicle was modeled on the basis of second-order dynamics, and featured five major segments: a battery, a spark ignition engine, a lithium battery, transmission and vehicle dynamics, and a driver model. To manage the power distribution of dual power sources, the IPSO was equipped with three inputs (rotational speed, battery state-of-charge, and demanded torque) and one output (power split ratio). Five steps were developed for IPSO: (1) initialization; (2) determination of the fitness function; (3) selection and memorization; (4) modification of position and velocity; and (5) a stopping rule. Equivalent fuel consumption by the engine and motor was used as the fitness function with five particles, and the IPSO-based vehicle control unit was completed and integrated with the vehicle simulator. To quantify the energy improvement of IPSO, a four-mode rule-based control (system ready, motor only, engine only, and hybrid modes) was designed according to the engine efficiency and rotational speed. A three-loop Equivalent Consumption Minimization Strategy (ECMS) was coded as the best case. The simulation results revealed that IPSO searches the optimal solution more efficiently than conventional PSO does. In two standard driving cycles, ECE and FTP, the improvements in the equivalent fuel consumption and energy consumption compared to baseline were (24.25%, 45.27%) and (31.85%, 56.41%), respectively, for the IPSO. The CO_2 emission for all five cases (pure engine, rule-based, PSO

Optimal production scheduling for energy efficiency improvement in biofuel feedstock preprocessing considering work-in-process particle separation

International Nuclear Information System (INIS)

Li, Lin; Sun, Zeyi; Yao, Xufeng; Wang, Donghai

2016-01-01

Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing. - Highlights: • A novel method to schedule production in biofuel feedstock preprocessing process. • Systems modeling approach is used. • Capable of optimize preprocessing to reduce energy waste and improve energy efficiency. • A numerical case is used to illustrate the effectiveness of the method. • Energy consumption per unit production can be significantly reduced.

Optimization of factors to obtain cassava starch films with improved mechanical properties

Science.gov (United States)

Monteiro, Mayra; Oliveira, Victor; Santos, Francisco; Barros Neto, Eduardo; Silva, Karyn; Silva, Rayane; Henrique, João; Chibério, Abimaelle

2017-08-01

In this study, was investigated the optimization of the factors that significantly influenced the mechanical property improvement of cassava starch films through complete factorial design 23. The factors to be analyzed were cassava starch, glycerol and modified clay contents. A regression model was proposed by the factorial analysis, aiming to estimate the condition of the individual factors investigated in the optimum state of the mechanical properties of the biofilm, using the following statistical tool: desirability function and response surface. The response variable that delimits the improvement of the mechanical property of the biofilm is the tensile strength, such improvement is obtained by maximizing the response variable. The factorial analysis showed that the best combination of factor configurations to reach the best response was found to be: with 5g of cassava starch, 10% of glycerol and 5% of modified clay, both percentages in relation to the dry mass of starch used. In addition, the starch biofilm showing the lowest response contained 2g of cassava starch, 0% of modified clay and 30% of glycerol, and was consequently considered the worst biofilm.

An efficient optimization method to improve the measuring accuracy of oxygen saturation by using triangular wave optical signal

Science.gov (United States)

Li, Gang; Yu, Yue; Zhang, Cui; Lin, Ling

2017-09-01

The oxygen saturation is one of the important parameters to evaluate human health. This paper presents an efficient optimization method that can improve the accuracy of oxygen saturation measurement, which employs an optical frequency division triangular wave signal as the excitation signal to obtain dynamic spectrum and calculate oxygen saturation. In comparison to the traditional method measured RMSE (root mean square error) of SpO2 which is 0.1705, this proposed method significantly reduced the measured RMSE which is 0.0965. It is notable that the accuracy of oxygen saturation measurement has been improved significantly. The method can simplify the circuit and bring down the demand of elements. Furthermore, it has a great reference value on improving the signal to noise ratio of other physiological signals.

Structural Performance Optimization and Verification of an Improved Thin-Walled Storage Tank for a Pico-Satellite

Directory of Open Access Journals (Sweden)

Lai Teng

2017-11-01

Full Text Available This paper presents an improved mesh storage tank structure obtained using 3D metal printing. The storage tank structure is optimized using a multi-objective uniform design method. Each parameter influencing the storage tank is considered as the optimization factor, and the compression stress ( σ , volume utilization ratio ( v , and weight ( m , are considered as the optimization objectives. Regression equations were established between the optimization factors and targets, the orders of the six factors affecting three target values are analyzed, and the relative deviations between the regression equation and calculation results for σ , v , and m were 9.72%, 4.15%, and 2.94%, respectively. The optimization results showed that the regression equations can predict the structure performance of the improved storage tank, and the values of the influence factors obtained through the optimization are effective. In addition, the compression stress was improved by 24.98%, the volume utilization ratio was increased by 26.86%, and the weight was reduced by 26.83%. The optimized storage tank was developed through 3D metal printing, and the compressive stress was improved by 58.71%, the volume utilization ratio was increased by 24.52%, and the weight was reduced by 11.67%.

Design and optimization of self-nanoemulsifying drug delivery systems for improved bioavailability of cyclovirobuxine D

Directory of Open Access Journals (Sweden)

Ke ZC

2016-06-01

Full Text Available Zhongcheng Ke,1–3 Xuefeng Hou,4 Xiao-bin Jia31Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 2Huangshan University, Huangshan, Anhui, 3Third Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 4Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of ChinaBackground: The main purpose of this research was to design a self-nanoemulsifying drug delivery system (SNEDDS for improving the bioavailability of cyclovirobuxine D as a poorly water-soluble drug.Materials and methods: Solubility trials, emulsifying studies, and pseudo-ternary phase diagrams were used to screen the SNEDDS formulations. The optimized drug-loaded SNEDDS was prepared at a mass ratio of 3:24:38:38 for cyclovirobuxine D, oleic acid, Solutol SH15, and propylene glycol, respectively. The optimized formulation was characterized in terms of physicochemical and pharmacokinetic parameters compared with marketed cyclovirobuxine D tablets.Results: The optimized cyclovirobuxine-D-loaded SNEDDS was spontaneously dispersed to form a nanoemulsion with a globule size of 64.80±3.58 nm, which exhibited significant improvement of drug solubility, rapid absorption rate, and enhanced area under the curve, together with increased permeation and decreased efflux. Fortunately, there was a nonsignificant cytotoxic effect toward Caco-2 cells. The relative bioavailability of SNEDDS was 200.22% in comparison with market tablets, in rabbits.Conclusion: SNEDDS could be a potential candidate for an oral dosage form of cyclovirobuxine D with improved bioavailability.Keywords: self-nanoemulsifying drug delivery, bioavailability, cyclovirobuxine D

Optimizing perioperative decision making: improved information for clinical workflow planning.

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Doebbeling, Bradley N; Burton, Matthew M; Wiebke, Eric A; Miller, Spencer; Baxter, Laurence; Miller, Donald; Alvarez, Jorge; Pekny, Joseph

2012-01-01

Perioperative care is complex and involves multiple interconnected subsystems. Delayed starts, prolonged cases and overtime are common. Surgical procedures account for 40-70% of hospital revenues and 30-40% of total costs. Most planning and scheduling in healthcare is done without modern planning tools, which have potential for improving access by assisting in operations planning support. We identified key planning scenarios of interest to perioperative leaders, in order to examine the feasibility of applying combinatorial optimization software solving some of those planning issues in the operative setting. Perioperative leaders desire a broad range of tools for planning and assessing alternate solutions. Our modeled solutions generated feasible solutions that varied as expected, based on resource and policy assumptions and found better utilization of scarce resources. Combinatorial optimization modeling can effectively evaluate alternatives to support key decisions for planning clinical workflow and improving care efficiency and satisfaction.

Design and optimization of automotive thermoelectric generators for maximum fuel efficiency improvement

International Nuclear Information System (INIS)

Kempf, Nicholas; Zhang, Yanliang

2016-01-01

Highlights: • A three-dimensional automotive thermoelectric generator (TEG) model is developed. • Heat exchanger design and TEG configuration are optimized for maximum fuel efficiency increase. • Heat exchanger conductivity has a strong influence on maximum fuel efficiency increase. • TEG aspect ratio and fin height increase with heat exchanger thermal conductivity. • A 2.5% fuel efficiency increase is attainable with nanostructured half-Heusler modules. - Abstract: Automotive fuel efficiency can be increased by thermoelectric power generation using exhaust waste heat. A high-temperature thermoelectric generator (TEG) that converts engine exhaust waste heat into electricity is simulated based on a light-duty passenger vehicle with a 4-cylinder gasoline engine. Strategies to optimize TEG configuration and heat exchanger design for maximum fuel efficiency improvement are provided. Through comparison of stainless steel and silicon carbide heat exchangers, it is found that both the optimal TEG design and the maximum fuel efficiency increase are highly dependent on the thermal conductivity of the heat exchanger material. Significantly higher fuel efficiency increase can be obtained using silicon carbide heat exchangers at taller fins and a longer TEG along the exhaust flow direction when compared to stainless steel heat exchangers. Accounting for major parasitic losses, a maximum fuel efficiency increase of 2.5% is achievable using newly developed nanostructured bulk half-Heusler thermoelectric modules.

Improvement of Power Flow Calculation with Optimization Factor Based on Current Injection Method

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Lei Wang

2014-01-01

Full Text Available This paper presents an improvement in power flow calculation based on current injection method by introducing optimization factor. In the method proposed by this paper, the PQ buses are represented by current mismatches while the PV buses are represented by power mismatches. It is different from the representations in conventional current injection power flow equations. By using the combined power and current injection mismatches method, the number of the equations required can be decreased to only one for each PV bus. The optimization factor is used to improve the iteration process and to ensure the effectiveness of the improved method proposed when the system is ill-conditioned. To verify the effectiveness of the method, the IEEE test systems are tested by conventional current injection method and the improved method proposed separately. Then the results are compared. The comparisons show that the optimization factor improves the convergence character effectively, especially that when the system is at high loading level and R/X ratio, the iteration number is one or two times less than the conventional current injection method. When the overloading condition of the system is serious, the iteration number in this paper appears 4 times less than the conventional current injection method.

A new improved artificial bee colony algorithm for ship hull form optimization

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Huang, Fuxin; Wang, Lijue; Yang, Chi

2016-04-01

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

Fabrication and Optimization of Self-Microemulsions to Improve the Oral Bioavailability of Total Flavones of Hippophaë rhamnoides L.

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Guo, Ruixue; Guo, Xinbo; Hu, Xiaodan; Abbasi, Arshad Mehmood; Zhou, Lin; Li, Tong; Fu, Xiong; Liu, Rui Hai

2017-12-01

The purpose of this work was to improve the oral bioavailability of a poorly soluble functional food ingredient, the total flavones of Hippophaë rhamnoides L. (TFH). A self-microemulsion drug delivery system (SMEDDS) was developed to overcome the problems of poor absorption of TFH in vivo. The optimal SMEDDS significantly enhanced the solubility of TFH up to 530 times compared to that in water. The mean droplet size was 61.76 nm with uniform distribution. And the loaded system was stable at 25 °C for 3 mo with transparent appearance. The in vitro release of TFH from SMEDDS was faster and more complete than that from suspension. After oral administration of TFH-SMEDDS in rats, the relative bioavailability of TFH was dramatically improved for 3.09 times compared with the unencapsulated form. The investigation indicated the potential application of SMEDDS as a vehicle to improve the oral bioavailability of TFH. The lipid-based nanotechnology, namely self-microemulsion drug delivery system (SMEDDS) was used to improve the bioavailability and oral delivery of total flavones of Hippophaë rhamnoides L. (TFH). The relevant bioavailability of TFH could be remarkably 3-fold improved by the optimized SMEDDS. The SMEDDS produced via a simple one-step process for poorly soluble TFH to achieve a significant improvement in the bioavailability, may endorse the promising utilization of TFH in functional foods as well as pharmaceutical fields with an enhanced absorption in vivo. © 2017 Institute of Food Technologists®.

Optimal Seamline Detection for Orthoimage Mosaicking Based on DSM and Improved JPS Algorithm

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Gang Chen

2018-05-01

Full Text Available Based on the digital surface model (DSM and jump point search (JPS algorithm, this study proposed a novel approach to detect the optimal seamline for orthoimage mosaicking. By threshold segmentation, DSM was first identified as ground regions and obstacle regions (e.g., buildings, trees, and cars. Then, the mathematical morphology method was used to make the edge of obstacles more prominent. Subsequently, the processed DSM was considered as a uniform-cost grid map, and the JPS algorithm was improved and employed to search for key jump points in the map. Meanwhile, the jump points would be evaluated according to an optimized function, finally generating a minimum cost path as the optimal seamline. Furthermore, the search strategy was modified to avoid search failure when the search map was completely blocked by obstacles in the search direction. Comparison of the proposed method and the Dijkstra’s algorithm was carried out based on two groups of image data with different characteristics. Results showed the following: (1 the proposed method could detect better seamlines near the centerlines of the overlap regions, crossing far fewer ground objects; (2 the efficiency and resource consumption were greatly improved since the improved JPS algorithm skips many image pixels without them being explicitly evaluated. In general, based on DSM, the proposed method combining threshold segmentation, mathematical morphology, and improved JPS algorithms was helpful for detecting the optimal seamline for orthoimage mosaicking.

Improved Sensitivity Relations in State Constrained Optimal Control

International Nuclear Information System (INIS)

Bettiol, Piernicola; Frankowska, Hélène; Vinter, Richard B.

2015-01-01

Sensitivity relations in optimal control provide an interpretation of the costate trajectory and the Hamiltonian, evaluated along an optimal trajectory, in terms of gradients of the value function. While sensitivity relations are a straightforward consequence of standard transversality conditions for state constraint free optimal control problems formulated in terms of control-dependent differential equations with smooth data, their verification for problems with either pathwise state constraints, nonsmooth data, or for problems where the dynamic constraint takes the form of a differential inclusion, requires careful analysis. In this paper we establish validity of both ‘full’ and ‘partial’ sensitivity relations for an adjoint state of the maximum principle, for optimal control problems with pathwise state constraints, where the underlying control system is described by a differential inclusion. The partial sensitivity relation interprets the costate in terms of partial Clarke subgradients of the value function with respect to the state variable, while the full sensitivity relation interprets the couple, comprising the costate and Hamiltonian, as the Clarke subgradient of the value function with respect to both time and state variables. These relations are distinct because, for nonsmooth data, the partial Clarke subdifferential does not coincide with the projection of the (full) Clarke subdifferential on the relevant coordinate space. We show for the first time (even for problems without state constraints) that a costate trajectory can be chosen to satisfy the partial and full sensitivity relations simultaneously. The partial sensitivity relation in this paper is new for state constraint problems, while the full sensitivity relation improves on earlier results in the literature (for optimal control problems formulated in terms of Lipschitz continuous multifunctions), because a less restrictive inward pointing hypothesis is invoked in the proof, and because

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

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Yue Wu

2017-01-01

Full Text Available Firefly Algorithm (FA, for short is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly, development of structural topology optimization method and the basic principle of standard FA are introduced in detail. Then, in order to apply the algorithm to optimization problems with discrete variables, the initial positions of fireflies and the position updating formula are discretized. By embedding the random-weight and enhancing the attractiveness, the performance of this algorithm is improved, and thus an Improved Firefly Algorithm (IFA, for short is proposed. Furthermore, using size variables which are capable of including topology variables and size and topology optimization for trusses with discrete variables is formulated based on the Ground Structure Approach. The essential techniques of variable elastic modulus technology and geometric construction analysis are applied in the structural analysis process. Subsequently, an optimization method for the size and topological design of trusses based on the IFA is introduced. Finally, two numerical examples are shown to verify the feasibility and efficiency of the proposed method by comparing with different deterministic methods.

Panorama parking assistant system with improved particle swarm optimization method

Science.gov (United States)

Cheng, Ruzhong; Zhao, Yong; Li, Zhichao; Jiang, Weigang; Wang, Xin'an; Xu, Yong

2013-10-01

A panorama parking assistant system (PPAS) for the automotive aftermarket together with a practical improved particle swarm optimization method (IPSO) are proposed in this paper. In the PPAS system, four fisheye cameras are installed in the vehicle with different views, and four channels of video frames captured by the cameras are processed as a 360-deg top-view image around the vehicle. Besides the embedded design of PPAS, the key problem for image distortion correction and mosaicking is the efficiency of parameter optimization in the process of camera calibration. In order to address this problem, an IPSO method is proposed. Compared with other parameter optimization methods, the proposed method allows a certain range of dynamic change for the intrinsic and extrinsic parameters, and can exploit only one reference image to complete all of the optimization; therefore, the efficiency of the whole camera calibration is increased. The PPAS is commercially available, and the IPSO method is a highly practical way to increase the efficiency of the installation and the calibration of PPAS in automobile 4S shops.

Performance improvements of binary diffractive structures via optimization of the photolithography and dry etch processes

Science.gov (United States)

Welch, Kevin; Leonard, Jerry; Jones, Richard D.

2010-08-01

Increasingly stringent requirements on the performance of diffractive optical elements (DOEs) used in wafer scanner illumination systems are driving continuous improvements in their associated manufacturing processes. Specifically, these processes are designed to improve the output pattern uniformity of off-axis illumination systems to minimize degradation in the ultimate imaging performance of a lithographic tool. In this paper, we discuss performance improvements in both photolithographic patterning and RIE etching of fused silica diffractive optical structures. In summary, optimized photolithographic processes were developed to increase critical dimension uniformity and featuresize linearity across the substrate. The photoresist film thickness was also optimized for integration with an improved etch process. This etch process was itself optimized for pattern transfer fidelity, sidewall profile (wall angle, trench bottom flatness), and across-wafer etch depth uniformity. Improvements observed with these processes on idealized test structures (for ease of analysis) led to their implementation in product flows, with comparable increases in performance and yield on customer designs.

Opposition-Based Improved PSO for Optimal Reactive Power Dispatch and Voltage Control

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Shengrang Cao

2015-01-01

Full Text Available An opposition-based improved particle swarm optimization algorithm (OIPSO is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation.

Significant improvement in the thermal annealing process of optical resonators

Science.gov (United States)

Salzenstein, Patrice; Zarubin, Mikhail

2017-05-01

Thermal annealing performed during process improves the quality of the roughness of optical resonators reducing stresses at the periphery of their surface thus allowing higher Q-factors. After a preliminary realization, the design of the oven and the electronic method were significantly improved thanks to nichrome resistant alloy wires and chopped basalt fibers for thermal isolation during the annealing process. Q-factors can then be improved.

Single-source dual-energy spectral multidetector CT of pancreatic adenocarcinoma: Optimization of energy level viewing significantly increases lesion contrast

International Nuclear Information System (INIS)

Patel, B.N.; Thomas, J.V.; Lockhart, M.E.; Berland, L.L.; Morgan, D.E.

2013-01-01

V was 31 ± 25 HU (p = 0.007). Conclusion: Significantly increased pancreatic lesion contrast was noted at lower viewing energies using spectral MDCT. Individual patient CNR-optimized energy level images have the potential to improve lesion conspicuity.

Optimization of microtubule affinity regulating kinase (MARK) inhibitors with improved physical properties

Energy Technology Data Exchange (ETDEWEB)

Sloman, David L.; Noucti, Njamkou; Altman, Michael D.; Chen, Dapeng; Mislak, Andrea C.; Szewczak, Alexander; Hayashi, Mansuo; Warren, Lee; Dellovade, Tammy; Wu, Zhenhua; Marcus, Jacob; Walker, Deborah; Su, Hua-Poo; Edavettal, Suzanne C.; Munshi, Sanjeev; Hutton, Michael; Nuthall, Hugh; Stanton, Matthew G. (Merck)

2016-09-01

Inhibition of microtubule affinity regulating kinase (MARK) represents a potentially attractive means of arresting neurofibrillary tangle pathology in Alzheimer’s disease. This manuscript outlines efforts to optimize a pyrazolopyrimidine series of MARK inhibitors by focusing on improvements in potency, physical properties and attributes amenable to CNS penetration. A unique cylcyclohexyldiamine scaffold was identified that led to remarkable improvements in potency, opening up opportunities to reduce MW, Pgp efflux and improve pharmacokinetic properties while also conferring improved solubility.

Omega-3 fatty acid therapy dose-dependently and significantly decreased triglycerides and improved flow-mediated dilation, however, did not significantly improve insulin sensitivity in patients with hypertriglyceridemia.

Science.gov (United States)

Oh, Pyung Chun; Koh, Kwang Kon; Sakuma, Ichiro; Lim, Soo; Lee, Yonghee; Lee, Seungik; Lee, Kyounghoon; Han, Seung Hwan; Shin, Eak Kyun

2014-10-20

Experimental studies demonstrate that higher intake of omega-3 fatty acids (n-3 FA) improves insulin sensitivity, however, we reported that n-3 FA 2g therapy, most commonly used dosage did not significantly improve insulin sensitivity despite reducing triglycerides by 21% in patients. Therefore, we investigated the effects of different dosages of n-3 FA in patients with hypertriglyceridemia. This was a randomized, single-blind, placebo-controlled, parallel study. Age, sex, and body mass index were matched among groups. All patients were recommended to maintain a low fat diet. Forty-four patients (about 18 had metabolic syndrome/type 2 diabetes mellitus) in each group were given placebo, n-3 FA 1 (O1), 2 (O2), or 4 g (O4), respectively daily for 2 months. n-3 FA therapy dose-dependently and significantly decreased triglycerides and triglycerides/HDL cholesterol and improved flow-mediated dilation, compared with placebo (by ANOVA). However, each n-3 FA therapy did not significantly decrease high-sensitivity C-reactive protein and fibrinogen, compared with placebo. O1 significantly increased insulin levels and decreased insulin sensitivity (determined by QUICKI) and O2 significantly decreased plasma adiponectin levels relative to baseline measurements. Of note, when compared with placebo, each n-3 FA therapy did not significantly change insulin, glucose, adiponectin, glycated hemoglobin levels and insulin sensitivity (by ANOVA). We observed similar results in a subgroup of patients with the metabolic syndrome. n-3 FA therapy dose-dependently and significantly decreased triglycerides and improved flow-mediated dilation. Nonetheless, n-3 FA therapy did not significantly improve acute-phase reactants and insulin sensitivity in patients with hypertriglyceridemia, regardless of dosages. Copyright © 2014. Published by Elsevier Ireland Ltd.

Simultaneous allocation of distributed resources using improved teaching learning based optimization

International Nuclear Information System (INIS)

Kanwar, Neeraj; Gupta, Nikhil; Niazi, K.R.; Swarnkar, Anil

2015-01-01

Highlights: • Simultaneous allocation of distributed energy resources in distribution networks. • Annual energy loss reduction is optimized using a multi-level load profile. • A new penalty factor approach is suggested to check node voltage deviations. • An improved TLBO is proposed by suggesting several modifications in standard TLBO. • An intelligent search is proposed to enhance the performance of solution technique. - Abstract: Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of Teaching Learning Based Optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results

Localized probability of improvement for kriging based multi-objective optimization

Science.gov (United States)

Li, Yinjiang; Xiao, Song; Barba, Paolo Di; Rotaru, Mihai; Sykulski, Jan K.

2017-12-01

The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

Optimization of CW-OSL parameters for improved dose detection threshold in Al2O3:C

International Nuclear Information System (INIS)

Rawat, N.S.; Dhabekar, B.; Kulkarni, M.S.; Muthe, K.P.; Mishra, D.R.; Soni, A.; Gupta, S.K.; Babu, D.A.R.

2014-01-01

Continuous wave optically stimulated luminescence (CW-OSL) is relatively a simple technique that offers good signal to noise ratio (SNR) and involves simple instrumentation. This study reports the influence and optimization of CW-OSL parameters on minimum detectable dose (MDD) using α-Al 2 O 3 :C phosphor. It is found that at a given stimulation intensity MDD in CW-OSL mode depends on signal integration time. At lower integration times MDD is inferior. It exhibits an improvement for intermediate values, shows a plateau region and deteriorates as integration time increases further. MDD is found to be ∼127 μGy at 4 mW/cm 2 stimulation intensity for integration time of 0.1 s, which improves to ∼10.5 μGy for 60 s. At stimulation intensity of 72 mW/cm 2 , MDD is 37 μGy for integration time of 60 s and improves significantly to 7 μGy for 1 s. - Highlights: • CW-OSL parameters are optimized to obtain best SNR and MDD in Al 2 O 3 :C. • MDD is found to depend on signal integration time and stimulation intensity. • With time, MDD initially improves, stabilizes then deteriorates. • At a given intensity, MDD is optimum for a certain range of integration time

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

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Englander, Jacob A.; Englander, Arnold C.

2014-01-01

Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.

Improved VMAT planning for head and neck tumors with an advanced optimization algorithm

International Nuclear Information System (INIS)

Klippel, Norbert; Schmuecking, Michael; Terribilini, Dario; Geretschlaeger, Andreas; Aebersold, Daniel M.; Manser, Peter

2015-01-01

In this study, the ''Progressive Resolution Optimizer PRO3'' (Varian Medical Systems) is compared to the previous version PRO2'' with respect to its potential to improve dose sparing to the organs at risk (OAR) and dose coverage of the PTV for head and neck cancer patients. Materials and Methods For eight head and neck cancer patients, volumetric modulated arc therapy (VMAT) treatment plans were generated in this study. All cases have 2-3 phases and the total prescribed dose (PD) was 60-72 Gy in the PTV. The study is mainly focused on the phase 1 plans, which all have an identical PD of 54 Gy, and complex PTV structures with an overlap to the parotids. Optimization was performed based on planning objectives for the PTV according to ICRU83, and with minimal dose to spinal cord, and parotids outside PTV. In order to assess the quality of the optimization algorithms, an identical set of constraints was used for both, PRO2 and PRO3. The resulting treatment plans were investigated with respect to dose distribution based on the analysis of the dose volume histograms. Results For the phase 1 plans (PD = 54 Gy) the near maximum dose D 2% of the spinal cord, could be minimized to 22±5 Gy with PRO3, as compared to 32±12 Gy with PRO2, averaged for all patients. The mean dose to the parotids was also lower in PRO3 plans compared to PRO2, but the differences were less pronounced. A PTV coverage of V 95% = 97±1% could be reached with PRO3, as compared to 86±5% with PRO2. In clinical routine, these PRO2 plans would require modifications to obtain better PTV coverage at the cost of higher OAR doses. Conclusion A comparison between PRO3 and PRO2 optimization algorithms was performed for eight head and neck cancer patients. In general, the quality of VMAT plans for head and neck patients are improved with PRO3 as compared to PRO2. The dose to OARs can be reduced significantly, especially for the spinal cord. These reductions are achieved with better

Significant Improvement of Catalytic Efficiencies in Ionic Liquids

International Nuclear Information System (INIS)

Song, Choong Eui; Yoon, Mi Young; Choi, Doo Seong

2005-01-01

The use of ionic liquids as reaction media can confer many advantages upon catalytic reactions over reactions in organic solvents. In ionic liquids, catalysts having polar or ionic character can easily be immobilized without additional structural modification and thus the ionic solutions containing the catalyst can easily be separated from the reagents and reaction products, and then, be reused. More interestingly, switching from an organic solvent to an ionic liquid often results in a significant improvement in catalytic performance (e.g., rate acceleration, (enantio)selectivity improvement and an increase in catalyst stability). In this review, some recent interesting results which can nicely demonstrate these positive 'ionic liquid effect' on catalysis are discussed

Improving topology optimization intuition through games

DEFF Research Database (Denmark)

Nobel-Jørgensen, Morten; Malmgren-Hansen, David; Bærentzen, J. Andreas

2016-01-01

This paper describes the educational game, TopOpt Game, which invites the player to solve various optimization challenges. The main purpose of gamifying topology optimization is to create a supplemental educational tool which can be used to introduce concepts of topology optimization to newcomers...

Improved Genetic Algorithm with Two-Level Approximation for Truss Optimization by Using Discrete Shape Variables

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Shen-yan Chen

2015-01-01

Full Text Available This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA to minimize truss weight by simultaneously optimizing size, shape, and topology variables. On the basis of a previously presented truss sizing/topology optimization method based on two-level approximation and genetic algorithm (GA, a new method for adding shape variables is presented, in which the nodal positions are corresponding to a set of coordinate lists. A uniform optimization model including size/shape/topology variables is established. First, a first-level approximate problem is constructed to transform the original implicit problem to an explicit problem. To solve this explicit problem which involves size/shape/topology variables, GA is used to optimize individuals which include discrete topology variables and shape variables. When calculating the fitness value of each member in the current generation, a second-level approximation method is used to optimize the continuous size variables. With the introduction of shape variables, the original optimization algorithm was improved in individual coding strategy as well as GA execution techniques. Meanwhile, the update strategy of the first-level approximation problem was also improved. The results of numerical examples show that the proposed method is effective in dealing with the three kinds of design variables simultaneously, and the required computational cost for structural analysis is quite small.

Improvement in PWR automatic optimization reloading methods using genetic algorithm

International Nuclear Information System (INIS)

Levine, S.H.; Ivanov, K.; Feltus, M.

1996-01-01

The objective of using automatic optimized reloading methods is to provide the Nuclear Engineer with an efficient method for reloading a nuclear reactor which results in superior core configurations that minimize fuel costs. Previous methods developed by Levine et al required a large effort to develop the initial core loading using a priority loading scheme. Subsequent modifications to this core configuration were made using expert rules to produce the final core design. Improvements in this technique have been made by using a genetic algorithm to produce improved core reload designs for PWRs more efficiently (authors)

Improvement in PWR automatic optimization reloading methods using genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Levine, S H; Ivanov, K; Feltus, M [Pennsylvania State Univ., University Park, PA (United States)

1996-12-01

The objective of using automatic optimized reloading methods is to provide the Nuclear Engineer with an efficient method for reloading a nuclear reactor which results in superior core configurations that minimize fuel costs. Previous methods developed by Levine et al required a large effort to develop the initial core loading using a priority loading scheme. Subsequent modifications to this core configuration were made using expert rules to produce the final core design. Improvements in this technique have been made by using a genetic algorithm to produce improved core reload designs for PWRs more efficiently (authors).

Energy efficiency improvement by gear shifting optimization

Directory of Open Access Journals (Sweden)

Blagojevic Ivan A.

2013-01-01

Full Text Available Many studies have proved that elements of driver’s behavior related to gear selection have considerable influence on the fuel consumption. Optimal gear shifting is a complex task, especially for inexperienced drivers. This paper presents an implemented idea for gear shifting optimization with the aim of fuel consumption minimization with more efficient engine working regimes. Optimized gear shifting enables the best possible relation between vehicle motion regimes and engine working regimes. New theoretical-experimental approach has been developed using On-Board Diagnostic technology which so far has not been used for this purpose. The matrix of driving modes according to which tests were performed is obtained and special data acquisition system and analysis process have been developed. Functional relations between experimental test modes and adequate engine working parameters have been obtained and all necessary operations have been conducted to enable their use as inputs for the designed algorithm. The created Model has been tested in real exploitation conditions on passenger car with Otto fuel injection engine and On-Board Diagnostic connection without any changes on it. The conducted tests have shown that the presented Model has significantly positive effects on fuel consumption which is an important ecological aspect. Further development and testing of the Model allows implementation in wide range of motor vehicles with various types of internal combustion engines.

Analytical Tools to Improve Optimization Procedures for Lateral Flow Assays

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Helen V. Hsieh

2017-05-01

Full Text Available Immunochromatographic or lateral flow assays (LFAs are inexpensive, easy to use, point-of-care medical diagnostic tests that are found in arenas ranging from a doctor’s office in Manhattan to a rural medical clinic in low resource settings. The simplicity in the LFA itself belies the complex task of optimization required to make the test sensitive, rapid and easy to use. Currently, the manufacturers develop LFAs by empirical optimization of material components (e.g., analytical membranes, conjugate pads and sample pads, biological reagents (e.g., antibodies, blocking reagents and buffers and the design of delivery geometry. In this paper, we will review conventional optimization and then focus on the latter and outline analytical tools, such as dynamic light scattering and optical biosensors, as well as methods, such as microfluidic flow design and mechanistic models. We are applying these tools to find non-obvious optima of lateral flow assays for improved sensitivity, specificity and manufacturing robustness.

Training directionally selective motion pathways can significantly improve reading efficiency

Science.gov (United States)

Lawton, Teri

2004-06-01

This study examined whether perceptual learning at early levels of visual processing would facilitate learning at higher levels of processing. This was examined by determining whether training the motion pathways by practicing leftright movement discrimination, as found previously, would improve the reading skills of inefficient readers significantly more than another computer game, a word discrimination game, or the reading program offered by the school. This controlled validation study found that practicing left-right movement discrimination 5-10 minutes twice a week (rapidly) for 15 weeks doubled reading fluency, and significantly improved all reading skills by more than one grade level, whereas inefficient readers in the control groups barely improved on these reading skills. In contrast to previous studies of perceptual learning, these experiments show that perceptual learning of direction discrimination significantly improved reading skills determined at higher levels of cognitive processing, thereby being generalized to a new task. The deficits in reading performance and attentional focus experienced by the person who struggles when reading are suggested to result from an information overload, resulting from timing deficits in the direction-selectivity network proposed by Russell De Valois et al. (2000), that following practice on direction discrimination goes away. This study found that practicing direction discrimination rapidly transitions the inefficient 7-year-old reader to an efficient reader.

Topology optimization using the improved element-free Galerkin method for elasticity*

International Nuclear Information System (INIS)

Wu Yi; Ma Yong-Qi; Feng Wei; Cheng Yu-Min

2017-01-01

The improved element-free Galerkin (IEFG) method of elasticity is used to solve the topology optimization problems. In this method, the improved moving least-squares approximation is used to form the shape function. In a topology optimization process, the entire structure volume is considered as the constraint. From the solid isotropic microstructures with penalization, we select relative node density as a design variable. Then we choose the minimization of compliance to be an objective function, and compute its sensitivity with the adjoint method. The IEFG method in this paper can overcome the disadvantages of the singular matrices that sometimes appear in conventional element-free Galerkin (EFG) method. The central processing unit (CPU) time of each example is given to show that the IEFG method is more efficient than the EFG method under the same precision, and the advantage that the IEFG method does not form singular matrices is also shown. (paper)

Optimization of caseinate-coated simvastatin-zein nanoparticles: improved bioavailability and modified release characteristics.

Science.gov (United States)

Ahmed, Osama A A; Hosny, Khaled M; Al-Sawahli, Majid M; Fahmy, Usama A

2015-01-01

The current study focuses on utilization of the natural biocompatible polymer zein to formulate simvastatin (SMV) nanoparticles coated with caseinate, to improve solubility and hence bioavailability, and in addition, to modify SMV-release characteristics. This formulation can be utilized for oral or possible depot parenteral applications. Fifteen formulations were prepared by liquid-liquid phase separation method, according to the Box-Behnken design, to optimize formulation variables. Sodium caseinate was used as an electrosteric stabilizer. The factors studied were: percentage of SMV in the SMV-zein mixture (X1), ethanol concentration (X2), and caseinate concentration (X3). The selected dependent variables were mean particle size (Y1), SMV encapsulation efficiency (Y2), and cumulative percentage of drug permeated after 1 hour (Y3). The diffusion of SMV from the prepared nanoparticles specified by the design was carried out using an automated Franz diffusion cell apparatus. The optimized SMV-zein formula was investigated for in vivo pharmacokinetic parameters compared with an oral SMV suspension. The optimized nanosized SMV-zein formula showed a 131 nm mean particle size and 89% encapsulation efficiency. In vitro permeation studies displayed delayed permeation characteristics, with about 42% and 85% of SMV cumulative amount released after 12 and 48 hours, respectively. Bioavailability estimation in rats revealed an augmentation in SMV bioavailability from the optimized SMV-zein formulation, by fourfold relative to SMV suspension. Formulation of caseinate-coated SMV-zein nanoparticles improves the pharmacokinetic profile and bioavailability of SMV. Accordingly, improved hypolipidemic activities for longer duration could be achieved. In addition, the reduced dosage rate of SMV-zein nanoparticles improves patient tolerability and compliance.

Knowledge Translation to Optimize Adult Inpatient Glycemic Management with Basal Bolus Insulin Therapy and Improve Patient Outcomes.

Science.gov (United States)

Helmle, Karmon E; Chacko, Sunita; Chan, Trevor; Drake, Alison; Edwards, Alun L; Moore, Glenda E; Philp, Leta C; Popeski, Naomi; Roedler, Rhonda L; Rogers, Edwin J R; Zimmermann, Gabrielle L; McKeen, Julie

2017-12-27

To develop and evaluate a Basal Bolus Insulin Therapy (BBIT) Knowledge Translation toolkit to address barriers to adoption of established best practice with BBIT in the care of adult inpatients. This study was conducted in 2 phases and focused on the hospitalist provider group across 4 acute care facilities in Calgary. Phase 1 involved a qualitative evaluation of provider and site specific barriers and facilitators, which were mapped to validated interventions using behaviour change theory. This informed the co-development and optimization of the BBIT Knowledge Translation toolkit, with each tool targeting a specific barrier to improved diabetes care practice, including BBIT ordering. In Phase 2, the BBIT Knowledge Translation toolkit was implemented and evaluated, focusing on BBIT ordering frequency, as well as secondary outcomes of hyperglycemia (patient-days with BG >14.0 mmol/L), hypoglycemia (patient-days with BG Knowledge Translation toolkit resulted in a significant 13% absolute increase in BBIT ordering. Hyperglycemic patient-days were significantly reduced, with no increase in hypoglycemia. There was a significant, absolute 14% reduction in length of stay. The implementation of an evidence-informed, multifaceted BBIT Knowledge Translation toolkit effectively reduced a deeply entrenched in-patient diabetes care gap. The resulting sustained practice change improved patient clinical and system resource utilization outcomes. This systemic approach to implementation will guide further scale and spread of glycemic optimization initiatives. Copyright © 2018 Diabetes Canada. Published by Elsevier Inc. All rights reserved.

Development and optimization of sulforaphane-loaded nanostructured lipid carriers by the Box-Behnken design for improved oral efficacy against cancer: in vitro, ex vivo and in vivo assessments.

Science.gov (United States)

Soni, Kriti; Rizwanullah, Md; Kohli, Kanchan

2017-11-28

In the present study, sulforaphane (SFN)-loaded nanostructured lipid carriers (NLC) were developed and optimized for improved oral efficacy against cancer. The SFN-loaded NLC formulation was developed by melt emulsification ultrasonication technique and optimized by Box-Behnken statistical design. The optimized SFN-loaded NLC formulation composed of precirol ® ATO 5 (solid lipid) and vitamin E (liquid lipid) as lipid phase (3% w/v), poloxamer 188 (1%) and Tween 80 (1%) as surfactant. The mean particle size, polydispersity index, zeta potential, entrapment efficiency (%) and drug loading (%) of optimized SFN-loaded NLC formulation was observed to be 145.38 ± 4.46 nm, 0.181 ± 0.023, -25.12 ± 2.36 mV, 84.94 ± 3.82% and 14.82 ± 3.46%, respectively. In vitro drug release studies showed that the release of SFN from optimized NLC formulation was significantly higher (86.52 ± 5.48%) compared to SFN suspension (38.47 ± 5.52%) up to 24 h. Ex vivo gut permeation studies revealed a very good enhancement in permeation of drug present in the NLC compared to plain SFN solution and were further confirmed by CLSM. MTT assay in different cancer cell lines showed that the optimized SFN-loaded NLC formulation exhibited significantly improved (p < .05) cytotoxicity compared to free SFN solution. SFN-loaded NLC formulation showed significantly improved antioxidant activity compared to free SFN solution. Furthermore, pharmacokinetic study on albino Wistar rats showed 5.04-fold increase in relative oral bioavailability with NLC (p < .05) compared to SFN suspension. Therefore, NLC represents a great potential for improved efficacy of SFN after oral administration.

Improving the automated optimization of profile extrusion dies by applying appropriate optimization areas and strategies

Science.gov (United States)

Hopmann, Ch.; Windeck, C.; Kurth, K.; Behr, M.; Siegbert, R.; Elgeti, S.

2014-05-01

The rheological design of profile extrusion dies is one of the most challenging tasks in die design. As no analytical solution is available, the quality and the development time for a new design highly depend on the empirical knowledge of the die manufacturer. Usually, prior to start production several time-consuming, iterative running-in trials need to be performed to check the profile accuracy and the die geometry is reworked. An alternative are numerical flow simulations. These simulations enable to calculate the melt flow through a die so that the quality of the flow distribution can be analyzed. The objective of a current research project is to improve the automated optimization of profile extrusion dies. Special emphasis is put on choosing a convenient starting geometry and parameterization, which enable for possible deformations. In this work, three commonly used design features are examined with regard to their influence on the optimization results. Based on the results, a strategy is derived to select the most relevant areas of the flow channels for the optimization. For these characteristic areas recommendations are given concerning an efficient parameterization setup that still enables adequate deformations of the flow channel geometry. Exemplarily, this approach is applied to a L-shaped profile with different wall thicknesses. The die is optimized automatically and simulation results are qualitatively compared with experimental results. Furthermore, the strategy is applied to a complex extrusion die of a floor skirting profile to prove the universal adaptability.

Single-source dual-energy spectral multidetector CT of pancreatic adenocarcinoma: optimization of energy level viewing significantly increases lesion contrast.

Science.gov (United States)

Patel, B N; Thomas, J V; Lockhart, M E; Berland, L L; Morgan, D E

2013-02-01

.007). Significantly increased pancreatic lesion contrast was noted at lower viewing energies using spectral MDCT. Individual patient CNR-optimized energy level images have the potential to improve lesion conspicuity. Copyright © 2012 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks

Directory of Open Access Journals (Sweden)

Naixue Xiong

2017-06-01

Full Text Available A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.

Optimization of Boiler Control for Improvement of Load Following Capabilities of Existing Power Plants

DEFF Research Database (Denmark)

Mortensen, J. H.; Mølbak, T.; Pedersen, Tom Søndergaard

1997-01-01

An An optimizing control system for improving the load following capabilities of power plant units has been developed. The system is implemented as a complement producing additive control signals to the existing boiler control system, a concept which has various practical advantages in terms...... of implementation and commissioning. The optimizing control system takes into account the multivariable and nonlinear characteristics of the boiler process as a gain-scheduled LQG-controller is utilized. Simulation results indicate that a reduction of steam temperature deviations of about 75% can be obtained.......optimizing control system for improving the load following capabilities of power plant units has been developed. The system is implemented as a complement producing additive control signals to the existing boiler control system, a concept which has various practical advantages in terms of implementation...

Improved Full-Newton Step O(nL) Infeasible Interior-Point Method for Linear Optimization

NARCIS (Netherlands)

Gu, G.; Mansouri, H.; Zangiabadi, M.; Bai, Y.Q.; Roos, C.

2009-01-01

We present several improvements of the full-Newton step infeasible interior-point method for linear optimization introduced by Roos (SIAM J. Optim. 16(4):1110–1136, 2006). Each main step of the method consists of a feasibility step and several centering steps. We use a more natural feasibility step,

Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

Directory of Open Access Journals (Sweden)

Yuan Pu

2015-01-01

Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

Small Signal Stability Improvement of Power Systems Using Optimal Load Responses in Competitive Electricity Markets

DEFF Research Database (Denmark)

Hu, Weihao; Su, Chi; Chen, Zhe

2011-01-01

Since the hourly spot market price is available one day ahead in Denmark, the price could be transferred to the consumers and they may shift some of their loads from high price periods to the low price periods in order to save their energy costs. The optimal load response to an electricity price...... price is proposed. A 17-bus power system with high wind power penetrations, which resembles the Eastern Danish power system, is chosen as the study case. Simulation results show that the optimal load response to electricity prices is an effective measure to improve the small signal stability of power...... for demand side management generates different load profiles and may provide an opportunity to improve the small signal stability of power systems with high wind power penetrations. In this paper, the idea of power system small signal stability improvement by using optimal load response to the electricity...

Improved multi-objective clustering algorithm using particle swarm optimization.

Science.gov (United States)

Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang

2017-01-01

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos; Mariani, Viviana Cocco

2007-01-01

Global optimization based on evolutionary algorithms can be used as the important component for many engineering optimization problems. Evolutionary algorithms have yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization problems in the power systems area. Differential evolution (DE) is a simple and efficient evolutionary algorithm for function optimization over continuous spaces. It has reportedly outperformed search heuristics when tested over both benchmark and real world problems. This paper proposes improved DE algorithms for solving economic load dispatch problems that take into account nonlinear generator features such as ramp rate limits and prohibited operating zones in the power system operation. The DE algorithms and its variants are validated for two test systems consisting of 6 and 15 thermal units. Various DE approaches outperforms other state of the art algorithms reported in the literature in solving load dispatch problems with generator constraints

Optimization of significant insolation distribution parameters - A new approach towards BIPV system design

Energy Technology Data Exchange (ETDEWEB)

Paul, D. [SSBB and Senior Member-ASQ, Kolkata (India); Mandal, S.N. [Kalyani Govt Engg College, Kalyani (India); Mukherjee, D.; Bhadra Chaudhuri, S.R. [Dept of E. and T. C. Engg, B.E.S.U., Shibpur (India)

2010-10-15

System efficiency and payback time are yet to attain a commercially viable level for solar photovoltaic energy projects. Despite huge development in prediction of solar radiation data, there is a gap in extraction of pertinent information from such data. Hence the available data cannot be effectively utilized for engineering application. This is acting as a barrier for the emerging technology. For making accurate engineering and financial calculations regarding any solar energy project, it is crucial to identify and optimize the most significant statistic(s) representing insolation availability by the Photovoltaic setup at the installation site. Quality Function Deployment (QFD) technique has been applied for identifying the statistic(s), which are of high significance from a project designer's point of view. A MATLAB trademark program has been used to build the annual frequency distribution of hourly insolation over any module plane at a given location. Descriptive statistical analysis of such distributions is done through MINITAB trademark. For Building Integrated Photo Voltaic (BIPV) installation, similar statistical analysis has been carried out for the composite frequency distribution, which is formed by weighted summation of insolation distributions for different module planes used in the installation. Vital most influential statistic(s) of the composite distribution have been optimized through Artificial Neural Network computation. This approach is expected to open up a new horizon in BIPV system design. (author)

Optimization of a fuel bundle within a CANDU supercritical water reactor

International Nuclear Information System (INIS)

Schofield, M.E.

2009-01-01

The supercritical water reactor is one of six nuclear reactor concepts being studied under the Generation IV International Forum. Generation IV nuclear reactors will improve the metrics of economics, sustainability, safety and reliability, and physical protection and proliferation resistance over current nuclear reactor designs. The supercritical water reactor has specific benefits in the areas of economics, safety and reliability, and physical protection. This work optimizes the fuel composition and bundle geometry to maximize the fuel burnup, and minimize the surface heat flux and the form factor. In optimizing these factors, improvements can be achieved in the areas of economics, safety and reliability of the supercritical water reactor. The WIMS-AECL software was used to model a fuel bundle within a CANDU supercritical water reactor. The Gauss' steepest descent method was used to optimize the above mentioned factors. Initially the fresh fuel composition was optimized within a 43-rod CANFLEX bundle and a 61-rod bundle. In both the 43-rod and 61-rod bundle scenarios an online refuelling scheme and non-refuelling scheme were studied. The geometry of the fuel bundles was then optimized. Finally, a homogeneous mixture of thorium and uranium fuel was studied in a 60-rod bundle. Each optimization process showed definitive improvements in the factors being studied, with the most significant improvement being an increase in the fuel burnup. The 43-rod CANFLEX bundle was the most successful at being optimized. There was little difference in the final fresh fuel content when comparing an online refuelling scheme and non-refuelling scheme. Through each optimization scenario the ratio of the fresh fuel content between the annuli was a significant determining cause in the improvements in the factors being optimized. The geometry optimization showed that improvement in the design of a fuel bundle is indeed possible, although it would be more advantageous to pursue it

Optimization strategies for improving irrigation water management of lower jhelum canal

International Nuclear Information System (INIS)

Rashid, M.U.

2015-01-01

The paper includes computing crop water requirement, identification of problems and optimization strategies for improved irrigation water management of a canal command. Lower Jhelum Canal (LJC) System was selected as a case study. Possible strategies for optimization are enhancing irrigation water productivity by high value and high yield crops, adoption of resource conservation interventions (RCIs) at the farm level, improving irrigation system efficiency and its management. Estimation of daily reference evapotranspiration of LJC command was carried out by Penman Montieth -2000 method and metrological data of Sargodha for the period 1999 to 2010 was used. Crop water requirements were computed from reference evapotranspiration, crop coefficients and periods of crops for existing cropping pattern. The comparison of the crop water requirements and available water supplies indicated shortage of more than 51% in Kharif and 54% in Rabi seasons. The gap between requirements and supplies is fulfilled by groundwater in the command. The structural measures identified in the present study for improving canal management include rationalization of canal capacities in keeping with the current water requirements and availability, rehabilitation and remodeling of canal network and lining of distributaries and minors in saline groundwater areas. An array of measures and practices identified for improved water management at the farm level include: improvement and lining of watercourses, proper farm design and layout, adoption of resource conservation technologies involving laser land leveling, zero tillage, and bed-furrow irrigation method. Adopting proper cropping systems considering land suitability and capacity building of farming community in improved soil, crop and water management technologies would enhance the water productivity in an effective and sustainable manner. (author)

Improved Ant Colony Optimization for Seafood Product Delivery Routing Problem

Directory of Open Access Journals (Sweden)

Baozhen Yao

2014-02-01

Full Text Available This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem.

Quality Improvement Initiatives to Optimize the Management of Chronic Obstructive Pulmonary Disease in Patients With Lung Cancer.

Science.gov (United States)

Digby, Geneviève C; Robinson, Andrew

2017-11-01

Patients with lung cancer (LC) frequently have chronic obstructive pulmonary disease (COPD), the optimization of which improves outcomes. A 2014 Queen's University Hospitals audit demonstrated that COPD was underdiagnosed and undertreated in outpatients with LC. We sought to improve the diagnosis and management of COPD in this population. We implemented change using a Define/Measure/Analyze/Improve/Control (DMAIC) improvement cycle. Data were obtained by chart review from the Cancer Care Ontario database and e-Patient System for patients with newly diagnosed LC, including patient characteristics, pulmonary function test (PFT) data, and bronchodilator therapies. Improvement cycle 1 included engaging stakeholders and prioritizing COPD management by respirologists in the Lung Diagnostic Assessment Program. Improvement cycle 2 included physician restructuring and developing a standard work protocol. Data were analyzed monthly and presented on statistical process control P-charts, which assessed differences over time. The χ 2 and McNemar tests assessed for significance between independent and dependent groups, respectively. A total of 477 patients were studied (165 patients at baseline, 166 patients in cycle 1, and 127 patients in cycle 2). There was no change in PFT completion over time, although respirology-managed patients were significantly more likely to undergo a PFT than patients who were not managed by respirology (56.7% v 96.1%; P managed patients with LC with airflow obstruction receiving inhaled bronchodilator significantly increased (baseline, 46.3%; cycle 1, 51.0%; and cycle 2, 74.3%). By cycle 2, patients with airflow obstruction were more likely to receive a long-acting bronchodilator if managed by respirology (74.3% v 44.8%; P = .0009). COPD is underdiagnosed and undertreated in outpatients with LC. A DMAIC quality improvement strategy emphasizing COPD treatment during LC evaluation in the Lung Diagnostic Assessment Program significantly improved COPD

Significantly improved surface morphology of N-polar GaN film grown on SiC substrate by the optimization of V/III ratio

Science.gov (United States)

Deng, Gaoqiang; Zhang, Yuantao; Yu, Ye; Yan, Long; Li, Pengchong; Han, Xu; Chen, Liang; Zhao, Degang; Du, Guotong

2018-04-01

In this paper, N-polar GaN films with different V/III ratios were grown on vicinal C-face SiC substrates by metalorganic chemical vapor deposition. During the growth of N-polar GaN film, the V/III ratio was controlled by adjusting the molar flow rate of ammonia while keeping the trimethylgallium flow rate unchanged. The influence of the V/III ratio on the surface morphology of N-polar GaN film has been studied. We find that the surface root mean square roughness of N-polar GaN film over an area of 20 × 20 μm2 can be reduced from 8.13 to 2.78 nm by optimization of the V/III ratio. Then, using the same growth conditions, N-polar InGaN/GaN multiple quantum wells (MQWs) light-emitting diodes (LEDs) were grown on the rough and the smooth N-polar GaN templates, respectively. Compared with the LED grown on the rough N-polar GaN template, dramatically improved interface sharpness and luminescence uniformity of the InGaN/GaN MQWs are achieved for the LED grown on the smooth N-polar GaN template.

Improved Solutions for the Optimal Coordination of DOCRs Using Firefly Algorithm

Directory of Open Access Journals (Sweden)

Muhammad Sulaiman

2018-01-01

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

Improving alignment in Tract-based spatial statistics: evaluation and optimization of image registration.

Science.gov (United States)

de Groot, Marius; Vernooij, Meike W; Klein, Stefan; Ikram, M Arfan; Vos, Frans M; Smith, Stephen M; Niessen, Wiro J; Andersson, Jesper L R

2013-08-01

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R(2) 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment. Copyright © 2013 Elsevier Inc. All rights reserved.

Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region.

Science.gov (United States)

Yang, Qidong; Zuo, Hongchao; Li, Weidong

2016-01-01

Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.

Improving of the working process of axial compressors of gas turbine engines by using an optimization method

Science.gov (United States)

Marchukov, E.; Egorov, I.; Popov, G.; Baturin, O.; Goriachkin, E.; Novikova, Y.; Kolmakova, D.

2017-08-01

The article presents one optimization method for improving of the working process of an axial compressor of gas turbine engine. Developed method allows to perform search for the best geometry of compressor blades automatically by using optimization software IOSO and CFD software NUMECA Fine/Turbo. Optimization was performed by changing the form of the middle line in the three sections of each blade and shifts of three sections of the guide vanes in the circumferential and axial directions. The calculation of the compressor parameters was performed for work and stall point of its performance map on each optimization step. Study was carried out for seven-stage high-pressure compressor and three-stage low-pressure compressors. As a result of optimization, improvement of efficiency was achieved for all investigated compressors.

Improving the Performance of PbS Quantum Dot Solar Cells by Optimizing ZnO Window Layer

Science.gov (United States)

Yang, Xiaokun; Hu, Long; Deng, Hui; Qiao, Keke; Hu, Chao; Liu, Zhiyong; Yuan, Shengjie; Khan, Jahangeer; Li, Dengbing; Tang, Jiang; Song, Haisheng; Cheng, Chun

2017-04-01

Comparing with hot researches in absorber layer, window layer has attracted less attention in PbS quantum dot solar cells (QD SCs). Actually, the window layer plays a key role in exciton separation, charge drifting, and so on. Herein, ZnO window layer was systematically investigated for its roles in QD SCs performance. The physical mechanism of improved performance was also explored. It was found that the optimized ZnO films with appropriate thickness and doping concentration can balance the optical and electrical properties, and its energy band align well with the absorber layer for efficient charge extraction. Further characterizations demonstrated that the window layer optimization can help to reduce the surface defects, improve the heterojunction quality, as well as extend the depletion width. Compared with the control devices, the optimized devices have obtained an efficiency of 6.7% with an enhanced V oc of 18%, J sc of 21%, FF of 10%, and power conversion efficiency of 58%. The present work suggests a useful strategy to improve the device performance by optimizing the window layer besides the absorber layer.

Strain improvement and statistical optimization as a combined strategy for improving fructosyltransferase production by Aureobasidium pullulans NAC8

Directory of Open Access Journals (Sweden)

Adedeji Nelson Ademakinwa

2017-12-01

A relatively low FTase-producing strain of Aureobasidium pullulans NAC8 was enhanced for optimum production using a two-pronged approach involving mutagenesis and statistical optimization. The improved mutant strain also had remarkable biotechnological properties that make it a suitable alternative than the wild-type.

Improving source discrimination performance by using an optimized acoustic array and adaptive high-resolution CLEAN-SC beamforming

NARCIS (Netherlands)

Luesutthiviboon, S.; Malgoezar, A.M.N.; Snellen, M.; Sijtsma, P.; Simons, D.G.

2018-01-01

Beamforming performance can be improved in two ways: optimizing the location of microphones on the acoustic array and applying advanced beamforming algorithms. In this study, the effects of the two approaches are studied. An optimization method is developed to optimize the location of microphones

Solution quality improvement in chiller loading optimization

International Nuclear Information System (INIS)

Geem, Zong Woo

2011-01-01

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

Improved multi-objective clustering algorithm using particle swarm optimization.

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Congcong Gong

Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

Optimization Design of IGV Profile in Centrifugal Compressor

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Qi Sun

2017-01-01

Full Text Available Variable inlet guide vane (IGV is used to control the mass flow and generate prewhirl in centrifugal compressors. The efficient operation of IGV is limited to the range of aerodynamic characteristics of their vane profiles. In order to find out the best vane profile for IGV regulation, the modern optimization method was adopted to optimize the inlet guide vane profile. The main methodology idea was to use artificial neural network for continuous fitness evaluation and use genetic algorithm for global optimization. After optimization, the regulating performance of IGV has improved significantly, the prewhirl ability has been enhanced greatly, and the pressure loss has been reduced. The mass flow and power of compressor reduced by using the optimized guide vane at large setting angles, and the efficiency increased significantly; the flow field distribution has been improved obviously, since the nonuniform distribution of flow and flow separation phenomenon greatly weakened or even completely disappeared. The achievement of this research can effectively improve the regulation ability of IGV and the performance of compressor.

Pelvic interstitial brachytherapy - improving the therapeutic ratio with magnetic resonance imaging and optimization

International Nuclear Information System (INIS)

Swift, Patrick S.; Hricak, Hedvig; Forstner, Rosemary; Powell, C. Bethan; Purser, Phil; Weaver, Keith; Phillips, Theodore L.

1996-01-01

Introduction Interstitial brachytherapy in the pelvic region is often hampered by the radiation oncologist's inability to precisely differentiate tumor versus normal tissue during the planning and implantation procedures, often resulting in either excessive or incomplete coverage of tumor volume. The marked improvement in pelvic imaging seen with magnetic resonance, in conjunction with isodose optimization programs for remote-afterloading units, has created an opportunity to significantly improve the therapeutic ratio. Methods From 1992-1995, 23 interstitial perineal templates were performed in 22 patients with pelvic malignancies, using the pulsed low-dose-rate Selectron with dose optimization. MR imaging was performed immediately prior to the implant, with a MUPIT placed against the perineum and a vaginal obturator in place. These images were used for tumor volume measurements, determination of the number, depth and angle of needles required for the implant, and identification of position of normal tissues (rectum, small bowel, bladder) relative to the tumor. After implantation of stainless steel needles, orthogonal radiographs were obtained for isodose calculation, and planning carried out with isodose optimization. Patients were followed closely on a routine schedule, until time of last visit or until death. Every effort possible was made to assess local disease status at time of death. Results Sixteen patients with primary disease (14 cervix, 1 vulva, 1 vagina) and 6 with recurrent (2 with prior radiation) were implanted, all but 3 with curative intent. Nine patients with advanced cervix or vulvar cancer received concomitant chemotherapy (5FU + platinum or mitomycin-C) with the external beam therapy. At a median follow-up of 18.1 months for all cases, only three patients have failed locally for an actuarial local control of 85% at 1.5 years. Nine patients are alive and free of disease, 8 are alive with distant disease only (mean follow-up of 19.1 months), 2

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

Science.gov (United States)

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

Inhaler Reminders Significantly Improve Asthma Patients' Use of Controller Medications

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... controller medications Share | Inhaler reminders significantly improve asthma patients’ use of controller medications Published Online: July 22, ... the burden and risk of asthma, but many patients do not use them regularly. This poor adherence ...

Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm

International Nuclear Information System (INIS)

Oliva, Diego; Abd El Aziz, Mohamed; Ella Hassanien, Aboul

2017-01-01

Highlights: •We modify the whale algorithm using chaotic maps. •We apply a chaotic algorithm to estimate parameter of photovoltaic cells. •We perform a study of chaos in whale algorithm. •Several comparisons and metrics support the experimental results. •We test the method with data from real solar cells. -- Abstract: The using of solar energy has been increased since it is a clean source of energy. In this way, the design of photovoltaic cells has attracted the attention of researchers over the world. There are two main problems in this field: having a useful model to characterize the solar cells and the absence of data about photovoltaic cells. This situation even affects the performance of the photovoltaic modules (panels). The characteristics of the current vs. voltage are used to describe the behavior of solar cells. Considering such values, the design problem involves the solution of the complex non-linear and multi-modal objective functions. Different algorithms have been proposed to identify the parameters of the photovoltaic cells and panels. Most of them commonly fail in finding the optimal solutions. This paper proposes the Chaotic Whale Optimization Algorithm (CWOA) for the parameters estimation of solar cells. The main advantage of the proposed approach is using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm. This situation is beneficial in complex problems, because along the iterative process, the proposed algorithm improves their capabilities to search for the best solution. The modified method is able to optimize complex and multimodal objective functions. For example, the function for the estimation of parameters of solar cells. To illustrate the capabilities of the proposed algorithm in the solar cell design, it is compared with other optimization methods over different datasets. Moreover, the experimental results support the improved performance of the proposed approach

Improvement of LOD in Fluorescence Detection with Spectrally Nonuniform Background by Optimization of Emission Filtering.

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Galievsky, Victor A; Stasheuski, Alexander S; Krylov, Sergey N

2017-10-17

The limit-of-detection (LOD) in analytical instruments with fluorescence detection can be improved by reducing noise of optical background. Efficiently reducing optical background noise in systems with spectrally nonuniform background requires complex optimization of an emission filter-the main element of spectral filtration. Here, we introduce a filter-optimization method, which utilizes an expression for the signal-to-noise ratio (SNR) as a function of (i) all noise components (dark, shot, and flicker), (ii) emission spectrum of the analyte, (iii) emission spectrum of the optical background, and (iv) transmittance spectrum of the emission filter. In essence, the noise components and the emission spectra are determined experimentally and substituted into the expression. This leaves a single variable-the transmittance spectrum of the filter-which is optimized numerically by maximizing SNR. Maximizing SNR provides an accurate way of filter optimization, while a previously used approach based on maximizing a signal-to-background ratio (SBR) is the approximation that can lead to much poorer LOD specifically in detection of fluorescently labeled biomolecules. The proposed filter-optimization method will be an indispensable tool for developing new and improving existing fluorescence-detection systems aiming at ultimately low LOD.

Selecting optimal monochromatic level with spectral CT imaging for improving imaging quality in hepatic venography

International Nuclear Information System (INIS)

Sun Jun; Luo Xianfu; Wang Shou'an; Wang Jun; Sun Jiquan; Wang Zhijun; Wu Jingtao

2013-01-01

Objective: To investigate the effect of spectral CT monochromatic images for improving imaging quality in hepatic venography. Methods: Thirty patients underwent spectral CT examination on a GE Discovery CT 750 HD scanner. During portal phase, 1.25 mm slice thickness polychromatic images and optimal monochromatic images were obtained, and volume rendering and maximum intensity projection were created to show the hepatic veins respectively. The overall imaging quality was evaluated on a five-point scale by two radiologists. Inter-observer agreement in subjective image quality grading was assessed by Kappa statistics. Paired-sample t test were used to compare hepatic vein attenuation, hepatic parenchyma attenuation, CT value difference between the hepatic vein and the liver parenchyma, image noise, vein-to-liver contrast-to-noise ratio (CNR), the image quality score of hepatic venography between the two image data sets. Results: The monochromatic images at 50 keV were found to demonstrate the best CNR for hepatic vein.The hepatic vein attenuation [(329 ± 47) HU], hepatic parenchyma attenuation [(178 ± 33) HU], CT value difference between the hepatic vein and the liver parenchyma [(151 ± 33) HU], image noise (17.33 ± 4.18), CNR (9.13 ± 2.65), the image quality score (4.2 ± 0.6) of optimal monochromatic images were significantly higher than those of polychromatic images [(149 ± 18) HU], [(107 ± 14) HU], [(43 ±11) HU], 12.55 ± 3.02, 3.53 ± 1.03, 3.1 ± 0.8 (t values were 24.79, 13.95, 18.85, 9.07, 13.25 and 12.04, respectively, P < 0.01). In the comparison of image quality, Kappa value was 0.81 with optimal monochromatic images and 0.69 with polychromatic images. Conclusion: Monochromatic images of spectral CT could improve CNR for displaying hepatic vein and improve the image quality compared to the conventional polychromatic images. (authors)

Improving package structure of object-oriented software using multi-objective optimization and weighted class connections

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Amarjeet

2017-07-01

Full Text Available The software maintenance activities performed without following the original design decisions about the package structure usually deteriorate the quality of software modularization, leading to decay of the quality of the system. One of the main reasons for such structural deterioration is inappropriate grouping of source code classes in software packages. To improve such grouping/modular-structure, previous researchers formulated the software remodularization problem as an optimization problem and solved it using search-based meta-heuristic techniques. These optimization approaches aimed at improving the quality metrics values of the structure without considering the original package design decisions, often resulting into a totally new software modularization. The entirely changed software modularization becomes costly to realize as well as difficult to understand for the developers/maintainers. To alleviate this issue, we propose a multi-objective optimization approach to improve the modularization quality of an object-oriented system with minimum possible movement of classes between existing packages of original software modularization. The optimization is performed using NSGA-II, a widely-accepted multi-objective evolutionary algorithm. In order to ensure minimum modification of original package structure, a new approach of computing class relations using weighted strengths has been proposed here. The weights of relations among different classes are computed on the basis of the original package structure. A new objective function has been formulated using these weighted class relations. This objective function drives the optimization process toward better modularization quality simultaneously ensuring preservation of original structure. To evaluate the results of the proposed approach, a series of experiments are conducted over four real-worlds and two random software applications. The experimental results clearly indicate the effectiveness

Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence

International Nuclear Information System (INIS)

Niu, Tianye; Fruhauf, Quentin; Petrongolo, Michael; Zhu, Lei; Ye, Xiaojing

2014-01-01

Recently, we proposed a new algorithm of accelerated barrier optimization compressed sensing (ABOCS) for iterative CT reconstruction. The previous implementation of ABOCS uses gradient projection (GP) with a Barzilai–Borwein (BB) step-size selection scheme (GP-BB) to search for the optimal solution. The algorithm does not converge stably due to its non-monotonic behavior. In this paper, we further improve the convergence of ABOCS using the unknown-parameter Nesterov (UPN) method and investigate the ABOCS reconstruction performance on clinical patient data. Comparison studies are carried out on reconstructions of computer simulation, a physical phantom and a head-and-neck patient. In all of these studies, the ABOCS results using UPN show more stable and faster convergence than those of the GP-BB method and a state-of-the-art Bregman-type method. As shown in the simulation study of the Shepp–Logan phantom, UPN achieves the same image quality as those of GP-BB and the Bregman-type methods, but reduces the iteration numbers by up to 50% and 90%, respectively. In the Catphan©600 phantom study, a high-quality image with relative reconstruction error (RRE) less than 3% compared to the full-view result is obtained using UPN with 17% projections (60 views). In the conventional filtered-backprojection reconstruction, the corresponding RRE is more than 15% on the same projection data. The superior performance of ABOCS with the UPN implementation is further demonstrated on the head-and-neck patient. Using 25% projections (91 views), the proposed method reduces the RRE from 21% as in the filtered backprojection (FBP) results to 7.3%. In conclusion, we propose UPN for ABOCS implementation. As compared to GP-BB and the Bregman-type methods, the new method significantly improves the convergence with higher stability and fewer iterations. (paper)

Bone marrow-derived cells for cardiovascular cell therapy: an optimized GMP method based on low-density gradient improves cell purity and function.

Science.gov (United States)

Radrizzani, Marina; Lo Cicero, Viviana; Soncin, Sabrina; Bolis, Sara; Sürder, Daniel; Torre, Tiziano; Siclari, Francesco; Moccetti, Tiziano; Vassalli, Giuseppe; Turchetto, Lucia

2014-09-27

Cardiovascular cell therapy represents a promising field, with several approaches currently being tested. The advanced therapy medicinal product (ATMP) for the ongoing METHOD clinical study ("Bone marrow derived cell therapy in the stable phase of chronic ischemic heart disease") consists of fresh mononuclear cells (MNC) isolated from autologous bone marrow (BM) through density gradient centrifugation on standard Ficoll-Paque. Cells are tested for safety (sterility, endotoxin), identity/potency (cell count, CD45/CD34/CD133, viability) and purity (contaminant granulocytes and platelets). BM-MNC were isolated by density gradient centrifugation on Ficoll-Paque. The following process parameters were optimized throughout the study: gradient medium density; gradient centrifugation speed and duration; washing conditions. A new manufacturing method was set up, based on gradient centrifugation on low density Ficoll-Paque, followed by 2 washing steps, of which the second one at low speed. It led to significantly higher removal of contaminant granulocytes and platelets, improving product purity; the frequencies of CD34+ cells, CD133+ cells and functional hematopoietic and mesenchymal precursors were significantly increased. The methodological optimization described here resulted in a significant improvement of ATMP quality, a crucial issue to clinical applications in cardiovascular cell therapy.

Improving texture optimization with application to visualizing meat products

DEFF Research Database (Denmark)

Clemmensen, Line Katrine Harder; Laursen, Lasse Farnung

2011-01-01

When inspecting food quality, CT Scanning is among the primary tools used to gain insight. It provides valuable volumetric data using a process, which leaves the product unspoiled and untouched. However, volumetric data is merely a measure of density and therefore contains no appearance information...... et al. in 2007. This method accepts a number of 2D input exemplars, from which it generates a solid texture volume. The volume is iteratively improved via an expectation maximization algorithm. The bottleneck of Texture Optimization occurs during a nearest neighbor search, between texture patches...

Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression

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Maokuan Zheng

2017-01-01

Full Text Available The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2. The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.

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

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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.

Reliability-Based Design Optimization of Trusses with Linked-Discrete Design Variables using the Improved Firefly Algorithm

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N. M. Okasha

2016-04-01

Full Text Available In this paper, an approach for conducting a Reliability-Based Design Optimization (RBDO of truss structures with linked-discrete design variables is proposed. The sections of the truss members are selected from the AISC standard tables and thus the design variables that represent the properties of each section are linked. Latin hypercube sampling is used in the evaluation of the structural reliability. The improved firefly algorithm is used for the optimization solution process. It was found that in order to use the improved firefly algorithm for efficiently solving problems of reliability-based design optimization with linked-discrete design variables; it needs to be modified as proposed in this paper to accelerate its convergence.

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.

Science.gov (United States)

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.

Improving the Thermostability and Optimal Temperature of a Lipase from the Hyperthermophilic Archaeon Pyrococcus furiosus by Covalent Immobilization

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Roberta V. Branco

2015-01-01

Full Text Available A recombinant thermostable lipase (Pf2001Δ60 from the hyperthermophilic Archaeon Pyrococcus furiosus (PFUL was immobilized by hydrophobic interaction on octyl-agarose (octyl PFUL and by covalent bond on aldehyde activated-agarose in the presence of DTT at pH = 7.0 (one-point covalent attachment (glyoxyl-DTT PFUL and on glyoxyl-agarose at pH 10.2 (multipoint covalent attachment (glyoxyl PFUL. The enzyme’s properties, such as optimal temperature and pH, thermostability, and selectivity, were improved by covalent immobilization. The highest enzyme stability at 70°C for 48 h incubation was achieved for glyoxyl PFUL (around 82% of residual activity, whereas glyoxyl-DTT PFUL maintained around 69% activity, followed by octyl PFUL (27% remaining activity. Immobilization on glyoxyl-agarose improved the optimal temperature to 90°C, while the optimal temperature of octyl PFUL was 70°C. Also, very significant changes in activity with different substrates were found. In general, the covalent bond derivatives were more active than octyl PFUL. The E value also depended substantially on the derivative and the conditions used. It was observed that the reaction of glyoxyl-DTT PFUL using methyl mandelate as a substrate at pH 7 presented the best results for enantioselectivity E=22 and enantiomeric excess (ee (% = 91.

Multi-point optimization on meridional shape of a centrifugal pump impeller for performance improvement

Energy Technology Data Exchange (ETDEWEB)

Pei, Ji; Wang, Wen Jie; Yuan, Shouqi [National Research Center of Pumps, Jiangsu University, Zhenjiang (China)

2016-11-15

A wide operating band is important for a pump to safely perform at maximum efficiency while saving energy. To widen the operating range, a multi-point optimization process based on numerical simulations in order to improve impeller performance of a centrifugal pump used in nuclear plant applications is proposed by this research. The Reynolds average Navier Stokes equations are utilized to perform the calculations. The meridional shape of the impeller was optimized based on the following four parameters; shroud arc radius, hub arc radius, shroud angle, and hub angle as the design variables. Efficiencies calculated under 0.6Qd, 1.0Qd and 1.62Qd were selected as the three optimized objectives. The Design of experiment method was applied to generate various impellers while 35 impellers were generated by the Latin hypercube sampling method. A Response surface function based on a second order function was applied to construct a mathematical relationship between the objectives and design variables. A multi-objective genetic algorithm was utilized to solve the response surface function to obtain the best optimized objectives as well as the best combination of design parameters. The results indicated that the pump performance predicted by numerical simulation was in agreement with the experimental performance. The optimized efficiencies based on the three operating conditions were increased by 3.9 %, 6.1 % and 2.6 %, respectively. In addition, the velocity distribution, pressure distribution, streamline and turbulence kinetic energy distribution of the optimized and reference impeller were compared and analyzed to illustrate the performance improvement.

Multi-point optimization on meridional shape of a centrifugal pump impeller for performance improvement

International Nuclear Information System (INIS)

Pei, Ji; Wang, Wen Jie; Yuan, Shouqi

2016-01-01

A wide operating band is important for a pump to safely perform at maximum efficiency while saving energy. To widen the operating range, a multi-point optimization process based on numerical simulations in order to improve impeller performance of a centrifugal pump used in nuclear plant applications is proposed by this research. The Reynolds average Navier Stokes equations are utilized to perform the calculations. The meridional shape of the impeller was optimized based on the following four parameters; shroud arc radius, hub arc radius, shroud angle, and hub angle as the design variables. Efficiencies calculated under 0.6Qd, 1.0Qd and 1.62Qd were selected as the three optimized objectives. The Design of experiment method was applied to generate various impellers while 35 impellers were generated by the Latin hypercube sampling method. A Response surface function based on a second order function was applied to construct a mathematical relationship between the objectives and design variables. A multi-objective genetic algorithm was utilized to solve the response surface function to obtain the best optimized objectives as well as the best combination of design parameters. The results indicated that the pump performance predicted by numerical simulation was in agreement with the experimental performance. The optimized efficiencies based on the three operating conditions were increased by 3.9 %, 6.1 % and 2.6 %, respectively. In addition, the velocity distribution, pressure distribution, streamline and turbulence kinetic energy distribution of the optimized and reference impeller were compared and analyzed to illustrate the performance improvement

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Science.gov (United States)

Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

Efficiency Improvements of Antenna Optimization Using Orthogonal Fractional Experiments

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Yen-Sheng Chen

2015-01-01

Full Text Available This paper presents an extremely efficient method for antenna design and optimization. Traditionally, antenna optimization relies on nature-inspired heuristic algorithms, which are time-consuming due to their blind-search nature. In contrast, design of experiments (DOE uses a completely different framework from heuristic algorithms, reducing the design cycle by formulating the surrogates of a design problem. However, the number of required simulations grows exponentially if a full factorial design is used. In this paper, a much more efficient technique is presented to achieve substantial time savings. By using orthogonal fractional experiments, only a small subset of the full factorial design is required, yet the resultant response surface models are still effective. The capability of orthogonal fractional experiments is demonstrated through three examples, including two tag antennas for radio-frequency identification (RFID applications and one internal antenna for long-term-evolution (LTE handheld devices. In these examples, orthogonal fractional experiments greatly improve the efficiency of DOE, thereby facilitating the antenna design with less simulation runs.

Optimization of Nanowire-Resistance Load Logic Inverter.

Science.gov (United States)

Hashim, Yasir; Sidek, Othman

2015-09-01

This study is the first to demonstrate characteristics optimization of nanowire resistance load inverter. Noise margins and inflection voltage of transfer characteristics are used as limiting factors in this optimization. Results indicate that optimization depends on resistance value. Increasing of load resistor tends to increasing in noise margins until saturation point, increasing load resistor after this point will not improve noise margins significantly.

Optimal setpoint generation for improved fuel temperature performance

International Nuclear Information System (INIS)

Johns, R.M.; Edwards, R.M.

1995-01-01

Nuclear power plant systems feature a high degree of non-linearity and high noise level, and the performance of conventional control systems may degrade when power plants operate under a wide range of conditions, such as startup, test, shutdown, etc. The conventional control system is not intended for nuclear power plant full-range operation. This is the reason that, at present, nuclear power plants rely on manual operations for most wide-range control and only use automatic control around nominal conditions. The availability of new powerful control techniques and mathematical tools has motivated an expanding research effort toward the development of the advanced hybrid feedforward-feedback control system. The planned command input is based on the analysis of a system model in some form in order to improve the performance of the overall system. The use of a feedforward optimal controller to improve the fuel temperature response to a step change in desired reactor power is being demonstrated. The Penn State TRIGA reactor is used as the basis of the reactor model so that validation of the controller may be shown

Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

Science.gov (United States)

Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

2011-01-01

One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

An Improved Genetic Algorithm for Optimal Stationary Energy Storage System Locating and Sizing

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Bin Wang

2014-10-01

Full Text Available The application of a stationary ultra-capacitor energy storage system (ESS in urban rail transit allows for the recuperation of vehicle braking energy for increasing energy savings as well as for a better vehicle voltage profile. This paper aims to obtain the best energy savings and voltage profile by optimizing the location and size of ultra-capacitors. This paper firstly raises the optimization objective functions from the perspectives of energy savings, regenerative braking cancellation and installation cost, respectively. Then, proper mathematical models of the DC (direct current traction power supply system are established to simulate the electrical load-flow of the traction supply network, and the optimization objections are evaluated in the example of a Chinese metro line. Ultimately, a methodology for optimal ultra-capacitor energy storage system locating and sizing is put forward based on the improved genetic algorithm. The optimized result shows that certain preferable and compromised schemes of ESSs’ location and size can be obtained, acting as a compromise between satisfying better energy savings, voltage profile and lower installation cost.

Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight.

Science.gov (United States)

Guo, Siqiu; Zhang, Tao; Song, Yulong; Qian, Feng

2018-04-23

This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.

Further optimization of the M1 PAM VU0453595: Discovery of novel heterobicyclic core motifs with improved CNS penetration.

Science.gov (United States)

Panarese, Joseph D; Cho, Hykeyung P; Adams, Jeffrey J; Nance, Kellie D; Garcia-Barrantes, Pedro M; Chang, Sichen; Morrison, Ryan D; Blobaum, Anna L; Niswender, Colleen M; Stauffer, Shaun R; Conn, P Jeffrey; Lindsley, Craig W

2016-08-01

This Letter describes the continued chemical optimization of the VU0453595 series of M1 positive allosteric modulators (PAMs). By surveying alternative 5,6- and 6,6-heterobicylic cores for the 6,7-dihydro-5H-pyrrolo[3,4-b]pyridine-5-one core of VU453595, we found new cores that engendered not only comparable or improved M1 PAM potency, but significantly improved CNS distribution (Kps 0.3-3.1). Moreover, this campaign provided fundamentally distinct M1 PAM chemotypes, greatly expanding the available structural diversity for this valuable CNS target, devoid of hydrogen-bond donors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

Directory of Open Access Journals (Sweden)

Sumaiya Thaseen Ikram

2016-06-01

Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

Bedtime Blood Pressure Chronotherapy Significantly Improves Hypertension Management.

Science.gov (United States)

Hermida, Ramón C; Ayala, Diana E; Fernández, José R; Mojón, Artemio; Crespo, Juan J; Ríos, María T; Smolensky, Michael H

2017-10-01

Consistent evidence of numerous studies substantiates the asleep blood pressure (BP) mean derived from ambulatory BP monitoring (ABPM) is both an independent and a stronger predictor of cardiovascular disease (CVD) risk than are daytime clinic BP measurements or the ABPM-determined awake or 24-hour BP means. Hence, cost-effective adequate control of sleep-time BP is of marked clinical relevance. Ingestion time, according to circadian rhythms, of hypertension medications of 6 different classes and their combinations significantly improves BP control, particularly sleep-time BP, and reduces adverse effects. Copyright © 2017 Elsevier Inc. All rights reserved.

National Emergency Preparedness and Response: Improving for Incidents of National Significance

National Research Council Canada - National Science Library

Clayton, Christopher M

2006-01-01

The national emergency management system has need of significant improvement in its contingency planning and early consolidation of effort and coordination between federal, state, and local agencies...

Optimal Spatial Subdivision method for improving geometry navigation performance in Monte Carlo particle transport simulation

International Nuclear Information System (INIS)

Chen, Zhenping; Song, Jing; Zheng, Huaqing; Wu, Bin; Hu, Liqin

2015-01-01

Highlights: • The subdivision combines both advantages of uniform and non-uniform schemes. • The grid models were proved to be more efficient than traditional CSG models. • Monte Carlo simulation performance was enhanced by Optimal Spatial Subdivision. • Efficiency gains were obtained for realistic whole reactor core models. - Abstract: Geometry navigation is one of the key aspects of dominating Monte Carlo particle transport simulation performance for large-scale whole reactor models. In such cases, spatial subdivision is an easily-established and high-potential method to improve the run-time performance. In this study, a dedicated method, named Optimal Spatial Subdivision, is proposed for generating numerically optimal spatial grid models, which are demonstrated to be more efficient for geometry navigation than traditional Constructive Solid Geometry (CSG) models. The method uses a recursive subdivision algorithm to subdivide a CSG model into non-overlapping grids, which are labeled as totally or partially occupied, or not occupied at all, by CSG objects. The most important point is that, at each stage of subdivision, a conception of quality factor based on a cost estimation function is derived to evaluate the qualities of the subdivision schemes. Only the scheme with optimal quality factor will be chosen as the final subdivision strategy for generating the grid model. Eventually, the model built with the optimal quality factor will be efficient for Monte Carlo particle transport simulation. The method has been implemented and integrated into the Super Monte Carlo program SuperMC developed by FDS Team. Testing cases were used to highlight the performance gains that could be achieved. Results showed that Monte Carlo simulation runtime could be reduced significantly when using the new method, even as cases reached whole reactor core model sizes

An Improved Particle Swarm Optimization Algorithm and Its Application in the Community Division

Directory of Open Access Journals (Sweden)

Jiang Hao

2016-01-01

Full Text Available With the deepening of the research on complex networks, the method of detecting and classifying social network is springing up. In this essay, the basic particle swarm algorithm is improved based on the GN algorithm. Modularity is taken as a measure of community division [1]. In view of the dynamic network community division, scrolling calculation method is put forward. Experiments show that using the improved particle swarm optimization algorithm can improve the accuracy of the community division and can also get higher value of the modularity in the dynamic community

Improvements, enhancements, and optimizations of COBRA-TF

International Nuclear Information System (INIS)

Salko, R. K.; Avramova, M. N.; Hooper, R.; Palmtag, S.; Popov, E.; Turner, J.

2013-01-01

The Reactor Dynamics and Fuel Management Group (RDFMG) at The Pennsylvania State University (PSU) has become active in the Consortium for Advanced Simulation of Light Water Reactors (CASL) program by delivering, supporting, and further developing CTF, the PSU version of the Coolant Boiling in Rod Arrays - Two Fluids (COBRA-TF) Thermal/Hydraulic (T/H), sub-channel program. New development work on CTF was primarily geared towards decreasing the execution time of the code so that it may eventually be used for performing pin-by-pin, full-core simulations. Great gains have been made through targeting sections of source code for optimization. For example, wall clock time has been reduced for a one-assembly, three-dimensional model, containing ∼9,400 mesh cells, from 9.2 min to 1 min. A further improvement has been reduction in code memory usage, which was previously prohibitive for large models. In conjunction with the run time speedups, this has enabled the simulation of a refined quarter-core model (∼460,000 mesh cells), which saw a reduction in memory usage from over 130 GB to less than 3 GB. In addition to the optimization work, RDFMG has also created a preprocessor utility for the fast and less error-prone generation of CTF input decks. Furthermore, basic post-processing capabilities have been implemented by creating a CTF subroutine for producing Visualization Tool-Kit (VTK) files that output mesh data and associated simulation results. These VTK files can be opened with visualization software. (authors)

Sootblowing optimization for improved boiler performance

Science.gov (United States)

James, John Robert; McDermott, John; Piche, Stephen; Pickard, Fred; Parikh, Neel J.

2012-12-25

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

Optimization of caseinate-coated simvastatin-zein nanoparticles: improved bioavailability and modified release characteristics

Directory of Open Access Journals (Sweden)

Ahmed OA

2015-01-01

Full Text Available Osama AA Ahmed,1,2 Khaled M Hosny,1,3 Majid M Al-Sawahli,1,4 Usama A Fahmy11Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia; 2Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Minia University, Minia, Egypt; 3Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Beni Suef University, Beni Suef, Egypt; 4Holding Company for Biological Products & Vaccines (VACSERA, Cairo, EgyptAbstract: The current study focuses on utilization of the natural biocompatible polymer zein to formulate simvastatin (SMV nanoparticles coated with caseinate, to improve solubility and hence bioavailability, and in addition, to modify SMV-release characteristics. This formulation can be utilized for oral or possible depot parenteral applications. Fifteen formulations were prepared by liquid–liquid phase separation method, according to the Box–Behnken design, to optimize formulation variables. Sodium caseinate was used as an electrosteric stabilizer. The factors studied were: percentage of SMV in the SMV-zein mixture (X1, ethanol concentration (X2, and caseinate concentration (X3. The selected dependent variables were mean particle size (Y1, SMV encapsulation efficiency (Y2, and cumulative percentage of drug permeated after 1 hour (Y3. The diffusion of SMV from the prepared nanoparticles specified by the design was carried out using an automated Franz diffusion cell apparatus. The optimized SMV-zein formula was investigated for in vivo pharmacokinetic parameters compared with an oral SMV suspension. The optimized nanosized SMV-zein formula showed a 131 nm mean particle size and 89% encapsulation efficiency. In vitro permeation studies displayed delayed permeation characteristics, with about 42% and 85% of SMV cumulative amount released after 12 and 48 hours, respectively. Bioavailability estimation in rats revealed an augmentation in SMV bioavailability

SU-E-T-295: Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)

Energy Technology Data Exchange (ETDEWEB)

Zarepisheh, M; Li, R; Xing, L [Stanford UniversitySchool of Medicine, Stanford, CA (United States); Ye, Y [Stanford Univ, Management Science and Engineering, Stanford, Ca (United States); Boyd, S [Stanford University, Electrical Engineering, Stanford, CA (United States)

2014-06-01

Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) and aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly improves

SU-E-T-295: Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)

International Nuclear Information System (INIS)

Zarepisheh, M; Li, R; Xing, L; Ye, Y; Boyd, S

2014-01-01

Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) and aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly improves

Optimizing silicon application to improve salinity tolerance in wheat

Directory of Open Access Journals (Sweden)

A. Ali

2009-05-01

Full Text Available Salinity often suppresses the wheat performance. As wheat is designated as silicon (Si accumulator, hence Si application may alleviate the salinity induced damages. With the objective to combat the salinity stress in wheat by Si application (0, 50, 100, 150 and 200 mg L-1 using calcium silicate, an experiment was conducted on two contrasting wheat genotypes (salt sensitive; Auqab-2000 and salt tolerant; SARC-5 in salinized (10 dS m-1 and non-salinized (2 dS m-1 solutions. Plants were harvested 32 days after transplanting and evaluation was done on the basis of different morphological and analytical characters. Silicon supplementation into the solution culture improved wheat growth and K+/Na+ with reduced Na+ and enhanced K+ uptake. Concomitant improvement in shoot growth was observed; nonetheless the root growth remained unaffected by Si application. Better results were obtained with 150 and 200 mg L-1 of Si which were found almost equally effective. It was concluded that SARC-5 is better than Auqab-2000 against salt stress and Si inclusion into the solution medium is beneficial for wheat and can improve the crop growth both under optimal and salt stressful conditions.

Increasing the statistical significance of entanglement detection in experiments.

Science.gov (United States)

Jungnitsch, Bastian; Niekamp, Sönke; Kleinmann, Matthias; Gühne, Otfried; Lu, He; Gao, Wei-Bo; Chen, Yu-Ao; Chen, Zeng-Bing; Pan, Jian-Wei

2010-05-28

Entanglement is often verified by a violation of an inequality like a Bell inequality or an entanglement witness. Considerable effort has been devoted to the optimization of such inequalities in order to obtain a high violation. We demonstrate theoretically and experimentally that such an optimization does not necessarily lead to a better entanglement test, if the statistical error is taken into account. Theoretically, we show for different error models that reducing the violation of an inequality can improve the significance. Experimentally, we observe this phenomenon in a four-photon experiment, testing the Mermin and Ardehali inequality for different levels of noise. Furthermore, we provide a way to develop entanglement tests with high statistical significance.

Improving compliance in remote healthcare systems through smartphone battery optimization.

Science.gov (United States)

Alshurafa, Nabil; Eastwood, Jo-Ann; Nyamathi, Suneil; Liu, Jason J; Xu, Wenyao; Ghasemzadeh, Hassan; Pourhomayoun, Mohammad; Sarrafzadeh, Majid

2015-01-01

Remote health monitoring (RHM) has emerged as a solution to help reduce the cost burden of unhealthy lifestyles and aging populations. Enhancing compliance to prescribed medical regimens is an essential challenge to many systems, even those using smartphone technology. In this paper, we provide a technique to improve smartphone battery consumption and examine the effects of smartphone battery lifetime on compliance, in an attempt to enhance users' adherence to remote monitoring systems. We deploy WANDA-CVD, an RHM system for patients at risk of cardiovascular disease (CVD), using a wearable smartphone for detection of physical activity. We tested the battery optimization technique in an in-lab pilot study and validated its effects on compliance in the Women's Heart Health Study. The battery optimization technique enhanced the battery lifetime by 192% on average, resulting in a 53% increase in compliance in the study. A system like WANDA-CVD can help increase smartphone battery lifetime for RHM systems monitoring physical activity.

Numerical optimization of composite hip endoprostheses under different loading conditions

Science.gov (United States)

Blake, T. A.; Davy, D. T.; Saravanos, D. A.; Hopkins, D. A.

1992-01-01

The optimization of composite hip implants was investigated. Emphasis was placed on the effect of shape and material tailoring of the implant to improve the implant-bone interaction. A variety of loading conditions were investigated to better understand the relationship between loading and optimization outcome. Comparisons of the initial and optimal models with more complex 3D finite element models were performed. The results indicate that design improvements made using this method result in similar improvements in the 3D models. Although the optimization outcomes were significantly affected by the choice of loading conditions, certain trends were observed that were independent of the applied loading.

Role of intermediate metallic sub-layers in improving the efficiency of kesterite solar cells: concept and optimization

Science.gov (United States)

Ferhati, H.; Djeffal, F.

2018-03-01

In this work, versatile CdS/Cu 2 ZnSnS 4 (CZTS) solar cell designs based on intermediate metallic sub-layers (Au, Ti, and Ag) engineering are proposed for enhancing light-scattering behavior and reducing recombination losses. The idea behind this work is to generate optical confinement regions in the CZTS absorber layer to achieve an improved absorption and appropriate antireflection effects. Moreover, the ultra-thin metal at the CZTS/Mo interface can be helpful for reducing the series resistance, where it behaves like a blocking layer for the Sulfur diffusion. We further combine the proposed designs with Particle Swarm Optimization (PSO)-based approach to achieve broadband absorption and boost the conversion efficiency. It is found that the optimized design with Ti sub-layer improves the CZTS solar cell properties, where it yields 31% improvement in short-circuit current and 60% in the power efficiency over the conventional one. Therefore, the optimized designs provide the opportunity for bridging the gap between improving the optical behavior and reducing the recombination losses.

An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes

Directory of Open Access Journals (Sweden)

Israr Ullah

2017-11-01

Full Text Available In the smart home environment, efficient energy management is a challenging task. Solutions are needed to achieve a high occupant comfort level with minimum energy consumption. User comfort is measured in terms of three fundamental parameters: (a thermal comfort, (b visual comfort and (c air quality. Temperature, illumination and CO 2 sensors are used to collect indoor contextual information. In this paper, we have proposed an improved optimization function to achieve maximum user comfort in the building environment with minimum energy consumption. A comprehensive formulation is done for energy optimization with detailed analysis. The Kalman filter algorithm is used to remove noise in sensor readings by predicting actual parameter values. For optimization, we have used genetic algorithm (GA and particle swarm optimization (PSO algorithms and performed comparative analysis with a baseline scheme on real data collected for a one-month duration in our lab’s indoor environment. Experimental results show that the proposed optimization function has achieved a 27 . 32 % and a 31 . 42 % reduction in energy consumption with PSO and GA, respectively. The user comfort index was also improved by 10 % i.e., from 0 . 86 to 0 . 96 . GA-based optimization results were better than PSO, as it has achieved almost the same user comfort with 4 . 19 % reduced energy consumption. Results show that the proposed optimization function gives better results than the baseline scheme in terms of user comfort and the amount of consumed energy. The proposed system can help with collecting the data about user preferences and energy consumption for long-term analysis and better decision making in the future for efficient resource utilization and overall profit maximization.

Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems

International Nuclear Information System (INIS)

Chen, Gonggui; Liu, Lilan; Song, Peizhu; Du, Yangwei

2014-01-01

Highlights: • New method for MOORPD problem using MOCIPSO and MOIPSO approaches. • Constrain-prior Pareto-dominance method is proposed to meet the constraints. • The limits of the apparent power flow of transmission line are considered. • MOORPD model is built up for MOORPD problem. • The achieved results by MOCIPSO and MOIPSO approaches are better than MOPSO method. - Abstract: Multi-objective optimal reactive power dispatch (MOORPD) seeks to not only minimize power losses, but also improve the stability of power system simultaneously. In this paper, the static voltage stability enhancement is achieved through incorporating L index in MOORPD problem. Chaotic improved PSO-based multi-objective optimization (MOCIPSO) and improved PSO-based multi-objective optimization (MOIPSO) approaches are proposed for solving complex multi-objective, mixed integer nonlinear problems such as minimization of power losses and L index in power systems simultaneously. In MOCIPSO and MOIPSO based optimization approaches, crossover operator is proposed to enhance PSO diversity and improve their global searching capability, and for MOCIPSO based optimization approach, chaotic sequences based on logistic map instead of random sequences is introduced to PSO for enhancing exploitation capability. In the two approaches, constrain-prior Pareto-dominance method (CPM) is proposed to meet the inequality constraints on state variables, the sorting and crowding distance methods are considered to maintain a well distributed Pareto optimal solutions, and moreover, fuzzy set theory is employed to extract the best compromise solution over the Pareto optimal curve. The proposed approaches have been examined and tested in the IEEE 30 bus and the IEEE 57 bus power systems. The performances of MOCIPSO, MOIPSO, and multi-objective PSO (MOPSO) approaches are compared with respect to multi-objective performance measures. The simulation results are promising and confirm the ability of MOCIPSO and

Topology Optimization of Vehicle Body Structure for Improved Ride & Handling

OpenAIRE

Lövgren, Sebastian; Norberg, Emil

2011-01-01

Ride and handling are important areas for safety and improved vehicle control during driving. To meet the demands on ride and handling a number of measures can be taken. This master thesis work has focused on the early design phase. At the early phases of design, the level of details is low and the design freedom is big. By introducing a tool to support the early vehicle body design, the potential of finding more efficient structures increases. In this study, topology optimization of a vehicl...

Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

Directory of Open Access Journals (Sweden)

Fei Wang

2017-07-01

Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

The dual-axis solar tracking system efficiency improving via the drive power consumption optimization

International Nuclear Information System (INIS)

Rambhowan, Y.; Oree, V.

2014-01-01

A major drawback with active dual-axis solar tracking systems is that the power used by the driving mechanism is often drawn from the output power of the solar panel itself. The net energy gain of the photo-voltaic panel is therefore less than its maximum value. This work presents a novel design which uses a three-fold strategy to minimize the power consumed by the tracking mechanism whilst maintaining the power out-put of the photovoltaic panel near its optimal value. The results reveal that the improved tracking system has a significant energy gain of about 43.6% as compared to a fixed photovoltaic panel. Experiments further show that an increase of 1.6% in energy output is achieved over conventional precise dual-axis tracking system. (author)

A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

Directory of Open Access Journals (Sweden)

Shaolong Chen

2016-01-01

Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

Science.gov (United States)

Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong

2014-03-01

A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

Performance Improvement of a Return Channel in a Multistage Centrifugal Compressor Using Multiobjective Optimization.

Science.gov (United States)

Nishida, Yoshifumi; Kobayashi, Hiromi; Nishida, Hideo; Sugimura, Kazuyuki

2013-05-01

The effect of the design parameters of a return channel on the performance of a multistage centrifugal compressor was numerically investigated, and the shape of the return channel was optimized using a multiobjective optimization method based on a genetic algorithm to improve the performance of the centrifugal compressor. The results of sensitivity analysis using Latin hypercube sampling suggested that the inlet-to-outlet area ratio of the return vane affected the total pressure loss in the return channel, and that the inlet-to-outlet radius ratio of the return vane affected the outlet flow angle from the return vane. Moreover, this analysis suggested that the number of return vanes affected both the loss and the flow angle at the outlet. As a result of optimization, the number of return vane was increased from 14 to 22 and the area ratio was decreased from 0.71 to 0.66. The radius ratio was also decreased from 2.1 to 2.0. Performance tests on a centrifugal compressor with two return channels (the original design and optimized design) were carried out using two-stage test apparatus. The measured flow distribution exhibited a swirl flow in the center region and a reversed swirl flow near the hub and shroud sides. The exit flow of the optimized design was more uniform than that of the original design. For the optimized design, the overall two-stage efficiency and pressure coefficient were increased by 0.7% and 1.5%, respectively. Moreover, the second-stage efficiency and pressure coefficient were respectively increased by 1.0% and 3.2%. It is considered that the increase in the second-stage efficiency was caused by the increased uniformity of the flow, and the rise in the pressure coefficient was caused by a decrease in the residual swirl flow. It was thus concluded from the numerical and experimental results that the optimized return channel improved the performance of the multistage centrifugal compressor.

Improved Full-Newton Step O(nL) Infeasible Interior-Point Method for Linear Optimization

OpenAIRE

Gu, G.; Mansouri, H.; Zangiabadi, M.; Bai, Y.Q.; Roos, C.

2009-01-01

We present several improvements of the full-Newton step infeasible interior-point method for linear optimization introduced by Roos (SIAM J. Optim. 16(4):1110–1136, 2006). Each main step of the method consists of a feasibility step and several centering steps. We use a more natural feasibility step, which targets the ?+-center of the next pair of perturbed problems. As for the centering steps, we apply a sharper quadratic convergence result, which leads to a slightly wider neighborhood for th...

Warpage improvement on wheel caster by optimizing the process parameters using genetic algorithm (GA)

Science.gov (United States)

Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.

2017-09-01

In injection moulding process, the defects will always encountered and affected the final product shape and functionality. This study is concerning on minimizing warpage and optimizing the process parameter of injection moulding part. Apart from eliminating product wastes, this project also giving out best recommended parameters setting. This research studied on five parameters. The optimization showed that warpage have been improved 42.64% from 0.6524 mm to 0.30879 mm in Autodesk Moldflow Insight (AMI) simulation result and Genetic Algorithm (GA) respectively.

Design and optimization of a novel organic Rankine cycle with improved boiling process

DEFF Research Database (Denmark)

Andreasen, Jesper Graa; Larsen, U.; Knudsen, Thomas

2015-01-01

to improve the boiling process. Optimizations are carried out for eight hydrocarbon mixtures for hot fluid inlet temperatures at 120 °C and 90 °C, using a genetic algorithm to determine the cycle conditions for which the net power output is maximized. The most promising mixture is an isobutane....../pentane mixture which, for the 90 °C hot fluid inlet temperature case, achieves a 14.5% higher net power output than an optimized organic Rankine cycle using the same mixture. Two parameter studies suggest that optimum conditions for the organic split-cycle are when the temperature profile allows the minimum...

Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor

International Nuclear Information System (INIS)

Fang Hongqing; Chen Long; Shen Zuyi

2011-01-01

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed. Besides the individual best position and the global best position, a nominal average position of the swarm is introduced in IPSO. The performance of IPSO is compared to different PSO variants with five well-known benchmark functions. The experimental results show that the proposed IPSO algorithm improves the searching performance on the benchmark functions. And then, IPSO, as well as other PSO variants, is applied to optimal tuning of Proportional-Integral-Derivative (PID) gains for a typical PID control system of water turbine governor. The computer simulation results of an actual hydro power plant in China show that IPSO algorithm has stable convergence characteristic and good computational ability, and it is an effective and easily implemented method for optimal tuning of PID gains of water turbine governor.

Energy Route Multi-Objective Optimization of Wireless Power Transfer Network: An Improved Cross-Entropy Method

Directory of Open Access Journals (Sweden)

Lijuan Xiang

2017-06-01

Full Text Available This paper identifies the Wireless Power Transfer Network (WPTN as an ideal model for long-distance Wireless Power Transfer (WPT in a certain region with multiple electrical equipment. The schematic circuit and design of each power node and the process of power transmission between the two power nodes are elaborated. The Improved Cross-Entropy (ICE method is proposed as an algorithm to solve for optimal energy route. Non-dominated sorting is introduced for optimization. A demonstration of the optimization result of a 30-nodes WPTN system based on the proposed algorithm proves ICE method to be efficacious and efficiency.

Optimally stopped variational quantum algorithms

Science.gov (United States)

Vinci, Walter; Shabani, Alireza

2018-04-01

Quantum processors promise a paradigm shift in high-performance computing which needs to be assessed by accurate benchmarking measures. In this article, we introduce a benchmark for the variational quantum algorithm (VQA), recently proposed as a heuristic algorithm for small-scale quantum processors. In VQA, a classical optimization algorithm guides the processor's quantum dynamics to yield the best solution for a given problem. A complete assessment of the scalability and competitiveness of VQA should take into account both the quality and the time of dynamics optimization. The method of optimal stopping, employed here, provides such an assessment by explicitly including time as a cost factor. Here, we showcase this measure for benchmarking VQA as a solver for some quadratic unconstrained binary optimization. Moreover, we show that a better choice for the cost function of the classical routine can significantly improve the performance of the VQA algorithm and even improve its scaling properties.

Improvement of RETRAN-MASTER-TORC transient capability and coupling optimization

International Nuclear Information System (INIS)

Cho, J. Y.; Song, J. S.; Joo, H. G.; Seo, K. W.; Whang, D. H.; Lee, C. C.; Zee, S. Q.

2003-11-01

This work is to improve MASTER-TORC transient calculation capability by complementing the previously developed consolidated code system RETRAN- MASTER-TORC, and to reduce the computing time by coupling optimization. The coupling soundness and optimization performance of the consolidated code system are evaluated by solving a YGN3 control bank ejection accident and the OECD Main Steam Line Break(MSLB) benchmark problems. The YGN3 control bank ejection accident is analyzed by the MASTER-TORC system. Most of all results including the transient core power, peak power and time are similar with those from the MASTER-COBRA system. In the computing time, the MASTER- TORC system is proved to be same as the MASTER-COBRA system, which means the coupling is sound and well-optimized. In the analysis of the OECD MSLB benchmark problem, the RETRAN-MASTER-TORC system shows the very similar results with the RETRAN-MASTER-COBRA system. However, minor differences due to fuel conductivity and thermal capacity model are noticed. In TORC, these parameters are treated as constants, while they are modeled as temperature dependent functions in COBRA. Therefore, in the future, TORC need to complement the temperature dependent thermal properties for accurate fuel and cladding temperature calculation. In the computing time for this problem, RETRAN-MASTER-TORC system shows a little bit faster than COBRA case

Optimal Sizing of a Stand-Alone Hybrid Power System Based on Battery/Hydrogen with an Improved Ant Colony Optimization

Directory of Open Access Journals (Sweden)

Weiqiang Dong

2016-09-01

Full Text Available A distributed power system with renewable energy sources is very popular in recent years due to the rapid depletion of conventional sources of energy. Reasonable sizing for such power systems could improve the power supply reliability and reduce the annual system cost. The goal of this work is to optimize the size of a stand-alone hybrid photovoltaic (PV/wind turbine (WT/battery (B/hydrogen system (a hybrid system based on battery and hydrogen (HS-BH for reliable and economic supply. Two objectives that take the minimum annual system cost and maximum system reliability described as the loss of power supply probability (LPSP have been addressed for sizing HS-BH from a more comprehensive perspective, considering the basic demand of load, the profit from hydrogen, which is produced by HS-BH, and an effective energy storage strategy. An improved ant colony optimization (ACO algorithm has been presented to solve the sizing problem of HS-BH. Finally, a simulation experiment has been done to demonstrate the developed results, in which some comparisons have been done to emphasize the advantage of HS-BH with the aid of data from an island of Zhejiang, China.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

Science.gov (United States)

Wang, Xingmei; Hao, Wenqian; Li, Qiming

2017-12-18

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

Directory of Open Access Journals (Sweden)

Íñigo Molina

2012-11-01

Full Text Available This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU times.

A dynamic inertia weight particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Gu Xingsheng

2008-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly

Improvement of the fringe analysis algorithm for wavelength scanning interferometry based on filter parameter optimization.

Science.gov (United States)

Zhang, Tao; Gao, Feng; Muhamedsalih, Hussam; Lou, Shan; Martin, Haydn; Jiang, Xiangqian

2018-03-20

The phase slope method which estimates height through fringe pattern frequency and the algorithm which estimates height through the fringe phase are the fringe analysis algorithms widely used in interferometry. Generally they both extract the phase information by filtering the signal in frequency domain after Fourier transform. Among the numerous papers in the literature about these algorithms, it is found that the design of the filter, which plays an important role, has never been discussed in detail. This paper focuses on the filter design in these algorithms for wavelength scanning interferometry (WSI), trying to optimize the parameters to acquire the optimal results. The spectral characteristics of the interference signal are analyzed first. The effective signal is found to be narrow-band (near single frequency), and the central frequency is calculated theoretically. Therefore, the position of the filter pass-band is determined. The width of the filter window is optimized with the simulation to balance the elimination of the noise and the ringing of the filter. Experimental validation of the approach is provided, and the results agree very well with the simulation. The experiment shows that accuracy can be improved by optimizing the filter design, especially when the signal quality, i.e., the signal noise ratio (SNR), is low. The proposed method also shows the potential of improving the immunity to the environmental noise by adapting the signal to acquire the optimal results through designing an adaptive filter once the signal SNR can be estimated accurately.

Improving transition between power optimization and power limitation of variable speed/variable pitch wind turbines

Energy Technology Data Exchange (ETDEWEB)

Hansen, A D; Bindner, H [Risoe National Lab., Wind Energy and Atmospheric Physics Dept., Roskilde (Denmark); Rebsdorf, A [Vestas Wind Systems A/S, Lem (Denmark)

1999-03-01

The paper summarises and describes the main results of a recently performed study of improving the transition between power optimization and power limitation for variable speed/variable pitch wind turbines. The results show that the capability of varying the generator speed also can be exploited in the transition stage to improve the quality of the generated power. (au)

[Improvement of Phi bodies stain and its clinical significance].

Science.gov (United States)

Gong, Xu-Bo; Lu, Xing-Guo; Yan, Li-Juan; Xiao, Xi-Bin; Wu, Dong; Xu, Gen-Bo; Zhang, Xiao-Hong; Zhao, Xiao-Ying

2009-02-01

The aim of this study was to improve the dyeing method of hydroperoxidase (HPO), to analyze the morphologic features of Phi bodies and to evaluate the clinical application of this method. 128 bone marrow or peripheral blood smears from patients with myeloid and lymphoid malignancies were stained by improved HPO staining. The Phi bodies were observed with detection rate of Phi bodies in different leukemias. 69 acute myeloid leukemia (AML) specimens were chosen randomly, the positive rate and the number of Phi bodies between the improved HPO and POX stain based on the same substrate of 3, 3'diaminobenzidine were compared. The results showed that the shape of bundle-like Phi bodies was variable, long or short. while the nubbly Phi bodies often presented oval and smooth. Club-like Phi bodies were found in M(3). The detection rates of bundle-like Phi bodies in AML M(1)-M(5) were 42.9% (6/14), 83.3% (15/18), 92.0% (23/25), 52.3% (11/21), 33.3% (5/15) respectively, and those of nubbly Phi bodies were 28.6% (4/14), 66.7% (12/18), 11.1% (3/25), 33.3% (7/21), 20.0% (3/15) respectively. The detection rate of bundle-like Phi bodies in M(3) was significantly higher than that in (M(1) + M(2)) or (M(4) + M(5)) groups. The detection rate of nubbly Phi bodies in (M(1) + M(2)) group was higher than that in M(3) group. In conclusion, after improvement of staining method, the HPO stain becomes simple, the detection rate of Phi bodies is higher than that by the previous method, the positive granules are more obvious, and the results become stable. This improved method plays an important role in differentiating AML from ALL, subtyping AML, and evaluating the therapeutic results.

Improved particle swarm optimization algorithm for android medical care IOT using modified parameters.

Science.gov (United States)

Sung, Wen-Tsai; Chiang, Yen-Chun

2012-12-01

This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.

The role of AV and VV optimization for CRT

Directory of Open Access Journals (Sweden)

William W. Brabham, M.D.

2013-06-01

Full Text Available Cardiac resynchronization therapy is an effective therapy for patients with left ventricular systolic dysfunction and a ventricular conduction delay; however, approximately 30% of patients do not experience significant clinical improvement with this treatment. Modern devices allow individualized programming of the AV delay and VV offset, which offer the possibility of improving clinical response rates with optimized programming. AV and VV delay optimization techniques have included echocardiography, device-based algorithms, and several other novel noninvasive techniques. While an acute improvement in hemodynamic function has been clearly demonstrated with optimized device settings, long-term clinical benefit is limited. In the majority of cases, an empiric AV delay with simultaneous biventricular or left ventricular pacing is adequate. The value of optimization of these intervals in “non-responders” still requires further investigation.

Design Parameter Optimization of a Silicon-Based Grating Waveguide for Performance Improvement in Biochemical Sensor Application.

Science.gov (United States)

Hong, Yoo-Seung; Cho, Chun-Hyung; Sung, Hyuk-Kee

2018-03-05

We performed numerical analysis and design parameter optimization of a silicon-based grating waveguide refractive index (RI) sensor. The performance of the grating waveguide RI sensor was determined by the full-width at half-maximum (FWHM) and the shift in the resonance wavelength in the transmission spectrum. The transmission extinction, a major figure-of-merit of an RI sensor that reflects both FWHM and resonance shift performance, could be significantly improved by the proper determination of three major grating waveguide parameters: duty ratio, grating period, and etching depth. We analyzed the transmission characteristics of the grating waveguide under various design parameter conditions using a finite-difference time domain method. We achieved a transmission extinction improvement of >26 dB under a given bioenvironmental target change by the proper choice of the design procedure and parameters. This design procedure and choice of appropriate parameters would enable the widespread application of silicon-based grating waveguide in high-performance RI biochemical sensor.

Planning of distributed generation in distribution network based on improved particle swarm optimization algorithm

Science.gov (United States)

Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng

2018-02-01

Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

Institute of Scientific and Technical Information of China (English)

Lili Tao; Bin Xu; Zhihua Hu; Weimin Zhong

2017-01-01

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta-neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob-lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

Improving the quality of learning in science through optimization of lesson study for learning community

Science.gov (United States)

Setyaningsih, S.

2018-03-01

Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.

OPTIMIZING LIFESTYLE IMPROVES GLYCEMIC PROFILE IN PATIENTS AT RISK FOR DIABETES MELLITUS

Directory of Open Access Journals (Sweden)

Rucsandra Dănciulescu Miulescu

2009-10-01

Full Text Available There is a pandemic of type 2 diabetes mellitus due to urban and sedentary lifestyle, ageing and obesity.The most important means to prevent this disease is to optimize the lifestyle.Our study aimed to follow-up the effect of moderate caloric restriction and increase of physical activityon clinical and metabolic parameters in persons at risk to develop type 2 diabetes.Twenty-three overweight or obese patients with either altered fasting glucose or altered glucosetolerance were included in this study. They were followed up for 2 years for clinical progress and metabolicprofile, while on lifestyle counseling.The dietary and physical recommendations to improve lifestyle were followed by a small reduction inthe BMI, total cholesterol, systolic and diastolic blood pressure, together with an increase of HDL at 1 and 2years of dietary counseling. However there was a significant reduction in abdominal circumference, fastingglycemia and glycemia at 2 hours during oral glucose tolerance test.The small reduction in BMI indicates the need of a more intensive lifestyle conseling.

Contextual cueing improves attentional guidance, even when guidance is supposedly optimal.

Science.gov (United States)

Harris, Anthony M; Remington, Roger W

2017-05-01

Visual search through previously encountered contexts typically produces reduced reaction times compared with search through novel contexts. This contextual cueing benefit is well established, but there is debate regarding its underlying mechanisms. Eye-tracking studies have consistently shown reduced number of fixations with repetition, supporting improvements in attentional guidance as the source of contextual cueing. However, contextual cueing benefits have been shown in conditions in which attentional guidance should already be optimal-namely, when attention is captured to the target location by an abrupt onset, or under pop-out conditions. These results have been used to argue for a response-related account of contextual cueing. Here, we combine eye tracking with response time to examine the mechanisms behind contextual cueing in spatially cued and pop-out conditions. Three experiments find consistent response time benefits with repetition, which appear to be driven almost entirely by a reduction in number of fixations, supporting improved attentional guidance as the mechanism behind contextual cueing. No differences were observed in the time between fixating the target and responding-our proxy for response related processes. Furthermore, the correlation between contextual cueing magnitude and the reduction in number of fixations on repeated contexts approaches 1. These results argue strongly that attentional guidance is facilitated by familiar search contexts, even when guidance is near-optimal. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Directory of Open Access Journals (Sweden)

Yongpeng Shen

2016-02-01

Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

Geometry Optimization of DC/RF Photoelectron Gun

CERN Document Server

Chen Ping; Yu, David

2005-01-01

Pre-acceleration of photoelectrons in a pulsed, high voltage, short, dc gap and its subsequent injection into an rf gun is a promising method to improve electron beam emittance in rf accelerators. Simulation work has been performed in order to optimize the geometric shapes of a dc/rf gun and improve electron beam properties. Variations were made on cathode and anode shapes, dc gap distance, and inlet shape of the rf cavity. Simulations showed that significant improvement on the normalized emittance (< 1 mm-mrad), compared to a dc gun with flat cathode, could be obtained after the geometric shapes of the gun were optimized.

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

NARCIS (Netherlands)

Wu, Yue; Li, Q.; Hu, Qingjie; Borgart, A.

2017-01-01

Firefly Algorithm (FA, for short) is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly,

Development of an improved genetic algorithm and its application in the optimal design of ship nuclear power system

International Nuclear Information System (INIS)

Jia Baoshan; Yu Jiyang; You Songbo

2005-01-01

This article focuses on the development of an improved genetic algorithm and its application in the optimal design of the ship nuclear reactor system, whose goal is to find a combination of system parameter values that minimize the mass or volume of the system given the power capacity requirement and safety criteria. An improved genetic algorithm (IGA) was developed using an 'average fitness value' grouping + 'specified survival probability' rank selection method and a 'separate-recombine' duplication operator. Combining with a simulated annealing algorithm (SAA) that continues the local search after the IGA reaches a satisfactory point, the algorithm gave satisfactory optimization results from both search efficiency and accuracy perspectives. This IGA-SAA algorithm successfully solved the design optimization problem of ship nuclear power system. It is an advanced and efficient methodology that can be applied to the similar optimization problems in other areas. (authors)

Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

Science.gov (United States)

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

Application of Six Sigma Robust Optimization in Sheet Metal Forming

International Nuclear Information System (INIS)

Li, Y.Q.; Cui, Z.S.; Ruan, X.Y.; Zhang, D.J.

2005-01-01

Numerical simulation technology and optimization method have been applied in sheet metal forming process to improve design quality and shorten design cycle. While the existence of fluctuation in design variables or operation condition has great influence on the quality. In addition to that, iterative solution in numerical simulation and optimization usually take huge computational time or endure expensive experiment cost In order to eliminate effect of perturbations in design and improve design efficiency, a CAE-based six sigma robust design method is developed in this paper. In the six sigma procedure for sheet metal forming, statistical technology and dual response surface approximate model as well as algorithm of 'Design for Six Sigma (DFSS)' are integrated together to perform reliability optimization and robust improvement. A deep drawing process of a rectangular cup is taken as an example to illustrate the method. The optimization solutions show that the proposed optimization procedure not only improves significantly the reliability and robustness of the forming quality, but also increases optimization efficiency with approximate model

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

Directory of Open Access Journals (Sweden)

Jing Xu

2016-07-01

Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

High Performance with Prescriptive Optimization and Debugging

DEFF Research Database (Denmark)

Jensen, Nicklas Bo

parallelization and automatic vectorization is attractive as it transparently optimizes programs. The thesis contributes an improved dependence analysis for explicitly parallel programs. These improvements lead to more loops being vectorized, on average we achieve a speedup of 1.46 over the existing dependence...... analysis and vectorizer in GCC. Automatic optimizations often fail for theoretical and practical reasons. When they fail we argue that a hybrid approach can be effective. Using compiler feedback, we propose to use the programmer’s intuition and insight to achieve high performance. Compiler feedback...... enlightens the programmer why a given optimization was not applied, and suggest how to change the source code to make it more amenable to optimizations. We show how this can yield significant speedups and achieve 2.4 faster execution on a real industrial use case. To aid in parallel debugging we propose...

Computer optimization of noncoplanar beam setups improves stereotactic treatment of liver tumors

International Nuclear Information System (INIS)

Pooter, Jacco A. de; Mendez Romero, Alejandra; Jansen, Wim; Storchi, Pascal; Woudstra, Evert; Levendag, Peter C.; Heijmen, Ben

2006-01-01

Purpose: To investigate whether computer-optimized fully noncoplanar beam setups may improve treatment plans for the stereotactic treatment of liver tumors. Methods: An algorithm for automated beam orientation and weight selection (Cycle) was extended for noncoplanar stereotactic treatments. For 8 liver patients previously treated in our clinic using a prescription isodose of 65%, Cycle was used to generate noncoplanar and coplanar plans with the highest achievable minimum planning target volume (PTV) dose for the clinically delivered isocenter and mean liver doses, while not violating the clinically applied hard planning constraints. The clinical and the optimized coplanar and noncoplanar plans were compared, with respect to D PTV,99% , the dose received by 99% of the PTV, the PTV generalized equivalent uniform dose (gEUD), and the compliance with the clinical constraints. Results: For each patient, the ratio between D PTV,99% and D isoc , and the gEUD -5 and gEUD -2 values of the optimized noncoplanar plan were higher than for the clinical plan with an average increase of respectively 18.8% (range, 7.8-24.0%), 6.4 Gy (range, 3.4-11.8 Gy), and 10.3 Gy (range, 6.7-12.5). D PTV,99% /D isoc , gEUD -5 , and gEUD -2 of the optimized noncoplanar plan was always higher than for the optimized coplanar plan with an average increase of, respectively, 4.5% (range, 0.2-9.7%), 2.7 Gy (range, 0.6-9.7 Gy), and 3.4 Gy (range, 0.6-9.9 Gy). All plans were within the imposed hard constraints. On average, the organs at risk were better spared with the optimized noncoplanar plan than with the optimized coplanar plan and the clinical plan. Conclusions: The use of automatically generated, fully noncoplanar beam setups results in plans that are favorable compared with coplanar techniques. Because of the automation, we found that the planning workload can be decreased from 1 to 2 days to 1 to 2 h

Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

Directory of Open Access Journals (Sweden)

Sen Zhang

2015-01-01

Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

Evaluation of GCC optimization parameters

Directory of Open Access Journals (Sweden)

Rodrigo D. Escobar

2012-12-01

Full Text Available Compile-time optimization of code can result in significant performance gains. The amount of these gains varies widely depending upon the code being optimized, the hardware being compiled for, the specific performance increase attempted (e.g. speed, throughput, memory utilization, etc. and the used compiler. We used the latest version of the SPEC CPU 2006 benchmark suite to help gain an understanding of possible performance improvements using GCC (GNU Compiler Collection options focusing mainly on speed gains made possible by tuning the compiler with the standard compiler optimization levels as well as a specific compiler option for the hardware processor. We compared the best standardized tuning options obtained for a core i7 processor, to the same relative options used on a Pentium4 to determine whether the GNU project has improved its performance tuning capabilities for specific hardware over time.

Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

Directory of Open Access Journals (Sweden)

Dongxiao Niu

2018-01-01

Full Text Available The electric power industry is of great significance in promoting social and economic development and improving people’s living standards. Power grid construction is a necessary part of infrastructure construction, whose sustainability plays an important role in economic development, environmental protection and social progress. In order to effectively evaluate the sustainability of power grid construction projects, in this paper, we first identified 17 criteria from four dimensions including economy, technology, society and environment to establish the evaluation criteria system. After that, the grey incidence analysis was used to modify the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS, which made it possible to evaluate the sustainability of electric power construction projects based on visual angle of similarity and nearness. Then, in order to simplify the procedure of experts scoring and computation, on the basis of evaluation results of the improved TOPSIS, the model using Modified Fly Optimization Algorithm (MFOA to optimize the Least Square Support Vector Machine (LSSVM was established. Finally, a numerical example was given to demonstrate the effectiveness of the proposed model.

Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

Science.gov (United States)

He, Yaoyao; Yang, Shanlin; Xu, Qifa

2013-07-01

In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.

Improving Loop Dependence Analysis

DEFF Research Database (Denmark)

Jensen, Nicklas Bo; Karlsson, Sven

2017-01-01

Programmers can no longer depend on new processors to have significantly improved single-thread performance. Instead, gains have to come from other sources such as the compiler and its optimization passes. Advanced passes make use of information on the dependencies related to loops. We improve th...

Development of Pangasius steaks by improved sous-vide technology and its process optimization.

Science.gov (United States)

Kumari, Namita; Singh, Chongtham Baru; Kumar, Raushan; Martin Xavier, K A; Lekshmi, Manjusha; Venkateshwarlu, Gudipati; Balange, Amjad K

2016-11-01

The present study embarked on the objective of optimizing improved sous - vide processing condition for development of ready-to-cook Pangasius steaks with extended shelf-life using response surface methodology. For the development of improved sous - vide cooked product, Pangasius steaks were treated with additional hurdles in various combinations for optimization. Based on the study, suitable combination of chitosan and spices was selected which enhanced antimicrobial and oxidative stability of the product. The Box-Behnken experimental design with 15 trials per model was adopted for designing the experiment to know the effect of independent variables, namely chitosan concentration (X 1 ), cooking time (X 2 ) and cooking temperature (X 3 ) on dependent variable i.e. TBARS value (Y 1 ). From RSM generated model, the optimum condition for sous - vide processing of Pangasius steaks were 1.08% chitosan concentration, 70.93 °C of cooking temperature and 16.48 min for cooking time and predicted minimum value of multiple response optimal condition was Y = 0.855 mg MDA/Kg of fish. The high correlation coefficient (R 2  = 0.975) between the model and the experimental data showed that the model was able to efficiently predict processing condition for development of sous - vide processed Pangasius steaks. This research may help the processing industries and Pangasius fish farmer as it provides an alternative low cost technology for the proper utilization of Pangasius .

An improved fast and elitist multi-objective genetic algorithm-ANSGA-II for multi-objective optimization of inverse radiotherapy treatment planning

International Nuclear Information System (INIS)

Cao Ruifen; Li Guoli; Song Gang; Zhao Pan; Lin Hui; Wu Aidong; Huang Chenyu; Wu Yican

2007-01-01

Objective: To provide a fast and effective multi-objective optimization algorithm for inverse radiotherapy treatment planning system. Methods: Non-dominated Sorting Genetic Algorithm-NSGA-II is a representative of multi-objective evolutionary optimization algorithms and excels the others. The paper produces ANSGA-II that makes use of advantage of NSGA-II, and uses adaptive crossover and mutation to improve its flexibility; according the character of inverse radiotherapy treatment planning, the paper uses the pre-known knowledge to generate individuals of every generation in the course of optimization, which enhances the convergent speed and improves efficiency. Results: The example of optimizing average dose of a sheet of CT, including PTV, OAR, NT, proves the algorithm could find satisfied solutions in several minutes. Conclusions: The algorithm could provide clinic inverse radiotherapy treatment planning system with selection of optimization algorithms. (authors)

Welding Robot Collision-Free Path Optimization

Directory of Open Access Journals (Sweden)

Xuewu Wang

2017-02-01

Full Text Available Reasonable welding path has a significant impact on welding efficiency, and a collision-free path should be considered first in the process of welding robot path planning. The shortest path length is considered as an optimization objective, and obstacle avoidance is considered as the constraint condition in this paper. First, a grid method is used as a modeling method after the optimization objective is analyzed. For local collision-free path planning, an ant colony algorithm is selected as the search strategy. Then, to overcome the shortcomings of the ant colony algorithm, a secondary optimization is presented to improve the optimization performance. Finally, the particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the desired welding path can be obtained based on the optimization strategy.

Improved Cat Swarm Optimization for Simultaneous Allocation of DSTATCOM and DGs in Distribution Systems

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Neeraj Kanwar

2015-01-01

Full Text Available This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.

Long-term lifestyle intervention with optimized high-intensity interval training improves body composition, cardiometabolic risk, and exercise parameters in patients with abdominal obesity.

Science.gov (United States)

Gremeaux, Vincent; Drigny, Joffrey; Nigam, Anil; Juneau, Martin; Guilbeault, Valérie; Latour, Elise; Gayda, Mathieu

2012-11-01

The aim of this study was to study the impact of a combined long-term lifestyle and high-intensity interval training intervention on body composition, cardiometabolic risk, and exercise tolerance in overweight and obese subjects. Sixty-two overweight and obese subjects (53.3 ± 9.7 yrs; mean body mass index, 35.8 ± 5 kg/m(2)) were retrospectively identified at their entry into a 9-mo program consisting of individualized nutritional counselling, optimized high-intensity interval exercise, and resistance training two to three times a week. Anthropometric measurements, cardiometabolic risk factors, and exercise tolerance were measured at baseline and program completion. Adherence rate was 97%, and no adverse events occurred with high-intensity interval exercise training. Exercise training was associated with a weekly energy expenditure of 1582 ± 284 kcal. Clinically and statistically significant improvements were observed for body mass (-5.3 ± 5.2 kg), body mass index (-1.9 ± 1.9 kg/m(2)), waist circumference (-5.8 ± 5.4 cm), and maximal exercise capacity (+1.26 ± 0.84 metabolic equivalents) (P high-density lipoprotein ratio were also significantly improved (P body mass and waist circumference loss were baseline body mass index and resting metabolic rate; those for body mass index decrease were baseline waist circumference and triglyceride/high-density lipoprotein cholesterol ratio. A long-term lifestyle intervention with optimized high-intensity interval exercise improves body composition, cardiometabolic risk, and exercise tolerance in obese subjects. This intervention seems safe, efficient, and well tolerated and could improve adherence to exercise training in this population.

Measurement of uranium in soil environment optimization of liquid fluorescent method improvement

International Nuclear Information System (INIS)

Qin Guangcheng; Li Yan

2013-01-01

Measurement of uranium in soil environment were introduced in this paper optimization improvement fluid fluorescence analysis method. Use 'on the determination of uranium in soil, rocks, etc. Samples of liquid fluorescent method' when measuring low environment soil samples can not meet the required precision of 8% or less in gansu province and method detection limit of 0.3 mg/kg or less. In affecting the method detection limit, recovery rate and precision of the soil sample decomposition temperature, measuring the temperature of the sample, sample pH value measurement, the background fluorescence measurement condition optimization of analysis is determined, the method detection limit of 0.133 mg/kg, the average recovery rate was 96.6%, the precision is 3.80%. The experimental results show that the method can meet the requirements for determination of trace uranium m environment soil samples. (authors)

An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

Directory of Open Access Journals (Sweden)

Xuanhu He

2015-03-01

Full Text Available Optimal power flow (OPF objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

Optimizing of Make Up Air Performance for Commercial Kitchen Ventilation Improvement

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Manshoor B.

2014-07-01

Full Text Available A commercial kitchen is a complicated environment where multiple components of a ventilation system including kitchen hood, exhaust fan, air supply, and make up air systems work together but not always in unison. For the commercial kitchen environment, make up air systems used to control the kitchen space from unwanted odor and thermal confort. Make air systems for commercial kitchen already established. However, an optimization is important to determine the most suitable make air systems and at the same time can improve the thermal comfort in the working space. In this study, a simulation work was conducted to investigate a suitable supply air velocity to optimize the make up air for kitchen ventilation system. In order to achieve the objectives, ANSYS FLUENT software (CFD was used to carry out the simulation and analysis. 3D kitchen space with 10m x 8m x 3m with air supply velocity was set to 0 m/s, 0.14 m/s, 0.28 m/s and 0.42 m/s. From the simulation work, the velocity of air flow tested which is 0.28 m/s is enough to control the heat and give an enough comfort to the working space for the size of kitchen simulated. Well implementation of the make up air in the kitchen hood can improve an air quality in the commercial kitchen and also keep the kitchen space comfortable to the workers.

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI.

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Nathan W Churchill

Full Text Available BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the "pipeline" significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard "fixed" preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each, demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

Science.gov (United States)

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Recent developments in KTF. Code optimization and improved numerics

International Nuclear Information System (INIS)

Jimenez, Javier; Avramova, Maria; Sanchez, Victor Hugo; Ivanov, Kostadin

2012-01-01

The rapid increase of computer power in the last decade facilitated the development of high fidelity simulations in nuclear engineering allowing a more realistic and accurate optimization as well as safety assessment of reactor cores and power plants compared to the legacy codes. Thermal hydraulic subchannel codes together with time dependent neutron transport codes are the options of choice for an accurate prediction of local safety parameters. Moreover, fast running codes with the best physical models are needed for high fidelity coupled thermal hydraulic / neutron kinetic solutions. Hence at KIT, different subchannel codes such as SUBCHANFLOW and KTF are being improved, validated and coupled with different neutron kinetics solutions. KTF is a subchannel code developed for best-estimate analysis of both Pressurized Water Reactor (PWR) and BWR. It is based on the Pennsylvania State University (PSU) version of COBRA-TF (Coolant Boling in Rod Arrays Two Fluids) named CTF. In this paper, the investigations devoted to the enhancement of the code numeric and informatics structure are presented and discussed. By some examples the gain on code speed-up will be demonstrated and finally an outlook of further activities concentrated on the code improvements will be given. (orig.)

Recent developments in KTF. Code optimization and improved numerics

Energy Technology Data Exchange (ETDEWEB)

Jimenez, Javier; Avramova, Maria; Sanchez, Victor Hugo; Ivanov, Kostadin [Karlsruhe Institute of Technology (KIT) (Germany). Inst. for Neutron Physics and Reactor Technology (INR)

2012-11-01

The rapid increase of computer power in the last decade facilitated the development of high fidelity simulations in nuclear engineering allowing a more realistic and accurate optimization as well as safety assessment of reactor cores and power plants compared to the legacy codes. Thermal hydraulic subchannel codes together with time dependent neutron transport codes are the options of choice for an accurate prediction of local safety parameters. Moreover, fast running codes with the best physical models are needed for high fidelity coupled thermal hydraulic / neutron kinetic solutions. Hence at KIT, different subchannel codes such as SUBCHANFLOW and KTF are being improved, validated and coupled with different neutron kinetics solutions. KTF is a subchannel code developed for best-estimate analysis of both Pressurized Water Reactor (PWR) and BWR. It is based on the Pennsylvania State University (PSU) version of COBRA-TF (Coolant Boling in Rod Arrays Two Fluids) named CTF. In this paper, the investigations devoted to the enhancement of the code numeric and informatics structure are presented and discussed. By some examples the gain on code speed-up will be demonstrated and finally an outlook of further activities concentrated on the code improvements will be given. (orig.)

Recent Progress on Data-Based Optimization for Mineral Processing Plants

Directory of Open Access Journals (Sweden)

Jinliang Ding

2017-04-01

Full Text Available In the globalized market environment, increasingly significant economic and environmental factors within complex industrial plants impose importance on the optimization of global production indices; such optimization includes improvements in production efficiency, product quality, and yield, along with reductions of energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelligence optimization methods and technologies in improving the performance of global production indices in mineral processing. First, we provide the problem description. Next, we summarize recent progress in data-based optimization for mineral processing plants. This optimization consists of four layers: optimization of the target values for monthly global production indices, optimization of the target values for daily global production indices, optimization of the target values for operational indices, and automation systems for unit processes. We briefly overview recent progress in each of the different layers. Finally, we point out opportunities for future works in data-based optimization for mineral processing plants.

Improved decomposition–coordination and discrete differential dynamic programming for optimization of large-scale hydropower system

International Nuclear Information System (INIS)

Li, Chunlong; Zhou, Jianzhong; Ouyang, Shuo; Ding, Xiaoling; Chen, Lu

2014-01-01

Highlights: • Optimization of large-scale hydropower system in the Yangtze River basin. • Improved decomposition–coordination and discrete differential dynamic programming. • Generating initial solution randomly to reduce generation time. • Proposing relative coefficient for more power generation. • Proposing adaptive bias corridor technology to enhance convergence speed. - Abstract: With the construction of major hydro plants, more and more large-scale hydropower systems are taking shape gradually, which brings up a challenge to optimize these systems. Optimization of large-scale hydropower system (OLHS), which is to determine water discharges or water levels of overall hydro plants for maximizing total power generation when subjecting to lots of constrains, is a high dimensional, nonlinear and coupling complex problem. In order to solve the OLHS problem effectively, an improved decomposition–coordination and discrete differential dynamic programming (IDC–DDDP) method is proposed in this paper. A strategy that initial solution is generated randomly is adopted to reduce generation time. Meanwhile, a relative coefficient based on maximum output capacity is proposed for more power generation. Moreover, an adaptive bias corridor technology is proposed to enhance convergence speed. The proposed method is applied to long-term optimal dispatches of large-scale hydropower system (LHS) in the Yangtze River basin. Compared to other methods, IDC–DDDP has competitive performances in not only total power generation but also convergence speed, which provides a new method to solve the OLHS problem

Improving best-phase image quality in cardiac CT by motion correction with MAM optimization

Energy Technology Data Exchange (ETDEWEB)

Rohkohl, Christopher; Bruder, Herbert; Stierstorfer, Karl [Siemens AG, Healthcare Sector, Siemensstrasse 1, 91301 Forchheim (Germany); Flohr, Thomas [Siemens AG, Healthcare Sector, Siemensstrasse 1, 91301 Forchheim (Germany); Institute of Diagnostic Radiology, Eberhard Karls University, Hoppe-Seyler-Str. 3, 72076 Tuebingen (Germany)

2013-03-15

Purpose: Research in image reconstruction for cardiac CT aims at using motion correction algorithms to improve the image quality of the coronary arteries. The key to those algorithms is motion estimation, which is currently based on 3-D/3-D registration to align the structures of interest in images acquired in multiple heart phases. The need for an extended scan data range covering several heart phases is critical in terms of radiation dose to the patient and limits the clinical potential of the method. Furthermore, literature reports only slight quality improvements of the motion corrected images when compared to the most quiet phase (best-phase) that was actually used for motion estimation. In this paper a motion estimation algorithm is proposed which does not require an extended scan range but works with a short scan data interval, and which markedly improves the best-phase image quality. Methods: Motion estimation is based on the definition of motion artifact metrics (MAM) to quantify motion artifacts in a 3-D reconstructed image volume. The authors use two different MAMs, entropy, and positivity. By adjusting the motion field parameters, the MAM of the resulting motion-compensated reconstruction is optimized using a gradient descent procedure. In this way motion artifacts are minimized. For a fast and practical implementation, only analytical methods are used for motion estimation and compensation. Both the MAM-optimization and a 3-D/3-D registration-based motion estimation algorithm were investigated by means of a computer-simulated vessel with a cardiac motion profile. Image quality was evaluated using normalized cross-correlation (NCC) with the ground truth template and root-mean-square deviation (RMSD). Four coronary CT angiography patient cases were reconstructed to evaluate the clinical performance of the proposed method. Results: For the MAM-approach, the best-phase image quality could be improved for all investigated heart phases, with a maximum

Improving best-phase image quality in cardiac CT by motion correction with MAM optimization

International Nuclear Information System (INIS)

Rohkohl, Christopher; Bruder, Herbert; Stierstorfer, Karl; Flohr, Thomas

2013-01-01

Purpose: Research in image reconstruction for cardiac CT aims at using motion correction algorithms to improve the image quality of the coronary arteries. The key to those algorithms is motion estimation, which is currently based on 3-D/3-D registration to align the structures of interest in images acquired in multiple heart phases. The need for an extended scan data range covering several heart phases is critical in terms of radiation dose to the patient and limits the clinical potential of the method. Furthermore, literature reports only slight quality improvements of the motion corrected images when compared to the most quiet phase (best-phase) that was actually used for motion estimation. In this paper a motion estimation algorithm is proposed which does not require an extended scan range but works with a short scan data interval, and which markedly improves the best-phase image quality. Methods: Motion estimation is based on the definition of motion artifact metrics (MAM) to quantify motion artifacts in a 3-D reconstructed image volume. The authors use two different MAMs, entropy, and positivity. By adjusting the motion field parameters, the MAM of the resulting motion-compensated reconstruction is optimized using a gradient descent procedure. In this way motion artifacts are minimized. For a fast and practical implementation, only analytical methods are used for motion estimation and compensation. Both the MAM-optimization and a 3-D/3-D registration-based motion estimation algorithm were investigated by means of a computer-simulated vessel with a cardiac motion profile. Image quality was evaluated using normalized cross-correlation (NCC) with the ground truth template and root-mean-square deviation (RMSD). Four coronary CT angiography patient cases were reconstructed to evaluate the clinical performance of the proposed method. Results: For the MAM-approach, the best-phase image quality could be improved for all investigated heart phases, with a maximum

A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

International Nuclear Information System (INIS)

Yu, Feng; Xu, Xiaozhong

2014-01-01

Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms

Bionic optimization in structural design stochastically based methods to improve the performance of parts and assemblies

CERN Document Server

Gekeler, Simon

2016-01-01

The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study’s parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware. Bionic Optimization means finding the best solution to a problem using methods found in nature. As Evolutionary Strategies and Particle Swarm Optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them. A set of sample applications shows how Bionic Optimization works in practice. From academic studies on simple fra...

VDLLA: A virtual daddy-long legs optimization

Science.gov (United States)

Yaakub, Abdul Razak; Ghathwan, Khalil I.

2016-08-01

Swarm intelligence is a strong optimization algorithm based on a biological behavior of insects or animals. The success of any optimization algorithm is depending on the balance between exploration and exploitation. In this paper, we present a new swarm intelligence algorithm, which is based on daddy long legs spider (VDLLA) as a new optimization algorithm with virtual behavior. In VDLLA, each agent (spider) has nine positions which represent the legs of spider and each position represent one solution. The proposed VDLLA is tested on four standard functions using average fitness, Medium fitness and standard deviation. The results of proposed VDLLA have been compared against Particle Swarm Optimization (PSO), Differential Evolution (DE) and Bat Inspired Algorithm (BA). Additionally, the T-Test has been conducted to show the significant deference between our proposed and other algorithms. VDLLA showed very promising results on benchmark test functions for unconstrained optimization problems and also significantly improved the original swarm algorithms.

An optimized BWR fuel lattice for improved fuel utilization

International Nuclear Information System (INIS)

Bernander, O.; Helmersson, S.; Schoen, C.G.

1984-01-01

Optimization of the BWR fuel lattice has evolved into the water cross concept, termed ''SVEA'', whereby the improved moderation within bundles augments reactivity and thus improves fuel cycle economy. The novel design introduces into the assembly a cruciform and double-walled partition containing nonboiling water, thus forming four subchannels, each of which holds a 4x4 fuel rod bundle. In Scandinavian BWRs - for which commercial SVEA reloads are now scheduled - the reactivity gain is well exploited without adverse impact in other respects. In effect, the water cross design improves both mechanical and thermal-hydraulic performance. Increased average burnup is also promoted through achieving flatter local power distributions. The fuel utilization savings are in the order of 10%, depending on the basis of comparison, e.g. choice of discharge burnup and lattice type. This paper reviews the design considerations and the fuel utilization benefits of the water cross fuel for non-Scandinavian BWRs which have somewhat different core design parameters relative to ASEA-ATOM reactors. For one design proposal, comparisons are made with current standard 8x8 fuel rod bundles as well as with 9x9 type fuel in reactors with symmetric or asymmetric inter-assembly water gaps. The effect on reactivity coefficients and shutdown margin are estimated and an assessment is made of thermal-hydraulic properties. Consideration is also given to a novel and advantageous way of including mixed-oxide fuel in BWR reloads. (author)

The Sizing and Optimization Language (SOL): A computer language to improve the user/optimizer interface

Science.gov (United States)

Lucas, S. H.; Scotti, S. J.

1989-01-01

The nonlinear mathematical programming method (formal optimization) has had many applications in engineering design. A figure illustrates the use of optimization techniques in the design process. The design process begins with the design problem, such as the classic example of the two-bar truss designed for minimum weight as seen in the leftmost part of the figure. If formal optimization is to be applied, the design problem must be recast in the form of an optimization problem consisting of an objective function, design variables, and constraint function relations. The middle part of the figure shows the two-bar truss design posed as an optimization problem. The total truss weight is the objective function, the tube diameter and truss height are design variables, with stress and Euler buckling considered as constraint function relations. Lastly, the designer develops or obtains analysis software containing a mathematical model of the object being optimized, and then interfaces the analysis routine with existing optimization software such as CONMIN, ADS, or NPSOL. This final state of software development can be both tedious and error-prone. The Sizing and Optimization Language (SOL), a special-purpose computer language whose goal is to make the software implementation phase of optimum design easier and less error-prone, is presented.

Optimality Conditions in Vector Optimization

CERN Document Server

Jiménez, Manuel Arana; Lizana, Antonio Rufián

2011-01-01

Vector optimization is continuously needed in several science fields, particularly in economy, business, engineering, physics and mathematics. The evolution of these fields depends, in part, on the improvements in vector optimization in mathematical programming. The aim of this Ebook is to present the latest developments in vector optimization. The contributions have been written by some of the most eminent researchers in this field of mathematical programming. The Ebook is considered essential for researchers and students in this field.

An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Optimization Problems

Directory of Open Access Journals (Sweden)

Shouheng Tuo

2013-01-01

Full Text Available Harmony search (HS algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL, is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

Optimal sizing and location of SVC devices for improvement of voltage profile in distribution network with dispersed photovoltaic and wind power plants

International Nuclear Information System (INIS)

Savić, Aleksandar; Đurišić, Željko

2014-01-01

Highlights: • Significant voltage variations in a distribution network with dispersed generation. • The use of SVC devices to improve the voltage profiles are an effective solution. • Number, size and location of SVC devices are optimized using genetic algorithm. • The methodology is presented on an example of a real distribution system in Serbia. - Abstract: Intermittent power generation of wind turbines and photovoltaic plants creates voltage disturbances in power distribution networks which may not be acceptable to the consumers. To control the deviations of the nodal voltages, it is necessary to use fast dynamic control of the reactive power in the distribution network. Implementation of the power electronic devices, such as Static Var Compensator (SVC), enables effective dynamic state as well as a static state of the nodal voltage control in the distribution network. This paper analyzed optimal sizing and location of SVC devices by using genetic algorithm, to improve nodal voltages profile in a distribution network with dispersed photovoltaic and wind power plants. Practical application of the developed methodology was tested on an example of a real distribution network

A dynamic global and local combined particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Chen Qunxian

2009-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.

Topology optimization of 3D shell structures with porous infill

DEFF Research Database (Denmark)

Clausen, Anders; Andreassen, Erik; Sigmund, Ole

2017-01-01

This paper presents a 3D topology optimization approach for designing shell structures with a porous or void interior. It is shown that the resulting structures are significantly more robust towards load perturbations than completely solid structures optimized under the same conditions. The study...... indicates that the potential benefit of using porous structures is higher for lower total volume fractions. Compared to earlier work dealing with 2D topology optimization, we found several new effects in 3D problems. Most notably, the opportunity for designing closed shells significantly improves...

Hierarchical Swarm Model: A New Approach to Optimization

Directory of Open Access Journals (Sweden)

Hanning Chen

2010-01-01

Full Text Available This paper presents a novel optimization model called hierarchical swarm optimization (HSO, which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O, based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.

Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

Science.gov (United States)

Shen, Chengcheng; Shi, Honghua; Liu, Yongzhi; Li, Fen; Ding, Dewen

2016-07-01

Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO), which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.

An Umeclidinium membrane sensor; Two-step optimization strategy for improved responses.

Science.gov (United States)

Yehia, Ali M; Monir, Hany H

2017-09-01

In the scientific context of membrane sensors and improved experimentation, we devised an experimentally designed protocol for sensor optimization. Two-step strategy was implemented for Umeclidinium bromide (UMEC) analysis which is a novel quinuclidine-based muscarinic antagonist used for maintenance treatment of symptoms accompanied with chronic obstructive pulmonary disease. In the first place, membrane components were screened for ideal ion exchanger, ionophore and plasticizer using three categorical factors at three levels in Taguchi design. Secondly, experimentally designed optimization was followed in order to tune the sensor up for finest responses. Twelve experiments were randomly carried out in a continuous factor design. Nernstian response, detection limit and selectivity were assigned as responses in these designs. The optimized membrane sensor contained tetrakis-[3,5-bis(trifluoro- methyl)phenyl] borate (0.44wt%) and calix[6]arene (0.43wt%) in 50.00% PVC plasticized with 49.13wt% 2-ni-tro-phenyl octylether. This sensor, along with an optimum concentration of inner filling solution (2×10 -4 molL -1 UMEC) and 2h of soaking time, attained the design objectives. Nernstian response approached 59.7mV/decade and detection limit decreased by about two order of magnitude (8×10 -8 mol L -1 ) through this optimization protocol. The proposed sensor was validated for UMEC determination in its linear range (3.16×10 -7 -1×10 -3 mol L -1 ) and challenged for selective discrimination of other congeners and inorganic cations. Results of INCRUSE ELLIPTA ® inhalation powder analyses obtained from the proposed sensor and manufacturer's UPLC were statistically compared. Moreover the proposed sensor was successfully used for the determination of UMEC in plasma samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

Science.gov (United States)

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung

2017-04-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Jing Li

2017-01-01

Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

[Optimized application of nested PCR method for detection of malaria].

Science.gov (United States)

Yao-Guang, Z; Li, J; Zhen-Yu, W; Li, C

2017-04-28

Objective To optimize the application of the nested PCR method for the detection of malaria according to the working practice, so as to improve the efficiency of malaria detection. Methods Premixing solution of PCR, internal primers for further amplification and new designed primers that aimed at two Plasmodium ovale subspecies were employed to optimize the reaction system, reaction condition and specific primers of P . ovale on basis of routine nested PCR. Then the specificity and the sensitivity of the optimized method were analyzed. The positive blood samples and examination samples of malaria were detected by the routine nested PCR and the optimized method simultaneously, and the detection results were compared and analyzed. Results The optimized method showed good specificity, and its sensitivity could reach the pg to fg level. The two methods were used to detect the same positive malarial blood samples simultaneously, the results indicated that the PCR products of the two methods had no significant difference, but the non-specific amplification reduced obviously and the detection rates of P . ovale subspecies improved, as well as the total specificity also increased through the use of the optimized method. The actual detection results of 111 cases of malarial blood samples showed that the sensitivity and specificity of the routine nested PCR were 94.57% and 86.96%, respectively, and those of the optimized method were both 93.48%, and there was no statistically significant difference between the two methods in the sensitivity ( P > 0.05), but there was a statistically significant difference between the two methods in the specificity ( P PCR can improve the specificity without reducing the sensitivity on the basis of the routine nested PCR, it also can save the cost and increase the efficiency of malaria detection as less experiment links.

Energetic optimization as a result of improvement for social comfort: international example

Energy Technology Data Exchange (ETDEWEB)

Melandri, D.

2007-07-01

Comfort of human beings is related to the availability of energetic sources. In these terms, connection between comfort and energy becomes synonymous of connection between comfort and productivity. Thus Energy Management can be considered as a tool to optimize different parameters, with clear results on global efficiency of all processes. A clear energetic analysis, besides standard evaluation, can bring to optimization of following parameters: Lay out, Human resources, Internal logistic processes, Business productivity. Through energetic cost, for example, it is possible to have reference indicators useful for the lay out and number of employees optimization in offices. An International study has been conducted in this sense, with important and surprising results. A design for a building in Australia shows that an excellent energetic efficiency brings to an annual saving of 10 ml dollars, as a result of the improved efficiency of the working area. Similar examples can be found in several parts of the world. For these reasons, in civil and tertiary sector, some important pollution causes must be considered during the design of energy efficiency systems: atmospheric pollution due to chemical agents and radiations, acoustic pollution, vibration pollution, microclimate states, lighting. All these elements are part of the study. (auth)

Optimization methods applied to hybrid vehicle design

Science.gov (United States)

Donoghue, J. F.; Burghart, J. H.

1983-01-01

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Contribution to the evaluation and to the improvement of multi-objective optimization methods: application to the optimization of nuclear fuel reloading pattern

International Nuclear Information System (INIS)

Collette, Y.

2002-01-01

In this thesis, we study the general problem of the selection of a multi-objective optimization method, then we study the improvement so as to efficiently solve a problem. The pertinent selection of a method presume the existence of a methodology: we have built tools to perform evaluation of performances and we propose an original method dedicated to the classification of know optimization methods. Our step has been applied to the elaboration of new methods for solving a very difficult problem: the nuclear core reload pattern optimization. First, we looked for a non usual approach of performances measurement: we have 'measured' the behavior of a method. To reach this goal, we have introduced several metrics. We have proposed to evaluate the 'aesthetic' of a distribution of solutions by defining two new metrics: a 'spacing metric' and a metric that allow us to measure the size of the biggest hole in the distribution of solutions. Then, we studied the convergence of multi-objective optimization methods by using some metrics defined in scientific literature and by proposing some more metrics: the 'Pareto ratio' which computes a ratio of solution production. Lastly, we have defined new metrics intended to better apprehend the behavior of optimization methods: the 'speed metric', which allows to compute the speed profile and a 'distribution metric' which allows to compute statistical distribution of solutions along the Pareto frontier. Next, we have studied transformations of a multi-objective problem and defined news methods: the modified Tchebychev method, or the penalized weighted sum of objective functions. We have elaborated new techniques to choose the initial point. These techniques allow to produce new initial points closer and closer to the Pareto frontier and, thanks to the 'proximal optimality concept', allowing dramatic improvements in the convergence of a multi-objective optimization method. Lastly, we have defined new vectorial multi-objective optimization

Improved identification of cranial nerves using paired-agent imaging: topical staining protocol optimization through experimentation and simulation

Science.gov (United States)

Torres, Veronica C.; Wilson, Todd; Staneviciute, Austeja; Byrne, Richard W.; Tichauer, Kenneth M.

2018-03-01

Skull base tumors are particularly difficult to visualize and access for surgeons because of the crowded environment and close proximity of vital structures, such as cranial nerves. As a result, accidental nerve damage is a significant concern and the likelihood of tumor recurrence is increased because of more conservative resections that attempt to avoid injuring these structures. In this study, a paired-agent imaging method with direct administration of fluorophores is applied to enhance cranial nerve identification. Here, a control imaging agent (ICG) accounts for non-specific uptake of the nerve-targeting agent (Oxazine 4), and ratiometric data analysis is employed to approximate binding potential (BP, a surrogate of targeted biomolecule concentration). For clinical relevance, animal experiments and simulations were conducted to identify parameters for an optimized stain and rinse protocol using the developed paired-agent method. Numerical methods were used to model the diffusive and kinetic behavior of the imaging agents in tissue, and simulation results revealed that there are various combinations of stain time and rinse number that provide improved contrast of cranial nerves, as suggested by optimal measures of BP and contrast-to-noise ratio.

Optimized ultra-high-pressure-assisted extraction of procyanidins from lychee pericarp improves the antioxidant activity of extracts.

Science.gov (United States)

Zhang, Ruifen; Su, Dongxiao; Hou, Fangli; Liu, Lei; Huang, Fei; Dong, Lihong; Deng, Yuanyuan; Zhang, Yan; Wei, Zhencheng; Zhang, Mingwei

2017-08-01

To establish optimal ultra-high-pressure (UHP)-assisted extraction conditions for procyanidins from lychee pericarp, a response surface analysis method with four factors and three levels was adopted. The optimum conditions were as follows: 295 MPa pressure, 13 min pressure holding time, 16.0 mL/g liquid-to-solid ratio, and 70% ethanol concentration. Compared with conventional ethanol extraction and ultrasonic-assisted extraction methods, the yields of the total procyanidins, flavonoids, and phenolics extracted using the UHP process were significantly increased; consequently, the oxygen radical absorbance capacity and cellular antioxidant activity of UHP-assisted lychee pericarp extracts were substantially enhanced. LC-MS/MS and high-performance liquid chromatography quantification results for individual phenolic compounds revealed that the yield of procyanidin compounds, including epicatechin, procyanidin A2, and procyanidin B2, from lychee pericarp could be significantly improved by the UHP-assisted extraction process. This UHP-assisted extraction process is thus a practical method for the extraction of procyanidins from lychee pericarp.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

Directory of Open Access Journals (Sweden)

Weixing Su

2017-03-01

Full Text Available There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

An improved superconducting neural circuit and its application for a neural network solving a combinatorial optimization problem

International Nuclear Information System (INIS)

Onomi, T; Nakajima, K

2014-01-01

We have proposed a superconducting Hopfield-type neural network for solving the N-Queens problem which is one of combinatorial optimization problems. The sigmoid-shape function of a neuron output is represented by the output of coupled SQUIDs gate consisting of a single-junction and a double-junction SQUIDs. One of the important factors for an improvement of the network performance is an improvement of a threshold characteristic of a neuron circuit. In this paper, we report an improved design of coupled SQUID gates for a superconducting neural network. A step-like function with a steep threshold at a rising edge is desirable for a neuron circuit to solve a combinatorial optimization problem. A neuron circuit is composed of two coupled SQUIDs gates with a cascade connection in order to obtain such characteristics. The designed neuron circuit is fabricated by a 2.5 kA/cm 2 Nb/AlOx/Nb process. The operation of a fabricated neuron circuit is experimentally demonstrated. Moreover, we discuss about the performance of the neural network using the improved neuron circuits and delayed negative self-connections.

Optimizing the dosing schedule of l-asparaginase improves its anti-tumor activity in breast tumor-bearing mice

Directory of Open Access Journals (Sweden)

Shoya Shiromizu

2018-04-01

Full Text Available Proliferation of acute lymphoblastic leukemic cells is nutritionally dependent on the external supply of asparagine. l-asparaginase, an enzyme hydrolyzing l-asparagine in blood, is used for treatment of acute lymphoblastic leukemic and other related blood cancers. Although previous studies demonstrated that l-asparaginase suppresses the proliferation of cultured solid tumor cells, it remains unclear whether this enzyme prevents the growth of solid tumors in vivo. In this study, we demonstrated the importance of optimizing dosing schedules for the anti-tumor activity of l-asparaginase in 4T1 breast tumor-bearing mice. Cultures of several types of murine solid tumor cells were dependent on the external supply of asparagine. Among them, we selected murine 4T1 breast cancer cells and implanted them into BALB/c female mice kept under standardized light/dark cycle conditions. The growth of 4T1 tumor cells implanted in mice was significantly suppressed by intravenous administration of l-asparaginase during the light phase, whereas its administration during the dark phase failed to show significant anti-tumor activity. Decreases in plasma asparagine levels due to the administration of l-asparaginase were closely related to the dosing time-dependency of its anti-tumor effects. These results suggest that the anti-tumor efficacy of l-asparaginase in breast tumor-bearing mice is improved by optimizing the dosing schedule. Keywords: l-asparaginase, Asparagine, Solid tumor, Chrono-pharmacotherapy

Optimizing the dosing schedule of l-asparaginase improves its anti-tumor activity in breast tumor-bearing mice.

Science.gov (United States)

Shiromizu, Shoya; Kusunose, Naoki; Matsunaga, Naoya; Koyanagi, Satoru; Ohdo, Shigehiro

2018-04-01

Proliferation of acute lymphoblastic leukemic cells is nutritionally dependent on the external supply of asparagine. l-asparaginase, an enzyme hydrolyzing l-asparagine in blood, is used for treatment of acute lymphoblastic leukemic and other related blood cancers. Although previous studies demonstrated that l-asparaginase suppresses the proliferation of cultured solid tumor cells, it remains unclear whether this enzyme prevents the growth of solid tumors in vivo. In this study, we demonstrated the importance of optimizing dosing schedules for the anti-tumor activity of l-asparaginase in 4T1 breast tumor-bearing mice. Cultures of several types of murine solid tumor cells were dependent on the external supply of asparagine. Among them, we selected murine 4T1 breast cancer cells and implanted them into BALB/c female mice kept under standardized light/dark cycle conditions. The growth of 4T1 tumor cells implanted in mice was significantly suppressed by intravenous administration of l-asparaginase during the light phase, whereas its administration during the dark phase failed to show significant anti-tumor activity. Decreases in plasma asparagine levels due to the administration of l-asparaginase were closely related to the dosing time-dependency of its anti-tumor effects. These results suggest that the anti-tumor efficacy of l-asparaginase in breast tumor-bearing mice is improved by optimizing the dosing schedule. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Spatially Explicit Estimation of Optimal Light Use Efficiency for Improved Satellite Data Driven Ecosystem Productivity Modeling

Science.gov (United States)

Madani, N.; Kimball, J. S.; Running, S. W.

2014-12-01

Remote sensing based light use efficiency (LUE) models, including the MODIS (MODerate resolution Imaging Spectroradiometer) MOD17 algorithm are commonly used for regional estimation and monitoring of vegetation gross primary production (GPP) and photosynthetic carbon (CO2) uptake. A common model assumption is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed biome maximum light use efficiency parameter defines the maximum photosynthetic carbon conversion rate under prevailing climate conditions and is a large source of model uncertainty. Here, we used tower (FLUXNET) eddy covariance measurement based carbon flux data for estimating optimal LUE (LUEopt) over a North American domain. LUEopt was first estimated using tower observed daily carbon fluxes, meteorology and satellite (MODIS) observed fraction of photosynthetically active radiation (FPAR). LUEopt was then spatially interpolated over the domain using empirical models derived from independent geospatial data including global plant traits, surface soil moisture, terrain aspect, land cover type and percent tree cover. The derived LUEopt maps were then used as primary inputs to the MOD17 LUE algorithm for regional GPP estimation; these results were evaluated against tower observations and alternate MOD17 GPP estimates determined using Biome-specific LUEopt constants. Estimated LUEopt shows large spatial variability within and among different land cover classes indicated from a sparse North American tower network. Leaf nitrogen content and soil moisture are two important factors explaining LUEopt spatial variability. GPP estimated from spatially explicit LUEopt inputs shows significantly improved model accuracy against independent tower observations (R2 = 0.76; Mean RMSE plant trait information can explain spatial heterogeneity in LUEopt, leading to improved GPP estimates from satellite based LUE models.

Optimization model of conventional missile maneuvering route based on improved Floyd algorithm

Science.gov (United States)

Wu, Runping; Liu, Weidong

2018-04-01

Missile combat plays a crucial role in the victory of war under high-tech conditions. According to the characteristics of maneuver tasks of conventional missile units in combat operations, the factors influencing road maneuvering are analyzed. Based on road distance, road conflicts, launching device speed, position requirements, launch device deployment, Concealment and so on. The shortest time optimization model was built to discuss the situation of road conflict and the strategy of conflict resolution. The results suggest that in the process of solving road conflict, the effect of node waiting is better than detour to another way. In this study, we analyzed the deficiency of the traditional Floyd algorithm which may limit the optimal way of solving road conflict, and put forward the improved Floyd algorithm, meanwhile, we designed the algorithm flow which would be better than traditional Floyd algorithm. Finally, throgh a numerical example, the model and the algorithm were proved to be reliable and effective.

Rehearsal significantly improves immediate and delayed recall on the Rey Auditory Verbal Learning Test.

Science.gov (United States)

Hessen, Erik

2011-10-01

A repeated observation during memory assessment with the Rey Auditory Verbal Learning Test (RAVLT) is that patients who spontaneously employ a memory rehearsal strategy by repeating the word list more than once achieve better scores than patients who only repeat the word list once. This observation led to concern about the ability of the standard test procedure of RAVLT and similar tests in eliciting the best possible recall scores. The purpose of the present study was to test the hypothesis that a rehearsal recall strategy of repeating the word list more than once would result in improved scores of recall on the RAVLT. We report on differences in outcome after standard administration and after experimental administration on Immediate and Delayed Recall measures from the RAVLT of 50 patients. The experimental administration resulted in significantly improved scores for all the variables employed. Additionally, it was found that patients who failed effort screening showed significantly poorer improvement on Delayed Recall compared with those who passed the effort screening. The general clear improvement both in raw scores and T-scores demonstrates that recall performance can be significantly influenced by the strategy of the patient or by small variations in instructions by the examiner.

Improvement of the cruise performances of a wing by means of aerodynamic optimization. Validation with a Far-Field method

Science.gov (United States)

Jiménez-Varona, J.; Ponsin Roca, J.

2015-06-01

Under a contract with AIRBUS MILITARY (AI-M), an exercise to analyze the potential of optimization techniques to improve the wing performances at cruise conditions has been carried out by using an in-house design code. The original wing was provided by AI-M and several constraints were posed for the redesign. To maximize the aerodynamic efficiency at cruise, optimizations were performed using the design techniques developed internally at INTA under a research program (Programa de Termofluidodinámica). The code is a gradient-based optimizaa tion code, which uses classical finite differences approach for gradient computations. Several techniques for search direction computation are implemented for unconstrained and constrained problems. Techniques for geometry modifications are based on different approaches which include perturbation functions for the thickness and/or mean line distributions and others by Bézier curves fitting of certain degree. It is very e important to afford a real design which involves several constraints that reduce significantly the feasible design space. And the assessment of the code is needed in order to check the capabilities and the possible drawbacks. Lessons learnt will help in the development of future enhancements. In addition, the validation of the results was done using also the well-known TAU flow solver and a far-field drag method in order to determine accurately the improvement in terms of drag counts.

Optimal Paths in Gliding Flight

Science.gov (United States)

Wolek, Artur

Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.

WE-AB-303-06: Combining DAO with MV + KV Optimization to Improve Skin Dose Sparing with Real-Time Fluoroscopy

Energy Technology Data Exchange (ETDEWEB)

Grelewicz, Z; Wiersma, R [The University of Chicago, Chicago, IL (United States)

2015-06-15

Purpose: Real-time fluoroscopy may allow for improved patient positioning and tumor tracking, particularly in the treatment of lung tumors. In order to mitigate the effects of the imaging dose, previous studies have demonstrated the effect of including both imaging dose and imaging constraints into the inverse treatment planning object function. That method of combined MV+kV optimization may Result in plans with treatment beams chosen to allow for more gentle imaging beam-on times. Direct-aperture optimization (DAO) is also known to produce treatment plans with fluence maps more conducive to lower beam-on times. Therefore, in this work we demonstrate the feasibility of a combination of DAO and MV+kV optimization for further optimized real-time kV imaging. Methods: Therapeutic and imaging beams were modeled in the EGSnrc Monte Carlo environment, and applied to a patient model for a previously treated lung patient to provide dose influence matrices from DOSXYZnrc. An MV + kV IMRT DAO treatment planning system was developed to compare DAO treatment plans with and without MV+kV optimization. The objective function was optimized using simulated annealing. In order to allow for comparisons between different cases of the stochastically optimized plans, the optimization was repeated twenty times. Results: Across twenty optimizations, combined MV+kV IMRT resulted in an average of 12.8% reduction in peak skin dose. Both non-optimized and MV+kV optimized imaging beams delivered, on average, mean dose of approximately 1 cGy per fraction to the target, with peak doses to target of approximately 6 cGy per fraction. Conclusion: When using DAO, MV+kV optimization is shown to Result in improvements to plan quality in terms of skin dose, when compared to the case of MV optimization with non-optimized kV imaging. The combination of DAO and MV+kV optimization may allow for real-time imaging without excessive imaging dose. Financial support for the work has been provided in part by NIH

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

Science.gov (United States)

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

2017-12-01

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

An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

Directory of Open Access Journals (Sweden)

Xuanlin Peng

2017-11-01

Full Text Available In this paper, a new methodology is proposed to reduce the vortex-induced vibration (VIV and improve the performance of the stay vane in a 200-MW Francis turbine. The process can be divided into two parts. Firstly, a diagnosis method for stay vane vibration based on field experiments and a finite element method (FEM is presented. It is found that the resonance between the Kármán vortex and the stay vane is the main cause for the undesired vibration. Then, we focus on establishing an intelligent optimization model of the stay vane’s trailing edge profile. To this end, an approach combining factorial experiments, extreme learning machine (ELM and particle swarm optimization (PSO is implemented. Three kinds of improved profiles of the stay vane are proposed and compared. Finally, the profile with a Donaldson trailing edge is adopted as the best solution for the stay vane, and verifications such as computational fluid dynamics (CFD simulations, structural analysis and fatigue analysis are performed to validate the optimized geometry.

Trading river services: optimizing dam decisions at the basin scale to improve socio-ecological resilience

Science.gov (United States)

Roy, S. G.; Gold, A.; Uchida, E.; McGreavy, B.; Smith, S. M.; Wilson, K.; Blachly, B.; Newcomb, A.; Hart, D.; Gardner, K.

2017-12-01

Dam removal has become a cornerstone of environmental restoration practice in the United States. One outcome of dam removal that has received positive attention is restored access to historic habitat for sea-run fisheries, providing a crucial gain in ecosystem resilience. But dams also provide stakeholders with valuable services, and uncertain socio-ecological outcomes can arise if there is not careful consideration of the basin scale trade offs caused by dam removal. In addition to fisheries, dam removals can significantly affect landscape nutrient flux, municipal water storage, recreational use of lakes and rivers, property values, hydroelectricity generation, the cultural meaning of dams, and many other river-based ecosystem services. We use a production possibility frontiers approach to explore dam decision scenarios and opportunities for trading between ecosystem services that are positively or negatively affected by dam removal in New England. Scenarios that provide efficient trade off potentials are identified using a multiobjective genetic algorithm. Our results suggest that for many river systems, there is a significant potential to increase the value of fisheries and other ecosystem services with minimal dam removals, and further increases are possible by including decisions related to dam operations and physical modifications. Run-of-river dams located near the head of tide are often found to be optimal for removal due to low hydroelectric capacity and high impact on fisheries. Conversely, dams with large impoundments near a river's headwaters can be less optimal for dam removal because their value as nitrogen sinks often outweighs the potential value for fisheries. Hydropower capacity is negatively impacted by dam removal but there are opportunities to meet or exceed lost capacity by upgrading preserved hydropower dams. Improving fish passage facilities for dams that are critical for safety or water storage can also reduce impacts on fisheries. Our

Efficiency Optimization in Class-D Audio Amplifiers

DEFF Research Database (Denmark)

Yamauchi, Akira; Knott, Arnold; Jørgensen, Ivan Harald Holger

2015-01-01

This paper presents a new power efficiency optimization routine for designing Class-D audio amplifiers. The proposed optimization procedure finds design parameters for the power stage and the output filter, and the optimum switching frequency such that the weighted power losses are minimized under...... the given constraints. The optimization routine is applied to minimize the power losses in a 130 W class-D audio amplifier based on consumer behavior investigations, where the amplifier operates at idle and low power levels most of the time. Experimental results demonstrate that the optimization method can...... lead to around 30 % of efficiency improvement at 1.3 W output power without significant effects on both audio performance and the efficiency at high power levels....

Welded joints integrity analysis and optimization for fiber laser welding of dissimilar materials

Science.gov (United States)

Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Liu, Wei

2016-11-01

Dissimilar materials welded joints provide many advantages in power, automotive, chemical, and spacecraft industries. The weld bead integrity which is determined by process parameters plays a significant role in the welding quality during the fiber laser welding (FLW) of dissimilar materials. In this paper, an optimization method by taking the integrity of the weld bead and weld area into consideration is proposed for FLW of dissimilar materials, the low carbon steel and stainless steel. The relationships between the weld bead integrity and process parameters are developed by the genetic algorithm optimized back propagation neural network (GA-BPNN). The particle swarm optimization (PSO) algorithm is taken for optimizing the predicted outputs from GA-BPNN for the objective. Through the optimization process, the desired weld bead with good integrity and minimum weld area are obtained and the corresponding microstructure and microhardness are excellent. The mechanical properties of the optimized joints are greatly improved compared with that of the un-optimized welded joints. Moreover, the effects of significant factors are analyzed based on the statistical approach and the laser power (LP) is identified as the most significant factor on the weld bead integrity and weld area. The results indicate that the proposed method is effective for improving the reliability and stability of welded joints in the practical production.

Improved Titanium Billet Inspection Sensitivity through Optimized Phased Array Design, Part II: Experimental Validation and Comparative Study with Multizone

International Nuclear Information System (INIS)

Hassan, W.; Vensel, F.; Knowles, B.; Lupien, V.

2006-01-01

The inspection of critical rotating components of aircraft engines has made important advances over the last decade. The development of Phased Array (PA) inspection capability for billet and forging materials used in the manufacturing of critical engine rotating components has been a priority for Honeywell Aerospace. The demonstration of improved PA inspection system sensitivity over what is currently used at the inspection houses is a critical step in the development of this technology and its introduction to the supply base as a production inspection. As described in Part I (in these proceedings), a new phased array transducer was designed and manufactured for optimal inspection of eight inch diameter Ti-6Al-4V billets. After confirming that the transducer was manufactured in accordance with the design specifications a validation study was conducted to assess the sensitivity improvement of the PAI over the current capability of Multi-zone (MZ) inspection. The results of this study confirm the significant (≅ 6 dB in FBH number sign sensitivity) improvement of the PAI sensitivity over that of MZI

Unified Health Gamification can significantly improve well-being in corporate environments.

Science.gov (United States)

Shahrestani, Arash; Van Gorp, Pieter; Le Blanc, Pascale; Greidanus, Fabrizio; de Groot, Kristel; Leermakers, Jelle

2017-07-01

There is a multitude of mHealth applications that aim to solve societal health problems by stimulating specific types of physical activities via gamification. However, physical health activities cover just one of the three World Health Organization (WHO) dimensions of health. This paper introduces the novel notion of Unified Health Gamification (UHG), which covers besides physical health also social and cognitive health and well-being. Instead of rewarding activities in the three WHO dimensions using different mHealth competitions, UHG combines the scores for such activities on unified leaderboards and lets people interact in social circles beyond personal interests. This approach is promising in corporate environments since UHG can connect the employees with intrinsic motivation for physical health with those who have quite different interests. In order to evaluate this approach, we realized an app prototype and we evaluated it in two corporate pilot studies. In total, eighteen pilot users participated voluntarily for six weeks. Half of the participants were recruited from an occupational health setting and the other half from a treatment setting. Our results suggest that the UHG principles are worth more investigation: various positive health effects were found based on a validated survey. The mean mental health improved significantly at one pilot location and at the level of individual pilot participants, multiple other effects were found to be significant: among others, significant mental health improvements were found for 28% of the participants. Most participants intended to use the app beyond the pilot, especially if it would be further developed.

Improved Atmospheric Correction Over the Indian Subcontinent Using Fast Radiative Transfer and Optimal Estimation

Science.gov (United States)

Natraj, V.; Thompson, D. R.; Mathur, A. K.; Babu, K. N.; Kindel, B. C.; Massie, S. T.; Green, R. O.; Bhattacharya, B. K.

2017-12-01

Remote Visible / ShortWave InfraRed (VSWIR) spectroscopy, typified by the Next-Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG), is a powerful tool to map the composition, health, and biodiversity of Earth's terrestrial and aquatic ecosystems. These studies must first estimate surface reflectance, removing the atmospheric effects of absorption and scattering by water vapor and aerosols. Since atmospheric state varies spatiotemporally, and is insufficiently constrained by climatological models, it is important to estimate it directly from the VSWIR data. However, water vapor and aerosol estimation is a significant ongoing challenge for existing atmospheric correction models. Conventional VSWIR atmospheric correction methods evolved from multi-band approaches and do not fully utilize the rich spectroscopic data available. We use spectrally resolved (line-by-line) radiative transfer calculations, coupled with optimal estimation theory, to demonstrate improved accuracy of surface retrievals. These spectroscopic techniques are already pervasive in atmospheric remote sounding disciplines but have not yet been applied to imaging spectroscopy. Our analysis employs a variety of scenes from the recent AVIRIS-NG India campaign, which spans various climes, elevation changes, a wide range of biomes and diverse aerosol scenarios. A key aspect of our approach is joint estimation of surface and aerosol parameters, which allows assessment of aerosol distortion effects using spectral shapes across the entire measured interval from 380-2500 nm. We expect that this method would outperform band ratio approaches, and enable evaluation of subtle aerosol parameters where in situ reference data is not available, or for extreme aerosol loadings, as is observed in the India scenarios. The results are validated using existing in-situ reference spectra, reflectance measurements from assigned partners in India, and objective spectral quality metrics for scenes without any

Planning Optimization of the Distributed Antenna System in High-Speed Railway Communication Network Based on Improved Cuckoo Search

Directory of Open Access Journals (Sweden)

Zhaoyu Chen

2018-01-01

Full Text Available The network planning is a key factor that directly affects the performance of the wireless networks. Distributed antenna system (DAS is an effective strategy for the network planning. This paper investigates the antenna deployment in a DAS for the high-speed railway communication networks and formulates an optimization problem which is NP-hard for achieving the optimal deployment of the antennas in the DAS. To solve this problem, a scheme based on an improved cuckoo search based on dimension cells (ICSDC algorithm is proposed. ICSDC introduces the dimension cell mechanism to avoid the internal dimension interferences in order to improve the performance of the algorithm. Simulation results show that the proposed ICSDC-based scheme obtains a lower network cost compared with the uniform network planning method. Moreover, ICSDC algorithm has better performance in terms of the convergence rate and accuracy compared with the conventional cuckoo search algorithm, the particle swarm optimization, and the firefly algorithm.

Equipment reliability process improvement and preventive maintenance optimization

International Nuclear Information System (INIS)

Darragi, M.; Georges, A.; Vaillancourt, R.; Komljenovic, D.; Croteau, M.

2004-01-01

The Gentilly-2 Nuclear Power Plant wants to optimize its preventive maintenance program through an Integrated Equipment Reliability Process. All equipment reliability related activities should be reviewed and optimized in a systematic approach especially for aging plants such as G2. This new approach has to be founded on best practices methods with the purpose of the rationalization of the preventive maintenance program and the performance monitoring of on-site systems, structures and components (SSC). A rational preventive maintenance strategy is based on optimized task scopes and frequencies depending on their applicability, critical effects on system safety and plant availability as well as cost-effectiveness. Preventive maintenance strategy efficiency is systematically monitored through degradation indicators. (author)

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

Science.gov (United States)

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

Directory of Open Access Journals (Sweden)

Chuncai Xiao

2014-12-01

Full Text Available This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM and improved particle swarm optimization (IPSO algorithm (SVM-IPSO. In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN, the basic particle swarm optimization (PSO method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

Frameworks for improvement: clinical audit, the plan-do-study-act cycle and significant event audit.

Science.gov (United States)

Gillam, Steve; Siriwardena, A Niroshan

2013-01-01

This is the first in a series of articles about quality improvement tools and techniques. We explore common frameworks for improvement, including the model for improvement and its application to clinical audit, plan-do-study-act (PDSA) cycles and significant event analysis (SEA), examining the similarities and differences between these and providing examples of each.

Luminosity Optimization Feedback in the SLC

International Nuclear Information System (INIS)

1999-01-01

The luminosity optimization at the SLC has been limited by the precision with which one can measure the micron size beams at the Interaction Point. Ten independent tuning parameters must be adjusted. An automated application has been used to scan each parameter over a significant range and set the minimum beam size as measured with a beam-beam deflection scan. Measurement errors limited the accuracy of this procedure and degraded the resulting luminosity. A new luminosity optimization feedback system has been developed using novel dithering techniques to maximize the luminosity with respect to the 10 parameters, which are adjusted one at a time. Control devices are perturbed around nominal setpoints, while the averaged readout of a digitized luminosity monitor measurement is accumulated for each setting. Results are averaged over many pulses to achieve high precision and then fitted to determine the optimal setting. The dithering itself causes a small loss in luminosity, but the improved optimization is expected to significantly enhance the performance of the SLC. Commissioning results are reported

Optimization of Materials and Interfaces for Spintronic Devices

Science.gov (United States)

Clark, Billy

In recent years' Spintronic devices have drawn a significant amount of research attention. This interest comes in large part from their ability to enable interesting and new technology such as Spin Torque Transfer Random Access Memory or improve existing technology such as High Signal Read Heads for Hard Disk Drives. For the former we worked on optimizing and improving magnetic tunnel junctions by optimizing their thermal stability by using Ta insertion layers in the free layer. We further tried to simplify the design of the MTJ stack by attempting to replace the Co/Pd multilayer with CoPd alloy. In this dissertation, we detail its development and examine the switching characteristics. Lastly we look at a highly spin polarized material, Fe2MnGe, for optimizing Hard Drive Disk read heads.

Increasing the statistical significance of entanglement detection in experiments

Energy Technology Data Exchange (ETDEWEB)

Jungnitsch, Bastian; Niekamp, Soenke; Kleinmann, Matthias; Guehne, Otfried [Institut fuer Quantenoptik und Quanteninformation, Innsbruck (Austria); Lu, He; Gao, Wei-Bo; Chen, Zeng-Bing [Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei (China); Chen, Yu-Ao; Pan, Jian-Wei [Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei (China); Physikalisches Institut, Universitaet Heidelberg (Germany)

2010-07-01

Entanglement is often verified by a violation of an inequality like a Bell inequality or an entanglement witness. Considerable effort has been devoted to the optimization of such inequalities in order to obtain a high violation. We demonstrate theoretically and experimentally that such an optimization does not necessarily lead to a better entanglement test, if the statistical error is taken into account. Theoretically, we show for different error models that reducing the violation of an inequality can improve the significance. We show this to be the case for an error model in which the variance of an observable is interpreted as its error and for the standard error model in photonic experiments. Specifically, we demonstrate that the Mermin inequality yields a Bell test which is statistically more significant than the Ardehali inequality in the case of a photonic four-qubit state that is close to a GHZ state. Experimentally, we observe this phenomenon in a four-photon experiment, testing the above inequalities for different levels of noise.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

Science.gov (United States)

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-05-04

Particle swarm optimization is a powerful metaheuristic population-based global optimization algorithm. However, when applied to non-separable objective functions its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant particle swarm optimization algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates a superior performance across several nonlinear, multimodal benchmark functions compared to the rotation-invariant Particle Swam Optimization (PSO) algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in ReaxFF-lg reactive force field is carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents a better performance compared to a Genetic Algorithm optimization method in the optimization of a ReaxFF-lg correction model parameters. The computational framework is implemented in a standalone C++ code that allows a straightforward development of ReaxFF reactive force fields.

Elastically Shaped Wing Optimization and Aircraft Concept for Improved Cruise Efficiency

Science.gov (United States)

Nguyen, Nhan; Trinh, Khanh; Reynolds, Kevin; Kless, James; Aftosmis, Michael; Urnes, James, Sr.; Ippolito, Corey

2013-01-01

This paper presents the findings of a study conducted tn 2010 by the NASA Innovation Fund Award project entitled "Elastically Shaped Future Air Vehicle Concept". The study presents three themes in support of meeting national and global aviation challenges of reducing fuel burn for present and future aviation systems. The first theme addresses the drag reduction goal through innovative vehicle configurations via non-planar wing optimization. Two wing candidate concepts have been identified from the wing optimization: a drooped wing shape and an inflected wing shape. The drooped wing shape is a truly biologically inspired wing concept that mimics a seagull wing and could achieve about 5% to 6% drag reduction, which is aerodynamically significant. From a practical perspective, this concept would require new radical changes to the current aircraft development capabilities for new vehicles with futuristic-looking wings such as this concept. The inflected wing concepts could achieve between 3% to 4% drag reduction. While the drag reduction benefit may be less, the inflected-wing concept could have a near-term impact since this concept could be developed within the current aircraft development capabilities. The second theme addresses the drag reduction goal through a new concept of elastic wing shaping control. By aeroelastically tailoring the wing shape with active control to maintain optimal aerodynamics, a significant drag reduction benefit could be realized. A significant reduction in fuel burn for long-range cruise from elastic wing shaping control could be realized. To realize the potential of the elastic wing shaping control concept, the third theme emerges that addresses the drag reduction goal through a new aerodynamic control effector called a variable camber continuous trailing edge flap. Conventional aerodynamic control surfaces are discrete independent surfaces that cause geometric discontinuities at the trailing edge region. These discontinuities promote

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

Directory of Open Access Journals (Sweden)

Ahmet Demir

2017-01-01

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

An optimized metal grid design to improve the solar cell performance under solar concentration using multiobjective computation

International Nuclear Information System (INIS)

Djeffal, F.; Bendib, T.; Arar, D.; Dibi, Z.

2013-01-01

Highlights: ► A new MOGA-based approach to design the solar cell metal grid is proposed. ► The cell parameters have been ascertained including the high illumination effects. ► An improved electrical behavior of the solar cell is found. ► The proposed optimized metal grid design is suitable for photovoltaic applications. -- Abstract: In this paper, a new multiobjective genetic algorithm (MOGA)-based approach is proposed to optimize the metal grid design in order to improve the electrical performance and the conversion efficiency behavior of the solar cells under high intensities of illumination. The proposed approach is applied to investigate the effect of two different metal grid patterns (one with 2 busbars outside the active area (linear grid) and another one with a circular busbar surrounding the active area (circular grid)) on the electrical performance of high efficiency c-Si solar cells under concentrated light (up to 150 suns). The dimensional and electrical parameters of the solar cell have been ascertained, and analytical expressions of the power losses and conversion efficiency, including high illumination effects, have been presented. The presented analytical models are used to formulate different objective functions, which are the prerequisite of the multiobjective optimization. The optimized design can also be incorporated into photovoltaic circuit simulator to study the impact of our approach on the photovoltaic circuit design

Optimizing the top profile of a nanowire for maximum forward emission

Institute of Scientific and Technical Information of China (English)

Wang Dong-Lin; Yu Zhong-Yuan; Liu Yu-Min; Guo Xiao-Tao; Cao Gui; Feng Hao

2011-01-01

The optimal top structure of a nanowire quantum emitter single photon source is significant in improving performance.Based on the axial symmetry of a cylindrical nanowire,this paper optimizes the top profile of a nanowire for the maximum forward emission by combining the geometry projection method and the finite element method.The results indicate that the nanowire with a cambered top has the stronger emission in the forward direction,which is helpful to improve the photon collection efficiency.

Optimization Design and Application of Underground Reinforced Concrete Bifurcation Pipe

Directory of Open Access Journals (Sweden)

Chao Su

2015-01-01

Full Text Available Underground reinforced concrete bifurcation pipe is an important part of conveyance structure. During construction, the workload of excavation and concrete pouring can be significantly decreased according to optimized pipe structure, and the engineering quality can be improved. This paper presents an optimization mathematical model of underground reinforced concrete bifurcation pipe structure according to real working status of several common pipe structures from real cases. Then, an optimization design system was developed based on Particle Swarm Optimization algorithm. Furthermore, take the bifurcation pipe of one hydropower station as an example: optimization analysis was conducted, and accuracy and stability of the optimization design system were verified successfully.

Determination of significance in Ecological Impact Assessment: Past change, current practice and future improvements

Energy Technology Data Exchange (ETDEWEB)

Briggs, Sam; Hudson, Malcolm D., E-mail: mdh@soton.ac.uk

2013-01-15

Ecological Impact Assessment (EcIA) is an important tool for conservation and achieving sustainable development. 'Significant' impacts are those which disturb or alter the environment to a measurable degree. Significance is a crucial part of EcIA, our understanding of the concept in practice is vital if it is to be effective as a tool. This study employed three methods to assess how the determination of significance has changed through time, what current practice is, and what would lead to future improvements. Three data streams were collected: interviews with expert stakeholders, a review of 30 Environmental Statements and a broad-scale survey of the United Kingdom Institute of Ecology and Environmental Management (IEEM) members. The approach taken in the determination of significance has become more standardised and subjectivity has become constrained through a transparent framework. This has largely been driven by a set of guidelines produced by IEEM in 2006. The significance of impacts is now more clearly justified and the accuracy with which it is determined has improved. However, there are limitations to accuracy and effectiveness of the determination of significance. These are the quality of baseline survey data, our scientific understanding of ecological processes and the lack of monitoring and feedback of results. These in turn are restricted by the limited resources available in consultancies. The most notable recommendations for future practice are the implementation of monitoring and the publication of feedback, the creation of a central database for baseline survey data and the streamlining of guidance. - Highlights: Black-Right-Pointing-Pointer The assessment of significance has changed markedly through time. Black-Right-Pointing-Pointer The IEEM guidelines have driven a standardisation of practice. Black-Right-Pointing-Pointer Currently limited by quality of baseline data and scientific understanding. Black-Right-Pointing-Pointer Monitoring

Pharmaceutical optimization of lipid-based dosage forms for the improvement of taste-masking, chemical stability and solubilizing capacity of phenobarbital.

Science.gov (United States)

Monteagudo, Ezequiel; Langenheim, Mariana; Salerno, Claudia; Buontempo, Fabián; Bregni, Carlos; Carlucci, Adriana

2014-06-01

Microemulsions (MEs) and self-emulsifying drug delivery systems (SEEDS) containing phenobarbital (Phe) were developed to improve its chemical stability, solubilizing capacity and taste-masking in oral liquid dosage forms. Cremophor® RH40 and Labrasol® were used as surfactants for the screening of ME regions, Capmul® MCM L, Captex® 355, Imwitor® 408, Myglyol® 840 and Isopropyl myristate were the oil phases assayed; Transcutol® P, Polyethylene-glycol 400, glycerol, Propylene-glycol and ethanol the cosurfactants. Phe stability assay was carried out (20:4:20:56% and 20:4:35:41% (w/w); surfactant:oily phase:cosurfactant:water) for both surfactants; only one containing ethanol showed significant dismissing in its drug content. Solubility capacity for these selected formulations were also evaluated, an amount between 17 and 58 mg/mL of Phe could be loaded. At last, an optimized ME formulation with Cremophor® RH40 20%, Capmul® MCM L 4%, PEG 400 35% and sucralose 2% (w/w) was chosen in order to optimize taste-masking using an electronic tongue. Strawberry along with banana and tutti-frutti flavors plus mint flavor proved to be the best ones. Labrasol-based pre-concentrates were tested for (micro)emulsifying properties; all of them resulted to behave as SEDDS. In summary, a rationale experimental design conducted to an optimized ME for Phe oral pediatric administration which was able to load 5-fold times the currently used dose (4 mg/mL), with no sign of physical or chemical instability and with improved taste; SEDDS for capsule filling were also obtained. The biopharmaceutical advantages described for these dosage forms encourage furthering in vivo evaluation.

Optimal river monitoring network using optimal partition analysis: a case study of Hun River, Northeast China.

Science.gov (United States)

Wang, Hui; Liu, Chunyue; Rong, Luge; Wang, Xiaoxu; Sun, Lina; Luo, Qing; Wu, Hao

2018-01-09

River monitoring networks play an important role in water environmental management and assessment, and it is critical to develop an appropriate method to optimize the monitoring network. In this study, an effective method was proposed based on the attainment rate of National Grade III water quality, optimal partition analysis and Euclidean distance, and Hun River was taken as a method validation case. There were 7 sampling sites in the monitoring network of the Hun River, and 17 monitoring items were analyzed once a month during January 2009 to December 2010. The results showed that the main monitoring items in the surface water of Hun River were ammonia nitrogen (NH 4 + -N), chemical oxygen demand, and biochemical oxygen demand. After optimization, the required number of monitoring sites was reduced from seven to three, and 57% of the cost was saved. In addition, there were no significant differences between non-optimized and optimized monitoring networks, and the optimized monitoring networks could correctly represent the original monitoring network. The duplicate setting degree of monitoring sites decreased after optimization, and the rationality of the monitoring network was improved. Therefore, the optimal method was identified as feasible, efficient, and economic.

An improved DPSO with mutation based on similarity algorithm for optimization of transmission lines loading

International Nuclear Information System (INIS)

Shayeghi, H.; Mahdavi, M.; Bagheri, A.

2010-01-01

Static transmission network expansion planning (STNEP) problem acquires a principal role in power system planning and should be evaluated carefully. Up till now, various methods have been presented to solve the STNEP problem. But only in one of them, lines adequacy rate has been considered at the end of planning horizon and the problem has been optimized by discrete particle swarm optimization (DPSO). DPSO is a new population-based intelligence algorithm and exhibits good performance on solution of the large-scale, discrete and non-linear optimization problems like STNEP. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In order to overcome these drawbacks and considering lines adequacy rate, in this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using an improved DPSO algorithm. The proposed improved DPSO is a new conception, collectivity, which is based on similarity between the particle and the current global best particle in the swarm that can prevent the premature convergence of DPSO around the local solution. The proposed method has been tested on the Garver's network and a real transmission network in Iran, and compared with the DPSO based method for solution of the TNEP problem. The results show that the proposed improved DPSO based method by preventing the premature convergence is caused that with almost the same expansion costs, the network adequacy is increased considerably. Also, regarding the convergence curves of both methods, it can be seen that precision of the proposed algorithm for the solution of the STNEP problem is more than DPSO approach.

Development and optimization of a self-microemulsifying drug delivery system for atorvastatin calcium by using D-optimal mixture design.

Science.gov (United States)

Yeom, Dong Woo; Song, Ye Seul; Kim, Sung Rae; Lee, Sang Gon; Kang, Min Hyung; Lee, Sangkil; Choi, Young Wook

2015-01-01

In this study, we developed and optimized a self-microemulsifying drug delivery system (SMEDDS) formulation for improving the dissolution and oral absorption of atorvastatin calcium (ATV), a poorly water-soluble drug. Solubility and emulsification tests were performed to select a suitable combination of oil, surfactant, and cosurfactant. A D-optimal mixture design was used to optimize the concentration of components used in the SMEDDS formulation for achieving excellent physicochemical characteristics, such as small droplet size and high dissolution. The optimized ATV-loaded SMEDDS formulation containing 7.16% Capmul MCM (oil), 48.25% Tween 20 (surfactant), and 44.59% Tetraglycol (cosurfactant) significantly enhanced the dissolution rate of ATV in different types of medium, including simulated intestinal fluid, simulated gastric fluid, and distilled water, compared with ATV suspension. Good agreement was observed between predicted and experimental values for mean droplet size and percentage of the drug released in 15 minutes. Further, pharmacokinetic studies in rats showed that the optimized SMEDDS formulation considerably enhanced the oral absorption of ATV, with 3.4-fold and 4.3-fold increases in the area under the concentration-time curve and time taken to reach peak plasma concentration, respectively, when compared with the ATV suspension. Thus, we successfully developed an optimized ATV-loaded SMEDDS formulation by using the D-optimal mixture design, that could potentially be used for improving the oral absorption of poorly water-soluble drugs.

Optimal Energy Control Strategy Design for a Hybrid Electric Vehicle

Directory of Open Access Journals (Sweden)

Yuan Zou

2013-01-01

Full Text Available A heavy-duty parallel hybrid electric truck is modeled, and its optimal energy control is studied in this paper. The fundamental architecture of the parallel hybrid electric truck is modeled feed-forwardly, together with necessary dynamic features of subsystem or components. Dynamic programming (DP technique is adopted to find the optimal control strategy including the gear-shifting sequence and the power split between the engine and the motor subject to a battery SOC-sustaining constraint. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement on the fuel economy can be achieved in the heavy-duty vehicle cycle from the natural driving statistics.

Improving bending stress in spur gears using asymmetric gears and shape optimization

DEFF Research Database (Denmark)

Pedersen, Niels Leergaard

2010-01-01

Bending stress plays a significant role in gear design wherein its magnitude is controlled by the nominal bending stress and the stress concentration due to the geometrical shape. The bending stress is indirectly related to shape changes made to the cutting tool. This work shows that the bending...... stress can be reduced significantly by using asymmetric gear teeth and by shape optimizing the gear through changes made to the tool geometry. However, to obtain the largest possible stress reduction a custom tool must be designed depending on the number of teeth, but the stress reductions found...

Performance of an improved logarithmic phase mask with optimized parameters in a wavefront-coding system.

Science.gov (United States)

Zhao, Hui; Li, Yingcai

2010-01-10

In two papers [Proc. SPIE 4471, 272-280 (2001) and Appl. Opt. 43, 2709-2721 (2004)], a logarithmic phase mask was proposed and proved to be effective in extending the depth of field; however, according to our research, this mask is not that perfect because the corresponding defocused modulation transfer function has large oscillations in the low-frequency region, even when the mask is optimized. So, in a previously published paper [Opt. Lett. 33, 1171-1173 (2008)], we proposed an improved logarithmic phase mask by making a small modification. The new mask can not only eliminate the drawbacks to a certain extent but can also be even less sensitive to focus errors according to Fisher information criteria. However, the performance comparison was carried out with the modified mask not being optimized, which was not reasonable. In this manuscript, we optimize the modified logarithmic phase mask first before analyzing its performance and more convincing results have been obtained based on the analysis of several frequently used metrics.

Digital marketing in travel industry. Case: Hotel landing page optimization

OpenAIRE

Bitkulova, Renata

2017-01-01

Landing page optimization is implementation of the principles of digital service design to improve the website’s user experience. Well done landing page optimization can have a significant positive effect on the usability and profitability of the website. The objective of the study was to optimize the Russian language version of Vuokatti landing page in order to increase conversion, defined as the number of clicks to accommodation search button. A literature survey was made to determine ...

Improving processes through evolutionary optimization.

Science.gov (United States)

Clancy, Thomas R

2011-09-01

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies on complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 18th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, I discuss methods to optimize complex healthcare processes through learning, adaptation, and evolutionary planning.

Optimizing strategies to improve interprofessional practice for veterans, part 1

Directory of Open Access Journals (Sweden)

Bhattacharya SB

2014-04-01

Full Text Available Shelley B Bhattacharya,1–3 Michelle I Rossi,1,2 Jennifer M Mentz11Geriatric Research Education and Clinical Center (GRECC, Veteran's Affairs Pittsburgh Healthcare System, 2University of Pittsburgh Medical Center, Pittsburgh, PA, USA; 3Albert Schweitzer Fellowship Program, Pittsburgh, PA, USAIntroduction: Interprofessional patient care is a well-recognized path that health care systems are striving toward. The Veteran's Affairs (VA system initiated interprofessional practice (IPP models with their Geriatric Evaluation and Management (GEM programs. GEM programs incorporate a range of specialties, including but not limited to, medicine, nursing, social work, physical therapy and pharmacy, to collaboratively evaluate veterans. Despite being a valuable resource, they are now faced with significant cut-backs, including closures. The primary goal of this project was to assess how the GEM model could be optimized at the Pittsburgh, Pennsylvania VA to allow for the sustainability of this important IPP assessment. Part 1 of the study evaluated the IPP process using program, patient, and family surveys. Part 2 examined how well the geriatrician matched patients to specialists in the GEM model. This paper describes Part 1 of our study.Methods: Three strategies were used: 1 a national GEM program survey; 2 a veteran/family satisfaction survey; and 3 an absentee assessment.Results: Twenty-six of 92 programs responded to the GEM IPP survey. Six strategies were shared to optimize IPP models throughout the country. Of the 34 satisfaction surveys, 80% stated the GEM clinic was beneficial, 79% stated their concerns were addressed, and 100% would recommend GEM to their friends. Of the 24 absentee assessments, the top three reasons for missing the appointments were transportation, medical illnesses, and not knowing/remembering about the appointment. Absentee rate diminished from 41% to 19% after instituting a reminder phone call policy.Discussion: Maintaining the

CFD optimization of a pellet burner

Directory of Open Access Journals (Sweden)

Westerlund Lars B.

2012-01-01

Full Text Available Increased capacity of computers has made CFD technology attractive for the design of different apparatuses. Optimization of a pellet burner using CFD was investigated in this paper. To make the design tool work fast, an approach with only mixing of gases was simulated. Other important phenomena such as chemical reactions were omitted in order to speed up the design process. The original design of the burner gave unsatisfactory performance. The optimized design achieved from simulation was validated and the results show a significant improvement. The power output increased and the emission of unburned species decreased but could be further reduced. The contact time between combustion gases and secondary air was probably too short. An increased contact time in high temperature conditions would possibly improve the design further.

Carotid endarterectomy significantly improves postoperative laryngeal sensitivity.

Science.gov (United States)

Hammer, Georg Philipp; Tomazic, Peter Valentin; Vasicek, Sarah; Graupp, Matthias; Gugatschka, Markus; Baumann, Anneliese; Konstantiniuk, Peter; Koter, Stephan Herwig

2016-11-01

sensory threshold on the operated-on side (6.08 ± 2.02 mm Hg) decreased significantly at the 6-week follow-up, even in relation to the preoperative measure (P = .022). With the exception of one patient with permanent unilateral vocal fold immobility, no signs of nerve injury were detected. In accordance with previous reports, injuries to the recurrent laryngeal nerve during CEA seem to be rare. In most patients, postoperative symptoms (globus, dysphagia, dysphonia) and signs fade within a few weeks without any specific therapeutic intervention. This study shows an improved long-term postoperative superior laryngeal nerve function with regard to laryngopharyngeal sensitivity. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

Science.gov (United States)

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Optimal Face-Iris Multimodal Fusion Scheme

Directory of Open Access Journals (Sweden)

Omid Sharifi

2016-06-01

Full Text Available Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA, a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

Institute of Scientific and Technical Information of China (English)

Yuhong Wang; Qin Liu; Jianrong Tang; Wenbin Cao; Xiaozhong Li

2014-01-01

A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM(1,1) model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM(1,1) model relatively.

HVAC system optimization - in-building section

Energy Technology Data Exchange (ETDEWEB)

Lu Lu; Wenjian Cai; Lihua Xie; Shujiang Li; Yeng Chai Soh [Nanyang Technological Univ., Singapore (Singapore). School of Electrical and Electronic Engineering

2005-01-01

This paper presents a practical method to optimize in-building section of centralized Heating, Ventilation and Air-conditioning (HVAC) systems which consist of indoor air loops and chilled water loops. First, through component characteristic analysis, mathematical models associated with cooling loads and energy consumption for heat exchangers and energy consuming devices are established. By considering variation of cooling load of each end user, adaptive neuro-fuzzy inference system (ANFIS) is employed to model duct and pipe networks and obtain optimal differential pressure (DP) set points based on limited sensor information. A mix-integer nonlinear constraint optimization of system energy is formulated and solved by a modified genetic algorithm. The main feature of our paper is a systematic approach in optimizing the overall system energy consumption rather than that of individual component. A simulation study for a typical centralized HVAC system is provided to compare the proposed optimization method with traditional ones. The results show that the proposed method indeed improves the system performance significantly. (author)

Combining kernel matrix optimization and regularization to improve particle size distribution retrieval

Science.gov (United States)

Ma, Qian; Xia, Houping; Xu, Qiang; Zhao, Lei

2018-05-01

A new method combining Tikhonov regularization and kernel matrix optimization by multi-wavelength incidence is proposed for retrieving particle size distribution (PSD) in an independent model with improved accuracy and stability. In comparison to individual regularization or multi-wavelength least squares, the proposed method exhibited better anti-noise capability, higher accuracy and stability. While standard regularization typically makes use of the unit matrix, it is not universal for different PSDs, particularly for Junge distributions. Thus, a suitable regularization matrix was chosen by numerical simulation, with the second-order differential matrix found to be appropriate for most PSD types.

Parameter definition using vibration prediction software leads to significant drilling performance improvements

Energy Technology Data Exchange (ETDEWEB)

Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

2012-07-01

field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

Optimizing available water capacity using microwave satellite data for improving irrigation management

Science.gov (United States)

Gupta, Manika; Bolten, John; Lakshmi, Venkat

2015-04-01

This work addresses the improvement of available water capacity by developing a technique for estimating soil hydraulic parameters through the utilization of satellite-retrieved near surface soil moisture. The prototype involves the usage of Monte Carlo analysis to assimilate historical remote sensing soil moisture data available from the Advanced Microwave Scanning Radiometer (AMSR-E) within the hydrological model. The main hypothesis used in this study is that near-surface soil moisture data contain useful information that can describe the effective hydrological conditions of the basin such that when appropriately In the method followed in this study the hydraulic parameters are derived directly from information on the soil moisture state at the AMSR-E footprint scale and the available water capacity is derived for the root zone by coupling of AMSR-E soil moisture with the physically-based hydrological model. The available capacity water, which refers to difference between the field capacity and wilting point of the soil and represent the soil moisture content at 0.33 bar and 15 bar respectively is estimated from the soil hydraulic parameters using the van Genuchten equation. The initial ranges of soil hydraulic parameters are taken in correspondence with the values available from the literature based on Soil Survey Geographic (SSURGO) database within the particular AMSR-E footprint. Using the Monte Carlo simulation, the ranges are narrowed in the region where simulation shows a good match between predicted and near-surface soil moisture from AMSR-E. In this study, the uncertainties in accurately determining the parameters of the nonlinear soil water retention function for large-scale hydrological modeling is the focus of the development of the Bayesian framework. Thus, the model forecasting has been combined with the observational information to optimize the model state and the soil hydraulic parameters simultaneously. The optimization process is divided into

A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

Science.gov (United States)

Hernandez, Wilmar

2007-01-01

In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today's cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher's interest in the fusion of intelligent sensors and optimal signal processing techniques.

Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis

International Nuclear Information System (INIS)

Han, Yong-Ming; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-01-01

Graphical abstract: This paper proposed an energy optimization and prediction of complex petrochemical industries based on a DEA-integrated ANN approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA-ANN prediction model is effectively verified by executing a linear comparison between all DMUs and the effective DMUs through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex ethylene production system of China petrochemical industry. Meanwhile, the optimization result and the prediction value are obtained to reduce energy consumption of the ethylene production system, guide ethylene production and improve energy efficiency. - Highlights: • The DEA-integrated ANN approach is proposed. • The DEA-ANN prediction model is effectively verified through the standard data source from the UCI repository. • The energy optimization and prediction framework of complex petrochemical industries based on the proposed method is obtained. • The proposed method is valid and efficient in improvement of energy efficiency in complex petrochemical plants. - Abstract: Since the complex petrochemical data have characteristics of multi-dimension, uncertainty and noise, it is difficult to accurately optimize and predict the energy usage of complex petrochemical systems. Therefore, this paper proposes a data envelopment analysis (DEA) integrated artificial neural network (ANN) approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA

A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases

Directory of Open Access Journals (Sweden)

Zhiyong ZHANG

2014-03-01

Full Text Available The neural networks have significance on recognition of crops disease diagnosis? but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in the image recognition. At last, an example of the emulation shows that neural network model based on recognizes significantly better than without optimization. Model accuracy has been improved to a certain extent to meet the actual needs of maize leaf diseases recognition.

Shape-optimization of round-to-slot holes for improving film cooling effectiveness on a flat surface

Science.gov (United States)

Huang, Ying; Zhang, Jing-zhou; Wang, Chun-hua

2018-06-01

Single-objective optimization for improving adiabatic film cooling effectiveness is performed for single row of round-to-slot film cooling holes on a flat surface by using CFD analysis and surrogate approximation methods. Among the main geometric parameters, dimensionless hole-to-hole pitch ( P/ d) and slot length-to-diameter ( l/ d) are fixed as 2.4 and 2 respectively, and the other parameters (hole height-to-diameter ratio, slot width-to-diameter and inclination angle) are chosen as the design variables. Given a wide range of possible geometric variables, the geometric optimization of round-to-slot holes is carried out under two typical blowing ratios of M = 0.5 and M = 1.5 by selecting a spatially-averaged adiabatic film cooling effectiveness between x/ d = 2 and x/ d = 12 as the objective function to be maximized. Radial basis function neural network is applied for constructing the surrogate model and then the optimal design point is searched by a genetic algorithm. It is revealed that the optimal round-to-slot hole is of converging feature under a low blowing ratio but of diffusing feature under a high blowing ratio. Further, the influence principle of optimal round-to-slot geometry on film cooling performance is illustrated according to the detailed flow and thermal behaviors.

Economic Load Dispatch - A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO

DEFF Research Database (Denmark)

Vlachogiannis, Ioannis (John); Lee, KY

2009-01-01

In this paper an improved coordinated aggregation-based particle swarm optimization (ICA-PSO) algorithm is introduced for solving the optimal economic load dispatch (ELD) problem in power systems. In the ICA-PSO algorithm each particle in the swarm retains a memory of its best position ever...... encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly. Moreover, the population size is increased adaptively, the number of search intervals for the particles is selected adaptively...

Topology optimization of vibration and wave propagation problems

DEFF Research Database (Denmark)

Jensen, Jakob Søndergaard

2007-01-01

The method of topology optimization is a versatile method to determine optimal material layouts in mechanical structures. The method relies on, in principle, unlimited design freedom that can be used to design materials, structures and devices with significantly improved performance and sometimes...... novel functionality. This paper addresses basic issues in simulation and topology design of vibration and wave propagation problems. Steady-state and transient wave propagation problems are addressed and application examples for both cases are presented....

Improving Topology Optimization using Games

DEFF Research Database (Denmark)

Nobel-Jørgensen, Morten; Christiansen, Asger Nyman; Bærentzen, J. Andreas

free of charge on iOS and Android devices1. The TopOptGame is inspired by puzzle-games (a genre of computer games), which constantly challenges the players and gives rewards when progress is made. This engagement loop will take the player on a journey starting with simple problems with few supports......, this will allow us to analyze the data to measure human performance of topology optimization and more importantly, in which cases people's intuition succeed or fail. The game is currently a working prototype and is scheduled for final release on both iOS and Android before WCSMO-10....

A method of segment weight optimization for intensity modulated radiation therapy

International Nuclear Information System (INIS)

Pei Xi; Cao Ruifen; Jing Jia; Cheng Mengyun; Zheng Huaqing; Li Jia; Huang Shanqing; Li Gui; Song Gang; Wang Weihua; Wu Yican; FDS Team

2011-01-01

The error caused by leaf sequencing often leads to planning of Intensity-Modulated Radiation Therapy (IMRT) arrange system couldn't meet clinical demand. The optimization approach in this paper can reduce this error and improve efficiency of plan-making effectively. Conjugate Gradient algorithm was used to optimize segment weight and readjust segment shape, which could minimize the error anterior-posterior leaf sequencing eventually. Frequent clinical cases were tasted by precise radiotherapy system, and then compared Dose-Volume histogram between target area and organ at risk as well as isodose line in computed tomography (CT) film, we found that the effect was improved significantly after optimizing segment weight. Segment weight optimizing approach based on Conjugate Gradient method can make treatment planning meet clinical request more efficiently, so that has extensive application perspective. (authors)

On Optimizing H. 264/AVC Rate Control by Improving R-D Model and Incorporating HVS Characteristics

Directory of Open Access Journals (Sweden)

Jiang Gangyi

2010-01-01

Full Text Available The state-of-the-art JVT-G012 rate control algorithm of H.264 is improved from two aspects. First, the quadratic rate-distortion (R-D model is modified based on both empirical observations and theoretical analysis. Second, based on the existing physiological and psychological research findings of human vision, the rate control algorithm is optimized by incorporating the main characteristics of the human visual system (HVS such as contrast sensitivity, multichannel theory, and masking effect. Experiments are conducted, and experimental results show that the improved algorithm can simultaneously enhance the overall subjective visual quality and improve the rate control precision effectively.

Improving the ensemble-optimization method through covariance-matrix adaptation

NARCIS (Netherlands)

Fonseca, R.M.; Leeuwenburgh, O.; Hof, P.M.J. van den; Jansen, J.D.

2015-01-01

Ensemble optimization (referred to throughout the remainder of the paper as EnOpt) is a rapidly emerging method for reservoirmodel-based production optimization. EnOpt uses an ensemble of controls to approximate the gradient of the objective function with respect to the controls. Current

Technical note: optimization for improved tube-loading efficiency in the dual-energy computed tomography coupled with balanced filter method.

Science.gov (United States)

Saito, Masatoshi

2010-08-01

This article describes the spectral optimization of dual-energy computed tomography using balanced filters (bf-DECT) to reduce the tube loadings and dose by dedicating to the acquisition of electron density information, which is essential for treatment planning in radiotherapy. For the spectral optimization of bf-DECT, the author calculated the beam-hardening error and air kerma required to achieve a desired noise level in an electron density image of a 50-cm-diameter cylindrical water phantom. The calculation enables the selection of beam parameters such as tube voltage, balanced filter material, and its thickness. The optimal combination of tube voltages was 80 kV/140 kV in conjunction with Tb/Hf and Bi/Mo filter pairs; this combination agrees with that obtained in a previous study [M. Saito, "Spectral optimization for measuring electron density by the dual-energy computed tomography coupled with balanced filter method," Med. Phys. 36, 3631-3642 (2009)], although the thicknesses of the filters that yielded a minimum tube output were slightly different from those obtained in the previous study. The resultant tube loading of a low-energy scan of the present bf-DECT significantly decreased from 57.5 to 4.5 times that of a high-energy scan for conventional DECT. Furthermore, the air kerma of bf-DECT could be reduced to less than that of conventional DECT, while obtaining the same figure of merit for the measurement of electron density and effective atomic number. The tube-loading and dose efficiencies of bf-DECT were considerably improved by sacrificing the quality of the noise level in the images of effective atomic number.

Optimization of safety equipment outages improves safety

International Nuclear Information System (INIS)

Cepin, Marko

2002-01-01

Testing and maintenance activities of safety equipment in nuclear power plants are an important potential for risk and cost reduction. An optimization method is presented based on the simulated annealing algorithm. The method determines the optimal schedule of safety equipment outages due to testing and maintenance based on minimization of selected risk measure. The mean value of the selected time dependent risk measure represents the objective function of the optimization. The time dependent function of the selected risk measure is obtained from probabilistic safety assessment, i.e. the fault tree analysis at the system level and the fault tree/event tree analysis at the plant level, both extended with inclusion of time requirements. Results of several examples showed that it is possible to reduce risk by application of the proposed method. Because of large uncertainties in the probabilistic safety assessment, the most important result of the method may not be a selection of the most suitable schedule of safety equipment outages among those, which results in similarly low risk. But, it may be a prevention of such schedules of safety equipment outages, which result in high risk. Such finding increases the importance of evaluation speed versus the requirement of getting always the global optimum no matter if it is only slightly better that certain local one

CREATION OF OPTIMIZATION MODEL OF STEAM BOILER RECUPERATIVE AIR HEATER

Directory of Open Access Journals (Sweden)

N. B. Carnickiy

2006-01-01

Full Text Available The paper proposes to use a mathematical modeling as one of the ways intended to improve quality of recuperative air heater design (RAH without significant additional costs, connected with the change of design materials or fuel type. The described conceptual mathematical AHP optimization model of RAH consists of optimized and constant parameters, technical limitations and optimality criteria.The paper considers a methodology for search of design and regime parameters of an air heater which is based on the methods of multi-criteria optimization. Conclusions for expediency of the given approach usage are made in the paper.

Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint

Energy Technology Data Exchange (ETDEWEB)

Cui, Mingjian; Zhang, Jie; Florita, Anthony R.; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

2015-08-06

Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.

Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling

Science.gov (United States)

Reniers, Jorn M.; Mulder, Grietus; Ober-Blöbaum, Sina; Howey, David A.

2018-03-01

The increased deployment of intermittent renewable energy generators opens up opportunities for grid-connected energy storage. Batteries offer significant flexibility but are relatively expensive at present. Battery lifetime is a key factor in the business case, and it depends on usage, but most techno-economic analyses do not account for this. For the first time, this paper quantifies the annual benefits of grid-connected batteries including realistic physical dynamics and nonlinear electrochemical degradation. Three lithium-ion battery models of increasing realism are formulated, and the predicted degradation of each is compared with a large-scale experimental degradation data set (Mat4Bat). A respective improvement in RMS capacity prediction error from 11% to 5% is found by increasing the model accuracy. The three models are then used within an optimal control algorithm to perform price arbitrage over one year, including degradation. Results show that the revenue can be increased substantially while degradation can be reduced by using more realistic models. The estimated best case profit using a sophisticated model is a 175% improvement compared with the simplest model. This illustrates that using a simplistic battery model in a techno-economic assessment of grid-connected batteries might substantially underestimate the business case and lead to erroneous conclusions.

Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

Science.gov (United States)

Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

2016-01-01

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

Improving management of patients with autism spectrum disorder having scheduled surgery: optimizing practice.

Science.gov (United States)

Thompson, Debbie Gearner; Tielsch-Goddard, Anna

2014-01-01

Surgical preparation for children with autism spectrum disorders can be a challenge to perioperative staff because of the unique individual needs and behaviors in this population. Most children with autism function best in predictable, routine environments, and being in the hospital and other health care settings can create a stressful situation. This prospective, descriptive, quality improvement project was conducted to optimize best practices for perioperative staff and better individualize the plan of care for the autistic child and his or her family. Forty-three patients with a diagnosis of autism or autistic spectrum disorder were seen over 6 months at a suburban pediatric hospital affiliated with a major urban pediatric hospital and had an upcoming scheduled surgery or procedure requiring anesthesia. Caregivers were interviewed before and after surgery to collect information to better help their child cope with their hospital visit. In an evaluation of project outcomes, data were tabulated and summarized and interview data were qualitatively coded for emerging themes to improve the perioperative process for the child. Findings showed that staff members were able to recognize potential and actual stressors and help identify individual needs of surgical patients with autism. The families were pleased and appreciative of the individual attention and focus on their child's special needs. Investigators also found increased staff interest in optimizing the surgical experience for autistic children. Copyright © 2014 National Association of Pediatric Nurse Practitioners. Published by Mosby, Inc. All rights reserved.

Neuro-Linguistic Programming: Improving Rapport between Track/Cross Country Coaches and Significant Others

Science.gov (United States)

Helm, David Jay

2017-01-01

This study examines the background information and the components of N.L.P., being eye movements, use of predicates, and posturing, as they apply to improving rapport and empathy between track/cross country coaches and their significant others in the arena of competition to help alleviate the inherent stressors.

The influence of optimism on functionality after total hip replacement surgery.

Science.gov (United States)

Balck, Friedrich; Lippmann, Maike; Jeszenszky, Csilla; Günther, Klaus-Peter; Kirschner, Stephan

2016-08-01

Among other factors, optimism has been shown to significantly influence the course of some diseases (cancer, HIV, coronary heart disease). This study investigated whether optimism of a patient before a total hip replacement can predict the functionality of the lower limbs 3 and 6 months after surgery. A total of 325 patients took part in the study (age: 58.7 years; w: 55%). The functionality was measured with the Western Ontario and McMaster Universities arthrosis index, and optimism with the Life Orientation Test. To analyse the influences of age, gender and optimism, general linear models were calculated. In optimistic patients, functionality improved significantly over time. The study showed a clear influence of dispositional optimism on the recovery after total hip replacement in the first 3 months after surgery. © The Author(s) 2015.

Application of Fuzzy Sets for the Improvement of Routing Optimization Heuristic Algorithms

Directory of Open Access Journals (Sweden)

Mattas Konstantinos

2016-12-01

Full Text Available The determination of the optimal circular path has become widely known for its difficulty in producing a solution and for the numerous applications in the scope of organization and management of passenger and freight transport. It is a mathematical combinatorial optimization problem for which several deterministic and heuristic models have been developed in recent years, applicable to route organization issues, passenger and freight transport, storage and distribution of goods, waste collection, supply and control of terminals, as well as human resource management. Scope of the present paper is the development, with the use of fuzzy sets, of a practical, comprehensible and speedy heuristic algorithm for the improvement of the ability of the classical deterministic algorithms to identify optimum, symmetrical or non-symmetrical, circular route. The proposed fuzzy heuristic algorithm is compared to the corresponding deterministic ones, with regard to the deviation of the proposed solution from the best known solution and the complexity of the calculations needed to obtain this solution. It is shown that the use of fuzzy sets reduced up to 35% the deviation of the solution identified by the classical deterministic algorithms from the best known solution.

Active surface model improvement by energy function optimization for 3D segmentation.

Science.gov (United States)

Azimifar, Zohreh; Mohaddesi, Mahsa

2015-04-01

This paper proposes an optimized and efficient active surface model by improving the energy functions, searching method, neighborhood definition and resampling criterion. Extracting an accurate surface of the desired object from a number of 3D images using active surface and deformable models plays an important role in computer vision especially medical image processing. Different powerful segmentation algorithms have been suggested to address the limitations associated with the model initialization, poor convergence to surface concavities and slow convergence rate. This paper proposes a method to improve one of the strongest and recent segmentation algorithms, namely the Decoupled Active Surface (DAS) method. We consider a gradient of wavelet edge extracted image and local phase coherence as external energy to extract more information from images and we use curvature integral as internal energy to focus on high curvature region extraction. Similarly, we use resampling of points and a line search for point selection to improve the accuracy of the algorithm. We further employ an estimation of the desired object as an initialization for the active surface model. A number of tests and experiments have been done and the results show the improvements with regards to the extracted surface accuracy and computational time of the presented algorithm compared with the best and recent active surface models. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improvement of ethanol production from crystalline cellulose via optimizing cellulase ratios in cellulolytic Saccharomyces cerevisiae.

Science.gov (United States)

Liu, Zhuo; Inokuma, Kentaro; Ho, Shih-Hsin; den Haan, Riaan; van Zyl, Willem H; Hasunuma, Tomohisa; Kondo, Akihiko

2017-06-01

Crystalline cellulose is one of the major contributors to the recalcitrance of lignocellulose to degradation, necessitating high dosages of cellulase to digest, thereby impeding the economic feasibility of cellulosic biofuels. Several recombinant cellulolytic yeast strains have been developed to reduce the cost of enzyme addition, but few of these strains are able to efficiently degrade crystalline cellulose due to their low cellulolytic activities. Here, by combining the cellulase ratio optimization with a novel screening strategy, we successfully improved the cellulolytic activity of a Saccharomyces cerevisiae strain displaying four different synergistic cellulases on the cell surface. The optimized strain exhibited an ethanol yield from Avicel of 57% of the theoretical maximum, and a 60% increase of ethanol titer from rice straw. To our knowledge, this work is the first optimization of the degradation of crystalline cellulose by tuning the cellulase ratio in a cellulase cell-surface display system. This work provides key insights in engineering the cellulase cocktail in a consolidated bioprocessing yeast strain. Biotechnol. Bioeng. 2017;114: 1201-1207. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Simultaneous beam sampling and aperture shape optimization for SPORT.

Science.gov (United States)

Zarepisheh, Masoud; Li, Ruijiang; Ye, Yinyu; Xing, Lei

2015-02-01

. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.

Simultaneous beam sampling and aperture shape optimization for SPORT

Energy Technology Data Exchange (ETDEWEB)

Zarepisheh, Masoud; Li, Ruijiang; Xing, Lei, E-mail: Lei@stanford.edu [Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States); Ye, Yinyu [Department of Management Science and Engineering, Stanford University, Stanford, California 94305 (United States)

2015-02-15

neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. Conclusions: The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.

Simultaneous beam sampling and aperture shape optimization for SPORT

International Nuclear Information System (INIS)

Zarepisheh, Masoud; Li, Ruijiang; Xing, Lei; Ye, Yinyu

2015-01-01

neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%. Conclusions: The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans

Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing

DEFF Research Database (Denmark)

Govindan, Kannan; Jha, P. C.; Garg, Kiran

2016-01-01

that emerge from that business’s economical, environmental and social dimensions. In this paper, we propose a multi-objective mixed integer mathematical problem for a generic closed-loop supply chain (CLSC) network to rationalise how a system’s product recovery helps to improve manufacturing sustainability....... The CLSC network proposed in this study consists of a hybrid manufacturing facility, warehouse, distribution centres, collection centres and a hybrid recovery facility (HRF). The proposed model determines the best location for the HRF and optimal flow of products, recovered parts and material...

Optimism is universal: exploring the presence and benefits of optimism in a representative sample of the world.

Science.gov (United States)

Gallagher, Matthew W; Lopez, Shane J; Pressman, Sarah D

2013-10-01

Current theories of optimism suggest that the tendency to maintain positive expectations for the future is an adaptive psychological resource associated with improved well-being and physical health, but the majority of previous optimism research has been conducted in industrialized nations. The present study examined (a) whether optimism is universal, (b) what demographic factors predict optimism, and (c) whether optimism is consistently associated with improved subjective well-being and perceived health worldwide. The present study used representative samples of 142 countries that together represent 95% of the world's population. The total sample of 150,048 individuals had a mean age of 38.28 (SD = 16.85) and approximately equal sex distribution (51.2% female). The relationships between optimism, subjective well-being, and perceived health were examined using hierarchical linear modeling. Results indicated that most individuals and most countries worldwide are optimistic and that higher levels of optimism are associated with improved subjective well-being and perceived health worldwide. The present study provides compelling evidence that optimism is a universal phenomenon and that the associations between optimism and improved psychological functioning are not limited to industrialized nations. © 2012 Wiley Periodicals, Inc.

Studies of fuel loading pattern optimization for a typical pressurized water reactor (PWR) using improved pivot particle swarm method

International Nuclear Information System (INIS)

Liu, Shichang; Cai, Jiejin

2012-01-01

Highlights: ► The mathematical model of loading pattern problems for PWR has been established. ► IPPSO was integrated with ‘donjon’ and ‘dragon’ into fuel arrangement optimizing code. ► The novel method showed highly efficiency for the LP problems. ► The core effective multiplication factor increases by about 10% in simulation cases. ► The power peaking factor decreases by about 0.6% in simulation cases. -- Abstract: An in-core fuel reload design tool using the improved pivot particle swarm method was developed for the loading pattern optimization problems in a typical PWR, such as Daya Bay Nuclear Power Plant. The discrete, multi-objective improved pivot particle swarm optimization, was integrated with the in-core physics calculation code ‘donjon’ based on finite element method, and assemblies’ group constant calculation code ‘dragon’, composing the optimization code for fuel arrangement. The codes of both ‘donjon’ and ‘dragon’ were programmed by Institute of Nuclear Engineering of Polytechnique Montréal, Canada. This optimization code was aiming to maximize the core effective multiplication factor (Keff), while keeping the local power peaking factor (Ppf) lower than a predetermined value to maintain fuel integrity. At last, the code was applied to the first cycle loading of Daya Bay Nuclear Power Plant. The result showed that, compared with the reference loading pattern design, the core effective multiplication factor increased by 9.6%, while the power peaking factor decreased by 0.6%, meeting the safety requirement.

PARALLEL IMPLEMENTATION OF CROSS-LAYER OPTIMIZATION - A PERFORMANCE EVALUATION BASED ON SWARM INTELLIGENCE

Directory of Open Access Journals (Sweden)

Vanaja Gokul

2012-01-01

Full Text Available In distributed systems real time optimizations need to be performed dynamically for better utilization of the network resources. Real time optimizations can be performed effectively by using Cross Layer Optimization (CLO within the network operating system. This paper presents the performance evaluation of Cross Layer Optimization (CLO in comparison with the traditional approach of Single-Layer Optimization (SLO. In the parallel implementation of the approaches the experimental study carried out indicates that the CLO results in a significant improvement in network utilization when compared to SLO. A variant of the Particle Swarm Optimization technique that utilizes Digital Pheromones (PSODP for better performance has been used here. A significantly higher speed up in performance was observed from the parallel implementation of CLO that used PSODP on a cluster of nodes.

Significant improvement in statin adherence and cholesterol levels after acute myocardial infarction

DEFF Research Database (Denmark)

Brogaard, Hilde Vaiva Tonstad; Køhn, Morten Ganderup; Berget, Oline Sofie

2012-01-01

Not all patients recovering from acute myocardial infarction (AMI) are optimally treated with statin, and their adherence to statin treatment may be inadequate. We set out to describe changes in statin treatment adherence and cholesterol values over time.......Not all patients recovering from acute myocardial infarction (AMI) are optimally treated with statin, and their adherence to statin treatment may be inadequate. We set out to describe changes in statin treatment adherence and cholesterol values over time....

Optimal configuration of power grid sources based on optimal particle swarm algorithm

Science.gov (United States)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

Optimization of Boiler Control to Improve the Load-following Capability of Power-plant Units

OpenAIRE

Mortensen, J. H.; Mølbak, T.; Andersen, Palle; Pedersen, Tom Søndergaard

1998-01-01

The capability to perform fast load changes has been an important issue in the power market, and will become increasingly more so due to the incresing commercialisation of the European power market. An optimizing control system for improving the load-following capability of power-plant units has therefore been developed. The system is implemented as a complement, producing control signals to be added to those of the existing boiler control system, a concept which has various practical advanta...

Die design optimization on sheet metal forming with considering the phenomenon of springback to improve product quality

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Darmawan Agung Setyo

2018-01-01

Full Text Available The process of sheet metal forming is one of the very important processes in manufacture of products mainly in the automotive field. In sheet metal forming, it is added a certain size at the die to tolerate a result of the elasticity restoration of material. Therefore, when the product is removed from the die then the process elastic recovery will end within the allowable tolerance size. Extra size of the die is one method to compensate for springback. The aim of this research is to optimize the die by entering a springback value in die design to improve product quality that is associated with accuracy the final size of the product. Simulation processes using AutoForm software are conducted to determine the optimal parameters to be used in the forming process. Variations the Blank Holder Force of 77 N, 97 N, and 117 N are applied to the plate material. The Blank Holder Force application higher than 97 N cannot be conducted because the Forming Limit Diagram indicates the risk of tearing. Then the Blank Holder Force of 37 N, 57 N and 77 N are selected and applied in cup drawing process. Even though a few of wrinkling are appear, however there is no significant deviation of dimension between the product and the design of cup.

Significance of MPEG-7 textural features for improved mass detection in mammography.

Science.gov (United States)

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

A study of the use of linear programming techniques to improve the performance in design optimization problems

Science.gov (United States)

Young, Katherine C.; Sobieszczanski-Sobieski, Jaroslaw

1988-01-01

This project has two objectives. The first is to determine whether linear programming techniques can improve performance when handling design optimization problems with a large number of design variables and constraints relative to the feasible directions algorithm. The second purpose is to determine whether using the Kreisselmeier-Steinhauser (KS) function to replace the constraints with one constraint will reduce the cost of total optimization. Comparisons are made using solutions obtained with linear and non-linear methods. The results indicate that there is no cost saving using the linear method or in using the KS function to replace constraints.

A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

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Wilmar Hernandez

2007-01-01

Full Text Available In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart sensors that today’s cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher’s interest in the fusion of intelligent sensors and optimal signal processing techniques.

Histone deacetylase inhibitor significantly improved the cloning efficiency of porcine somatic cell nuclear transfer embryos.

Science.gov (United States)

Huang, Yongye; Tang, Xiaochun; Xie, Wanhua; Zhou, Yan; Li, Dong; Yao, Chaogang; Zhou, Yang; Zhu, Jianguo; Lai, Liangxue; Ouyang, Hongsheng; Pang, Daxin

2011-12-01

Valproic acid (VPA), a histone deacetylase inbibitor, has been shown to generate inducible pluripotent stem (iPS) cells from mouse and human fibroblasts with a significant higher efficiency. Because successful cloning by somatic cell nuclear transfer (SCNT) undergoes a full reprogramming process in which the epigenetic state of a differentiated donor nuclear is converted into an embryonic totipotent state, we speculated that VPA would be useful in promoting cloning efficiency. Therefore, in the present study, we examined whether VPA can promote the developmental competence of SCNT embryos by improving the reprogramming state of donor nucleus. Here we report that 1 mM VPA for 14 to 16 h following activation significantly increased the rate of blastocyst formation of porcine SCNT embryos constructed from Landrace fetal fibroblast cells compared to the control (31.8 vs. 11.4%). However, we found that the acetylation level of Histone H3 lysine 14 and Histone H4 lysine 5 and expression level of Oct4, Sox2, and Klf4 was not significantly changed between VPA-treated and -untreated groups at the blastocyst stage. The SCNT embryos were transferred to 38 surrogates, and the cloning efficiency in the treated group was significantly improved compared with the control group. Taken together, we have demonstrated that VPA can improve both in vitro and in vivo development competence of porcine SCNT embryos.

Ceramic Composite Intermediate Temperature Stress-Rupture Properties Improved Significantly

Science.gov (United States)

Morscher, Gregory N.; Hurst, Janet B.

2002-01-01

Silicon carbide (SiC) composites are considered to be potential materials for future aircraft engine parts such as combustor liners. It is envisioned that on the hot side (inner surface) of the combustor liner, composites will have to withstand temperatures in excess of 1200 C for thousands of hours in oxidizing environments. This is a severe condition; however, an equally severe, if not more detrimental, condition exists on the cold side (outer surface) of the combustor liner. Here, the temperatures are expected to be on the order of 800 to 1000 C under high tensile stress because of thermal gradients and attachment of the combustor liner to the engine frame (the hot side will be under compressive stress, a less severe stress-state for ceramics). Since these composites are not oxides, they oxidize. The worst form of oxidation for strength reduction occurs at these intermediate temperatures, where the boron nitride (BN) interphase oxidizes first, which causes the formation of a glass layer that strongly bonds the fibers to the matrix. When the fibers strongly bond to the matrix or to one another, the composite loses toughness and strength and becomes brittle. To increase the intermediate temperature stress-rupture properties, researchers must modify the BN interphase. With the support of the Ultra-Efficient Engine Technology (UEET) Program, significant improvements were made as state-of-the-art SiC/SiC composites were developed during the Enabling Propulsion Materials (EPM) program. Three approaches were found to improve the intermediate-temperature stress-rupture properties: fiber-spreading, high-temperature silicon- (Si) doped boron nitride (BN), and outside-debonding BN.

Factors Related to Significant Improvement of Estimated Glomerular Filtration Rates in Chronic Hepatitis B Patients Receiving Telbivudine Therapy

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Te-Fu Lin

2017-01-01

Full Text Available Background and Aim. The improvement of estimated glomerular filtration rates (eGFRs in chronic hepatitis B (CHB patients receiving telbivudine therapy is well known. The aim of this study was to clarify the kinetics of eGFRs and to identify the significant factors related to the improvement of eGFRs in telbivudine-treated CHB patients in a real-world setting. Methods. Serial eGFRs were calculated every 3 months using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI equation. The patients were classified as CKD-1, -2, or -3 according to a baseline eGFR of ≥90, 60–89, or <60 mL/min/1.73 m2, respectively. A significant improvement of eGFR was defined as a more than 10% increase from the baseline. Results. A total of 129 patients were enrolled, of whom 36% had significantly improved eGFRs. According to a multivariate analysis, diabetes mellitus (DM (p=0.028 and CKD-3 (p=0.043 were both significantly related to such improvement. The rates of significant improvement of eGFR were about 73% and 77% in patients with DM and CKD-3, respectively. Conclusions. Telbivudine is an alternative drug of choice for the treatment of hepatitis B patients for whom renal safety is a concern, especially patients with DM and CKD-3.

Mobile physician reporting of clinically significant events-a novel way to improve handoff communication and supervision of resident on call activities.

Science.gov (United States)

Nabors, Christopher; Peterson, Stephen J; Aronow, Wilbert S; Sule, Sachin; Mumtaz, Arif; Shah, Tushar; Eskridge, Etta; Wold, Eric; Stallings, Gary W; Burak, Kathleen Kelly; Goldberg, Randy; Guo, Gary; Sekhri, Arunabh; Mathew, George; Khera, Sahil; Montoya, Jessica; Sharma, Mala; Paudel, Rajiv; Frishman, William H

2014-12-01

Reporting of clinically significant events represents an important mechanism by which patient safety problems may be identified and corrected. However, time pressure and cumbersome report entry procedures have discouraged the full participation of physicians. To improve the process, our internal medicine training program developed an easy-to-use mobile platform that combines the reporting process with patient sign-out. Between August 25, 2011, and January 25, 2012, our trainees entered clinically significant events into i-touch/i-phone/i-pad based devices functioning in wireless-synchrony with our desktop application. Events were collected into daily reports that were sent from the handoff system to program leaders and attending physicians to plan for rounds and to correct safety problems. Using the mobile module, residents entered 31 reportable events per month versus the 12 events per month that were reported via desktop during a previous 6-month study period. Advances in information technology now permit clinically significant events that take place during "off hours" to be identified and reported (via handoff) to next providers and to supervisors via collated reports. This information permits hospital leaders to correct safety issues quickly and effectively, while attending physicians are able to use information gleaned from the reports to optimize rounding plans and to provide additional oversight of trainee on call patient management decisions.

Parameters identification of photovoltaic models using an improved JAYA optimization algorithm

International Nuclear Information System (INIS)

Yu, Kunjie; Liang, J.J.; Qu, B.Y.; Chen, Xu; Wang, Heshan

2017-01-01

Highlights: • IJAYA algorithm is proposed to identify the PV model parameters efficiently. • A self-adaptive weight is introduced to purposefully adjust the search process. • Experience-based learning strategy is developed to enhance the population diversity. • Chaotic learning method is proposed to refine the quality of the best solution. • IJAYA features the superior performance in identifying parameters of PV models. - Abstract: Parameters identification of photovoltaic (PV) models based on measured current-voltage characteristic curves is significant for the simulation, evaluation, and control of PV systems. To accurately and reliably identify the parameters of different PV models, an improved JAYA (IJAYA) optimization algorithm is proposed in the paper. In IJAYA, a self-adaptive weight is introduced to adjust the tendency of approaching the best solution and avoiding the worst solution at different search stages, which enables the algorithm to approach the promising area at the early stage and implement the local search at the later stage. Furthermore, an experience-based learning strategy is developed and employed randomly to maintain the population diversity and enhance the exploration ability. A chaotic elite learning method is proposed to refine the quality of the best solution in each generation. The proposed IJAYA is used to solve the parameters identification problems of different PV models, i.e., single diode, double diode, and PV module. Comprehensive experiment results and analyses indicate that IJAYA can obtain a highly competitive performance compared with other state-of-the-state algorithms, especially in terms of accuracy and reliability.

Networked Timetable Stability Improvement Based on a Bilevel Optimization Programming Model

Directory of Open Access Journals (Sweden)

Xuelei Meng

2014-01-01

Full Text Available Train timetable stability is the possibility to recover the status of the trains to serve as arranged according to the original timetable when the trains are disturbed. To improve the train timetable stability from the network perspective, the bilevel programming model is constructed, in which the upper level programming is to optimize the timetable stability on the network level and the lower is to improve the timetable stability on the dispatching railway segments. Timetable stability on the network level is defined with the variances of the utilization coefficients of the section capacity and station capacity. Weights of stations and sections are decided by the capacity index number and the degrees. The lower level programming focuses on the buffer time distribution plan of the trains operating on the sections and stations, taking the operating rules of the trains as constraints. A novel particle swarm algorithm is proposed and designed for the bilevel programming model. The computing case proves the feasibility of the model and the efficiency of the algorithm. The method outlined in this paper can be embedded in the networked train operation dispatching system.

Joint Optimized CPU and Networking Control Scheme for Improved Energy Efficiency in Video Streaming on Mobile Devices

Directory of Open Access Journals (Sweden)

Sung-Woong Jo

2017-01-01

Full Text Available Video streaming service is one of the most popular applications for mobile users. However, mobile video streaming services consume a lot of energy, resulting in a reduced battery life. This is a critical problem that results in a degraded user’s quality of experience (QoE. Therefore, in this paper, a joint optimization scheme that controls both the central processing unit (CPU and wireless networking of the video streaming process for improved energy efficiency on mobile devices is proposed. For this purpose, the energy consumption of the network interface and CPU is analyzed, and based on the energy consumption profile a joint optimization problem is formulated to maximize the energy efficiency of the mobile device. The proposed algorithm adaptively adjusts the number of chunks to be downloaded and decoded in each packet. Simulation results show that the proposed algorithm can effectively improve the energy efficiency when compared with the existing algorithms.

Optimal treatment scheduling of ionizing radiation and sunitinib improves the antitumor activity and allows dose reduction

International Nuclear Information System (INIS)

Kleibeuker, Esther A; Hooven, Matthijs A ten; Castricum, Kitty C; Honeywell, Richard; Griffioen, Arjan W; Verheul, Henk M; Slotman, Ben J; Thijssen, Victor L

2015-01-01

The combination of radiotherapy with sunitinib is clinically hampered by rare but severe side effects and varying results with respect to clinical benefit. We studied different scheduling regimes and dose reduction in sunitinib and radiotherapy in preclinical tumor models to improve potential outcome of this combination treatment strategy. The chicken chorioallantoic membrane (CAM) was used as an angiogenesis in vivo model and as a xenograft model with human tumor cells (HT29 colorectal adenocarcinoma, OE19 esophageal adenocarcinoma). Treatment consisted of ionizing radiation (IR) and sunitinib as single therapy or in combination, using different dose-scheduling regimes. Sunitinib potentiated the inhibitory effect of IR (4 Gy) on angiogenesis. In addition, IR (4 Gy) and sunitinib (4 days of 32.5 mg/kg per day) inhibited tumor growth. Ionizing radiation induced tumor cell apoptosis and reduced proliferation, whereas sunitinib decreased tumor angiogenesis and reduced tumor cell proliferation. When IR was applied before sunitinib, this almost completely inhibited tumor growth, whereas concurrent IR was less effective and IR after sunitinib had no additional effect on tumor growth. Moreover, optimal scheduling allowed a 50% dose reduction in sunitinib while maintaining comparable antitumor effects. This study shows that the therapeutic efficacy of combination therapy improves when proper dose-scheduling is applied. More importantly, optimal treatment regimes permit dose reductions in the angiogenesis inhibitor, which will likely reduce the side effects of combination therapy in the clinical setting. Our study provides important leads to optimize combination treatment in the clinical setting

Improvement of the GERDA Ge Detectors Energy Resolution by an Optimized Digital Signal Processing

Science.gov (United States)

Benato, G.; D'Andrea, V.; Cattadori, C.; Riboldi, S.

GERDA is a new generation experiment searching for neutrinoless double beta decay of 76Ge, operating at INFN Gran Sasso Laboratories (LNGS) since 2010. Coaxial and Broad Energy Germanium (BEGe) Detectors have been operated in liquid argon (LAr) in GERDA Phase I. In the framework of the second GERDA experimental phase, both the contacting technique, the connection to and the location of the front end readout devices are novel compared to those previously adopted, and several tests have been performed. In this work, starting from considerations on the energy scale stability of the GERDA Phase I calibrations and physics data sets, an optimized pulse filtering method has been developed and applied to the Phase II pilot tests data sets, and to few GERDA Phase I data sets. In this contribution the detector performances in term of energy resolution and time stability are here presented. The improvement of the energy resolution, compared to standard Gaussian shaping adopted for Phase I data analysis, is discussed and related to the optimized noise filtering capability. The result is an energy resolution better than 0.1% at 2.6 MeV for the BEGe detectors operated in the Phase II pilot tests and an improvement of the energy resolution in LAr of about 8% achieved on the GERDA Phase I calibration runs, compared to previous analysis algorithms.

SiC-VJFETs power switching devices: an improved model and parameter optimization technique

Science.gov (United States)

Ben Salah, T.; Lahbib, Y.; Morel, H.

2009-12-01

Silicon carbide junction field effect transistor (SiC-JFETs) is a mature power switch newly applied in several industrial applications. SiC-JFETs are often simulated by Spice model in order to predict their electrical behaviour. Although such a model provides sufficient accuracy for some applications, this paper shows that it presents serious shortcomings in terms of the neglect of the body diode model, among many others in circuit model topology. Simulation correction is then mandatory and a new model should be proposed. Moreover, this paper gives an enhanced model based on experimental dc and ac data. New devices are added to the conventional circuit model giving accurate static and dynamic behaviour, an effect not accounted in the Spice model. The improved model is implemented into VHDL-AMS language and steady-state dynamic and transient responses are simulated for many SiC-VJFETs samples. Very simple and reliable optimization algorithm based on the optimization of a cost function is proposed to extract the JFET model parameters. The obtained parameters are verified by comparing errors between simulations results and experimental data.

A multi-criteria optimization and decision-making approach for improvement of food engineering processes

Directory of Open Access Journals (Sweden)

Alik Abakarov

2013-04-01

Full Text Available The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demonstrated using experimental data obtained on osmotic dehydration of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses, namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality. Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP and the Tabular Method (TM, were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.

On the efficiency of chaos optimization algorithms for global optimization

International Nuclear Information System (INIS)

Yang Dixiong; Li Gang; Cheng Gengdong

2007-01-01

Chaos optimization algorithms as a novel method of global optimization have attracted much attention, which were all based on Logistic map. However, we have noticed that the probability density function of the chaotic sequences derived from Logistic map is a Chebyshev-type one, which may affect the global searching capacity and computational efficiency of chaos optimization algorithms considerably. Considering the statistical property of the chaotic sequences of Logistic map and Kent map, the improved hybrid chaos-BFGS optimization algorithm and the Kent map based hybrid chaos-BFGS algorithm are proposed. Five typical nonlinear functions with multimodal characteristic are tested to compare the performance of five hybrid optimization algorithms, which are the conventional Logistic map based chaos-BFGS algorithm, improved Logistic map based chaos-BFGS algorithm, Kent map based chaos-BFGS algorithm, Monte Carlo-BFGS algorithm, mesh-BFGS algorithm. The computational performance of the five algorithms is compared, and the numerical results make us question the high efficiency of the chaos optimization algorithms claimed in some references. It is concluded that the efficiency of the hybrid optimization algorithms is influenced by the statistical property of chaotic/stochastic sequences generated from chaotic/stochastic algorithms, and the location of the global optimum of nonlinear functions. In addition, it is inappropriate to advocate the high efficiency of the global optimization algorithms only depending on several numerical examples of low-dimensional functions

Optimization Controller for Mechatronic Sun Tracking System to Improve Performance

Directory of Open Access Journals (Sweden)

Mustafa Engin

2013-01-01

Full Text Available An embedded system that contains hardware and software was developed for two-axis solar tracking system to improve photovoltaic panel utilization. The hardware section of the embedded system consists of a 32-bit ARM core microcontroller, motor driver circuits, a motion control unit, pyranometer, GPS receiver, and an anemometer. The real-time control algorithm enables the solar tracker to operate automatically without external control as a stand-alone system, combining the advantages of the open-loop and the closed-loop control methods. The pyranometer is employed to continuously send radiation data to the controller if the measured radiation is above the lower radiation limit the photovoltaic panel can generate power, guaranteeing the solar tracking process to be highly efficient. The anemometer is utilized in the system to ensure that the solar tracking procedure halts under high wind speed conditions to protect the entire system. Latitude, longitude, altitude, date, and real-time clock data are provided by GPS receiver. The algorithm calculates solar time using astronomical equations with GPS data and converts it to pulse-width modulated motor control signal. The overall objective of this study is to develop a control algorithm that improves performance and reliability of the two-axis solar tracker, focusing on optimization of the controller board, drive hardware, and software.

Optimization analysis of propulsion motor control efficiency

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CAI Qingnan

2017-12-01

Full Text Available [Objectives] This paper aims to strengthen the control effect of propulsion motors and decrease the energy used during actual control procedures.[Methods] Based on the traditional propulsion motor equivalence circuit, we increase the iron loss current component, introduce the definition of power matching ratio, calculate the highest efficiency of a motor at a given speed and discuss the flux corresponding to the power matching ratio with the highest efficiency. In the original motor vector efficiency optimization control module, an efficiency optimization control module is added so as to achieve motor efficiency optimization and energy conservation.[Results] MATLAB/Simulink simulation data shows that the efficiency optimization control method is suitable for most conditions. The operation efficiency of the improved motor model is significantly higher than that of the original motor model, and its dynamic performance is good.[Conclusions] Our motor efficiency optimization control method can be applied in engineering to achieve energy conservation.

Optimization Based Shunt APF Controller to Mitigate Reactive Power, Burden of Neutral Conductor, Current Harmonics and Improve cosɸ

Directory of Open Access Journals (Sweden)

P. Anjana

2017-03-01

Full Text Available This paper presents a Modified Gravitational Search Algorithm (MGSA to improve the performance of PI controller in varying load condition. The proposed approach is capable of mitigating reactive power, neutral current, source current THD and significant improvement in power factor nearly unity (0.997. The DC link voltage across the capacitor is controlled by PI controller which is deciding the performance of shunt APF. Hence, the robust optimization technique based integral time square error (ITSE with consideration of weight factor (α & β, maximum overshoot ((|(∆_Ve ̅〖(n〗_max | and setling time t_s-t_0, is providing the optimum solution of Kp & Ki. The robustness of proposed objective function and algorithm compared with GSA based three other error criterion techniques. The efficiency of the proposed controller has been tested over nonlinear and unbalance loading condition. The performance of ITSE based MGSA-PI controller is batter then other three error criterion techniques. The values of THD are below the mark of 5% specified in IEEE-519 standard.

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

Directory of Open Access Journals (Sweden)

Weitian Lin

2014-01-01

Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

Optimization of Boiler Control to Improve the Load-following Capability of Power-plant Units

DEFF Research Database (Denmark)

Mortensen, J. H.; Mølbak, T.; Andersen, Palle

The capability to perform fast load changes has been an important issue in the power market, and will become increasingly more so due to the incresing commercialisation of the European power market. An optimizing control system for improving the load-following capability of power-plant units has...... tests on a 265 MW coal-fired power-plant unit reveals that the maximum allowable load gradient that can be imposed on the plant, can be increased from 4 MW/min. to 8 MW/min....

Optimization of Boiler Control to Improve the Load-following Capability of Power-plant Units

DEFF Research Database (Denmark)

Mortensen, J. H.; Mølbak, T.; Andersen, Palle

1998-01-01

The capability to perform fast load changes has been an important issue in the power market, and will become increasingly more so due to the incresing commercialisation of the European power market. An optimizing control system for improving the load-following capability of power-plant units has...... tests on a 265 MW coal-fired power-plant unit reveals that the maximum allowable load gradient that can be imposed on the plant, can be increased from 4 MW/min. to 8 MW/min....

A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits

OpenAIRE

Simmonds, Anna J.

2015-01-01

Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred fr...

ZT Optimization: An Application Focus.

Science.gov (United States)

Tuley, Richard; Simpson, Kevin

2017-03-17

Significant research has been performed on the challenge of improving thermoelectric materials, with maximum peak figure of merit, ZT, the most common target. We use an approximate thermoelectric material model, matched to real materials, to demonstrate that when an application is known, average ZT is a significantly better optimization target. We quantify this difference with some examples, with one scenario showing that changing the doping to increase peak ZT by 19% can lead to a performance drop of 16%. The importance of average ZT means that the temperature at which the ZT peak occurs should be given similar weight to the value of the peak. An ideal material for an application operates across the maximum peak ZT, otherwise maximum performance occurs when the peak value is reduced in order to improve the peak position.

Study on network traffic forecast model of SVR optimized by GAFSA

International Nuclear Information System (INIS)

Liu, Yuan; Wang, RuiXue

2016-01-01

There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

Inventory Optimization through Safety Stock Schemata

Directory of Open Access Journals (Sweden)

Abdul Aleem

2013-04-01

Full Text Available In the complex business environment and stiff competition, inventory optimization in an industry's supply chain has gained tremendous significance. It has become business imperative to optimally tune the supply chain and save lot of working capital by reducing inventory levels; this can surely be done while increasing the customer service level and utilizing the internal capacities optimally. Stock out costs and stock surplus costs both impact businesses badly, the former in the form of opportunity loss and resultantly causing customer annoyance and later in high financial markups and increasing cost and reducing margins accordingly. So inventory optimization can essentially help to reduce costs, which results in a considerable improvement of the company performance indicators. Traditional IMS (Inventory Management System followed in a selected manufacturing industry has been examined for all types of inventories, i.e. raw materials; WIP (Work In Process, and finished goods as a case study. The paper suggests an optimized inventory model for an organization to provide the best possible customer service within the restraint of the lowest practical inventory costs. The safety stock optimization was implemented in a complex business environment and considerable savings were realized thereof

Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes

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Trine Krogh-Madsen

2017-12-01

Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.

Slot Optimization Design of Induction Motor for Electric Vehicle

Science.gov (United States)

Shen, Yiming; Zhu, Changqing; Wang, Xiuhe

2018-01-01

Slot design of induction motor has a great influence on its performance. The RMxprt module based on magnetic circuit method can be used to analyze the influence of rotor slot type on motor characteristics and optimize slot parameters. In this paper, the authors take an induction motor of electric vehicle for a typical example. The first step of the design is to optimize the rotor slot by RMxprt, and then compare the main performance of the motor before and after the optimization through Ansoft Maxwell 2D. After that, the combination of optimum slot type and the optimum parameters are obtained. The results show that the power factor and the starting torque of the optimized motor have been improved significantly. Furthermore, the electric vehicle works at a better running status after the optimization.

Pre-ejection period by radial artery tonometry supplements echo doppler findings during biventricular pacemaker optimization

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Qamruddin Salima

2011-07-01

Full Text Available Abstract Background Biventricular (Biv pacemaker echo optimization has been shown to improve cardiac output however is not routinely used due to its complexity. We investigated the role of a simple method involving computerized pre-ejection time (PEP assessment by radial artery tonometry in guiding Biv pacemaker optimization. Methods Blinded echo and radial artery tonometry were performed simultaneously in 37 patients, age 69.1 ± 12.8 years, left ventricular (LV ejection fraction (EF 33 ± 10%, during Biv pacemaker optimization. Effect of optimization on echo derived velocity time integral (VTI, ejection time (ET, myocardial performance index (MPI, radial artery tonometry derived PEP and echo-radial artery tonometry derived PEP/VTI and PEP/ET indices was evaluated. Results Significant improvement post optimization was achieved in LV ET (286.9 ± 37.3 to 299 ± 34.6 ms, p Conclusion An acute shortening of PEP by radial artery tonometry occurs post Biv pacemaker optimization and correlates with improvement in hemodynamics by echo Doppler and may provide a cost-efficient approach to assist with Biv pacemaker echo optimization.

Co-Optimization of Internal Combustion Engines and Biofuels

Energy Technology Data Exchange (ETDEWEB)

McCormick, Robert L.

2016-03-08

The development of advanced engines has significant potential advantages in reduced aftertreatment costs for air pollutant emission control, and just as importantly for efficiency improvements and associated greenhouse gas emission reductions. There are significant opportunities to leverage fuel properties to create more optimal engine designs for both advanced spark-ignition and compression-ignition combustion strategies. The fact that biofuel blendstocks offer a potentially low-carbon approach to fuel production, leads to the idea of optimizing the entire fuel production-utilization value chain as a system from the standpoint of life cycle greenhouse gas emissions. This is a difficult challenge that has yet to be realized. This presentation will discuss the relationship between chemical structure and critical fuel properties for more efficient combustion, survey the properties of a range of biofuels that may be produced in the future, and describe the ongoing challenges of fuel-engine co-optimization.

Optimization of municipal pressure pumping station layout and sewage pipe network design

Science.gov (United States)

Tian, Jiandong; Cheng, Jilin; Gong, Yi

2018-03-01

Accelerated urbanization places extraordinary demands on sewer networks; thus optimization research to improve the design of these systems has practical significance. In this article, a subsystem nonlinear programming model is developed to optimize pumping station layout and sewage pipe network design. The subsystem model is expanded into a large-scale complex nonlinear programming system model to find the minimum total annual cost of the pumping station and network of all pipe segments. A comparative analysis is conducted using the sewage network in Taizhou City, China, as an example. The proposed method demonstrated that significant cost savings could have been realized if the studied system had been optimized using the techniques described in this article. Therefore, the method has practical value for optimizing urban sewage projects and provides a reference for theoretical research on optimization of urban drainage pumping station layouts.

Improving the energy balance of grass-based anaerobic digestion through harvesting optimization

DEFF Research Database (Denmark)

Tsapekos, Panagiotis; Kougias, Panagiotis; Egelund, H.

with a number of coarse barbs) to simultaneously mow and mechanically pretreat two different lignocellulosic substrates. Thus, ensiled meadow grass was initially examined at the first experimental set up. Regarding the second field test, an area sowed with regularly cultivated grass was harvested. In order......) protocol. The findings showed that methane production can efficiently be enhanced by mechanical pretreatment applied at the harvesting step. More specifically, the most effective treatment yielded more than 10% increase in the bioenergy production from both examined grass silages. Our study demonstrates...... that the appropriate harvester can improve the energy output by approximately 2.4 GJ/ha under optimal conditions and subsequently, the overall sustainability of grass-based AD....

Improving real-time estimation of heavy-to-extreme precipitation using rain gauge data via conditional bias-penalized optimal estimation

Science.gov (United States)

Seo, Dong-Jun; Siddique, Ridwan; Zhang, Yu; Kim, Dongsoo

2014-11-01

A new technique for gauge-only precipitation analysis for improved estimation of heavy-to-extreme precipitation is described and evaluated. The technique is based on a novel extension of classical optimal linear estimation theory in which, in addition to error variance, Type-II conditional bias (CB) is explicitly minimized. When cast in the form of well-known kriging, the methodology yields a new kriging estimator, referred to as CB-penalized kriging (CBPK). CBPK, however, tends to yield negative estimates in areas of no or light precipitation. To address this, an extension of CBPK, referred to herein as extended conditional bias penalized kriging (ECBPK), has been developed which combines the CBPK estimate with a trivial estimate of zero precipitation. To evaluate ECBPK, we carried out real-world and synthetic experiments in which ECBPK and the gauge-only precipitation analysis procedure used in the NWS's Multisensor Precipitation Estimator (MPE) were compared for estimation of point precipitation and mean areal precipitation (MAP), respectively. The results indicate that ECBPK improves hourly gauge-only estimation of heavy-to-extreme precipitation significantly. The improvement is particularly large for estimation of MAP for a range of combinations of basin size and rain gauge network density. This paper describes the technique, summarizes the results and shares ideas for future research.

Parameter Improved Particle Swarm Optimization Based Direct-Current Vector Control Strategy for Solar PV System

Directory of Open Access Journals (Sweden)

NAMMALVAR, P.

2018-02-01

Full Text Available This paper projects Parameter Improved Particle Swarm Optimization (PIPSO based direct current vector control technology for the integration of photovoltaic array in an AC micro-grid to enhance the system performance and stability. A photovoltaic system incorporated with AC micro-grid is taken as the pursuit of research study. The test system features two power converters namely, PV side converter which consists of DC-DC boost converter with Perturbation and Observe (P&O MPPT control to reap most extreme power from the PV array, and grid side converter which consists of Grid Side-Voltage Source Converter (GS-VSC with proposed direct current vector control strategy. The gain of the proposed controller is chosen from a set of three values obtained using apriori test and tuned through the PIPSO algorithm so that the Integral of Time multiplied Absolute Error (ITAE between the actual and the desired DC link capacitor voltage reaches a minimum and allows the system to extract maximum power from PV system, whereas the existing d-q control strategy is found to perform slowly to control the DC link voltage under varying solar insolation and load fluctuations. From simulation results, it is evident that the proposed optimal control technique provides robust control and improved efficiency.

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

Science.gov (United States)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN

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Arie Rachmad Syulistyo

2016-02-01

Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

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Shao-Fei Jiang

2014-01-01

Full Text Available Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO. This paper presents an improved MPSCO algorithm (IMPSCO firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA. The results show threefold: (1 the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2 the damage location can be accurately detected using the damage threshold proposed in this paper; and (3 compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

The challenges of ESRD care in developing economies: sub-Saharan African opportunities for significant improvement.

Science.gov (United States)

Bamgboye, Ebun Ladipo

Chronic kidney disease (CKD) is a significant cause of morbidity and mortality in sub-Saharan Africa. This, along with other noncommunicable diseases like hypertension, diabetes, and heart diseases, poses a double burden on a region that is still struggling to cope with the scourge of communicable diseases like malaria, tuberculosis, HIV, and more recently Ebola. Causes of CKD in the region are predominantly glomerulonephritis and hypertension, although type 2 diabetes is also becoming a significant cause as is the retroviral disease. Patients are generally younger than in the developed world, and there is a significant male preponderance. Most patients are managed by hemodialysis, with peritoneal dialysis and kidney transplantation being available in only few countries in the region. Government funding and support for dialysis is often unavailable, and when available, often with restrictions. There is a dearth of trained manpower to treat the disease, and many countries have a limited number of units, which are often ill-equipped to deal adequately with the number of patients who require end-stage renal disease (ESRD) care in the region. Although there has been a significant improvement when compared with the situation, even as recently as 10 years ago, there is also the potential for further improvement, which would significantly improve the outcomes in patients with ESRD in the region. The information in this review was obtained from a combination of renal registry reports (published and unpublished), published articles, responses to a questionnaire sent to nephrologists prior to the World Congress of Nephrology (WCN) in Cape Town, and from nephrologists attending the WCN in Cape Town (March 13 - 17, 2015).

Interrelations of stress, optimism and control in older people's psychological adjustment.

Science.gov (United States)

Bretherton, Susan Jane; McLean, Louise Anne

2015-06-01

To investigate the influence of perceived stress, optimism and perceived control of internal states on the psychological adjustment of older adults. The sample consisted of 212 older adults, aged between 58 and 103 (M = 80.42 years, SD = 7.31 years), living primarily in retirement villages in Melbourne, Victoria. Participants completed the Perceived Stress Scale, Life Orientation Test-Revised, Perceived Control of Internal States Scale and the World Health Organisation Quality of Life-Bref. Optimism significantly mediated the relationship between older people's perceived stress and psychological health, and perceived control of internal states mediated the relationships among stress, optimism and psychological health. The variables explained 49% of the variance in older people's psychological adjustment. It is suggested that strategies to improve optimism and perceived control may improve the psychological adjustment of older people struggling to adapt to life's stressors. © 2014 ACOTA.

Optimal Frequency Ranges for Sub-Microsecond Precision Pulsar Timing

Science.gov (United States)

Lam, Michael Timothy; McLaughlin, Maura; Cordes, James; Chatterjee, Shami; Lazio, Joseph

2018-01-01

Precision pulsar timing requires optimization against measurement errors and astrophysical variance from the neutron stars themselves and the interstellar medium. We investigate optimization of arrival time precision as a function of radio frequency and bandwidth. We find that increases in bandwidth that reduce the contribution from receiver noise are countered by the strong chromatic dependence of interstellar effects and intrinsic pulse-profile evolution. The resulting optimal frequency range is therefore telescope and pulsar dependent. We demonstrate the results for five pulsars included in current pulsar timing arrays and determine that they are not optimally observed at current center frequencies. We also find that arrival-time precision can be improved by increases in total bandwidth. Wideband receivers centered at high frequencies can reduce required overall integration times and provide significant improvements in arrival time uncertainty by a factor of $\\sim$$\\sqrt{2}$ in most cases, assuming a fixed integration time. We also discuss how timing programs can be extended to pulsars with larger dispersion measures through the use of higher-frequency observations.

An improved artificial physical optimization algorithm for dynamic dispatch of generators with valve-point effects and wind power

International Nuclear Information System (INIS)

Yuan, Xiaohui; Ji, Bin; Zhang, Shuangquan; Tian, Hao; Chen, Zhihuan

2014-01-01

Highlights: • Dynamic load economic dispatch with wind power (DLEDW) model is established. • Markov chains combined with scenario analysis method are used to predict wind power. • Chance constrained technique is used to simulate the impacts of wind forecast error. • Improved artificial physical optimization algorithm is proposed to solve DLEDW. • Heuristic search strategies are applied to handle the constraints of DLEDW. - Abstract: Wind power, a kind of promising renewable energy resource, has recently been getting more attractive because of various environmental and economic considerations. But the penetration of wind power with its fluctuation nature has made the operation of power system more intractable. To coordinate the reliability and operation cost, this paper established a stochastic model of dynamic load economic dispatch with wind integration (DLEDW). In this model, constraints such as ramping up/down capacity, prohibited operating zone are considered and effects of valve-point are taken into account. Markov chains combined with scenario analysis method is used to generate predictive values of wind power and chance constrained programming (CCP) is applied to simulate the impacts of wind power fluctuation on system operation. An improved artificial physical optimization algorithm is presented to solve the DLEDW problem. Heuristic strategies based on the priority list and stochastic simulation techniques are proposed to handle the constraints. In addition, a local chaotic mutation strategy is applied to overcome the disadvantage of premature convergence of artificial physical optimization algorithm. Two test systems with and without wind power integration are used to verify the feasibility and effectiveness of the proposed method and the results are compared with those of gravitational search algorithm, particle swarm optimization and standard artificial physical optimization. The simulation results demonstrate that the proposed method has a

Optimal digital filtering for tremor suppression.

Science.gov (United States)

Gonzalez, J G; Heredia, E A; Rahman, T; Barner, K E; Arce, G R

2000-05-01

Remote manually operated tasks such as those found in teleoperation, virtual reality, or joystick-based computer access, require the generation of an intermediate electrical signal which is transmitted to the controlled subsystem (robot arm, virtual environment, or a cursor in a computer screen). When human movements are distorted, for instance, by tremor, performance can be improved by digitally filtering the intermediate signal before it reaches the controlled device. This paper introduces a novel tremor filtering framework in which digital equalizers are optimally designed through pursuit tracking task experiments. Due to inherent properties of the man-machine system, the design of tremor suppression equalizers presents two serious problems: 1) performance criteria leading to optimizations that minimize mean-squared error are not efficient for tremor elimination and 2) movement signals show ill-conditioned autocorrelation matrices, which often result in useless or unstable solutions. To address these problems, a new performance indicator in the context of tremor is introduced, and the optimal equalizer according to this new criterion is developed. Ill-conditioning of the autocorrelation matrix is overcome using a novel method which we call pulled-optimization. Experiments performed with artificially induced vibrations and a subject with Parkinson's disease show significant improvement in performance. Additional results, along with MATLAB source code of the algorithms, and a customizable demo for PC joysticks, are available on the Internet at http:¿tremor-suppression.com.

Improving thermal performance of an existing UK district heat network: a case for temperature optimization

DEFF Research Database (Denmark)

Tunzi, Michele; Boukhanouf, Rabah; Li, Hongwei

2018-01-01

This paper presents results of a research study into improving energy performance of small-scale district heat network through water supply and return temperature optimization technique. The case study involves establishing the baseline heat demand of the estate’s buildings, benchmarking...... the existing heat network operating parameters, and defining the optimum supply and return temperature. A stepwise temperature optimization technique of plate radiators heat emitters was applied to control the buildings indoor thermal comfort using night set back temperature strategy of 21/18 °C....... It was established that the heat network return temperature could be lowered from the current measured average of 55 °C to 35.6 °C, resulting in overall reduction of heat distribution losses and fuel consumption of 10% and 9% respectively. Hence, the study demonstrates the potential of operating existing heat...

Innovative practice model to optimize resource utilization and improve access to care for high-risk and BRCA+ patients.

Science.gov (United States)

Head, Linden; Nessim, Carolyn; Usher Boyd, Kirsty

2017-02-01

Bilateral prophylactic mastectomy (BPM) has demonstrated breast cancer risk reduction in high-risk/ BRCA + patients. However, priority of active cancers coupled with inefficient use of operating room (OR) resources presents challenges in offering BPM in a timely manner. To address these challenges, a rapid access prophylactic mastectomy and immediate reconstruction (RAPMIR) program was innovated. The purpose of this study was to evaluate RAPMIR with regards to access to care and efficiency. We retrospectively reviewed the cases of all high-risk/ BRCA + patients having had BPM between September 2012 and August 2014. Patients were divided into 2 groups: those managed through the traditional model and those managed through the RAPMIR model. RAPMIR leverages 2 concurrently running ORs with surgical oncology and plastic surgery moving between rooms to complete 3 combined BPMs with immediate reconstruction in addition to 1-2 independent cases each operative day. RAPMIR eligibility criteria included high-risk/ BRCA + status; BPM with immediate, implant-based reconstruction; and day surgery candidacy. Wait times, case volumes and patient throughput were measured and compared. There were 16 traditional patients and 13 RAPMIR patients. Mean wait time (days from referral to surgery) for RAPMIR was significantly shorter than for the traditional model (165.4 v. 309.2 d, p = 0.027). Daily patient throughput (4.3 v. 2.8), plastic surgery case volume (3.7 v. 1.6) and surgical oncology case volume (3.0 v. 2.2) were significantly greater in the RAPMIR model than the traditional model ( p = 0.003, p < 0.001 and p = 0.015, respectively). A multidisciplinary model with optimized scheduling has the potential to improve access to care and optimize resource utilization.

Adaptive Bacterial Foraging Optimization

Directory of Open Access Journals (Sweden)

Hanning Chen

2011-01-01

Full Text Available Bacterial Foraging Optimization (BFO is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO, employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO and a real-coded genetic algorithm (GA on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Improvements of visual X-ray inspection with optimized digital detector technology. Faster and more reliable inspection with High Dynamic Radiology (HDR)

International Nuclear Information System (INIS)

Bavendiek, Klaus

2010-01-01

Improvements in speed and contrast resolution of Digital Detector Arrays (DDA) and significant higher power of X-Ray tubes in combination with a small focal spot open the door to an improved visual inspection of castings for automotive and aerospace applications. The result is a film-like image quality of castings in a live view. For the new image quality the x-ray parameter have to be optimized in energy and the subject contrast has to be increased to avoid that flaws are covered by the noise in the image. HDR - high dynamic radiology - expands the local contrast in the image and transfers the grey values to the range the human inspector can separate. Due to the movement in the image the inspector gets a glas-like impression of the object and the flaws allowing him to do a decision about the 3D position of a flaw in the object. (orig.)

Optimization-based topology identification of complex networks

International Nuclear Information System (INIS)

Tang Sheng-Xue; Chen Li; He Yi-Gang

2011-01-01

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs.

Science.gov (United States)

Heim, Catherine; Cole, Elaine; West, Anita; Tai, Nigel; Brohi, Karim

2016-09-01

Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement. We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator. Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care. TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs. Copyright © 2016 Elsevier

The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters

Directory of Open Access Journals (Sweden)

Yancai Xiao

2016-05-01

Full Text Available In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD wind turbines, this paper proposes a fuzzy proportional integral (PI controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.