Optimalization of selected RFID systems Parameters
Directory of Open Access Journals (Sweden)
Peter Vestenicky
2004-01-01
Full Text Available This paper describes procedure for maximization of RFID transponder read range. This is done by optimalization of magnetics field intensity at transponder place and by optimalization of antenna and transponder coils coupling factor. Results of this paper can be used for RFID with inductive loop, i.e. system working in near electromagnetic field.
Numerical optimization methods for controlled systems with parameters
Tyatyushkin, A. I.
2017-10-01
First- and second-order numerical methods for optimizing controlled dynamical systems with parameters are discussed. In unconstrained-parameter problems, the control parameters are optimized by applying the conjugate gradient method. A more accurate numerical solution in these problems is produced by Newton's method based on a second-order functional increment formula. Next, a general optimal control problem with state constraints and parameters involved on the righthand sides of the controlled system and in the initial conditions is considered. This complicated problem is reduced to a mathematical programming one, followed by the search for optimal parameter values and control functions by applying a multimethod algorithm. The performance of the proposed technique is demonstrated by solving application problems.
Digital simulation of continuous systems with and without parameter optimization
International Nuclear Information System (INIS)
Gitt, W.; Herrmann, H.J.
1977-05-01
In addition to the simulation of steady systems the simulation system DISIOP (DIgital SImulation with OPtimization) described here, which may still be improved, enables an optimization of, at present, 6 parameters according to a criterion randomly chosen by the user. The examples given show a vast field of possible applications, from simple simulation to optimization and boundary value problems with one boundary value. Some limits of application are: 1) due to the serial working of the digital computer, real-time problem solutions are impossible; 2) in high-frequency runs, the step width number may become critical (long computing time, numerical instabilities). (orig./WB) [de
Optimization Design of Multi-Parameters in Rail Launcher System
Directory of Open Access Journals (Sweden)
Yujiao Zhang
2014-05-01
Full Text Available Today the energy storage systems are still encumbering, therefore it is useful to think about the optimization of a railgun system in order to achieve the best performance with the lowest energy input. In this paper, an optimal design method considering 5 parameters is proposed to improve the energy conversion efficiency of a simple railgun. In order to avoid costly trials, the field- circuit method is employed to analyze the operations of different structural railguns with different parameters respectively. And the orthogonal test approach is used to guide the simulation for choosing the better parameter combinations, as well reduce the calculation cost. The research shows that the proposed method gives a better result in the energy efficiency of the system. To improve the energy conversion efficiency of electromagnetic rail launchers, the selection of more parameters must be considered in the design stage, such as the width, height and length of rail, the distance between rail pair, and pulse forming inductance. However, the relationship between these parameters and energy conversion efficiency cannot be directly described by one mathematical expression. So optimization methods must be applied to conduct design. In this paper, a rail launcher with five parameters was optimized by using orthogonal test method. According to the arrangement of orthogonal table, the better parameters’ combination can be obtained through less calculation. Under the condition of different parameters’ value, field and circuit simulation analysis were made. The results show that the energy conversion efficiency of the system is increased by 71.9 % after parameters optimization.
METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS
Directory of Open Access Journals (Sweden)
V. Panteleev Andrei
2017-01-01
Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and
Parameter optimization CCPP and coolant system gas turbine
Клер, Александр Матвеевич; Захаров, Юрий Борисович; Потанина, Юлия Михайловна
2013-01-01
Today most researchers optimize the parameters of cycles in combined cycle power plants without detailed calculations of the gas turbine flow path, which often involves separate optimization of the steam cycle and the gas turbine parameters, including the parameters of the gas turbine flow path that are usually known beforehand. This paper is the first to suggest a technique for coordinated optimization of combined cycle power plants, where both the parameters of the steam cycle in the combin...
Method for Predicting and Optimizing System Parameters for Electrospinning System
Wincheski, Russell A. (Inventor)
2011-01-01
An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
Adaptive neuro-fuzzy estimation of optimal lens system parameters
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
Optimization of Experimental Model Parameter Identification for Energy Storage Systems
Directory of Open Access Journals (Sweden)
Rosario Morello
2013-09-01
Full Text Available The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.
Directory of Open Access Journals (Sweden)
Rajiv Kumar
2017-07-01
Full Text Available In the present work, a recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching–learning-based optimization (TLBO. The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.
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.
The Study of the Optimal Parameter Settings in a Hospital Supply Chain System in Taiwan
Directory of Open Access Journals (Sweden)
Hung-Chang Liao
2014-01-01
Full Text Available This study proposed the optimal parameter settings for the hospital supply chain system (HSCS when either the total system cost (TSC or patient safety level (PSL (or both simultaneously was considered as the measure of the HSCS’s performance. Four parameters were considered in the HSCS: safety stock, maximum inventory level, transportation capacity, and the reliability of the HSCS. A full-factor experimental design was used to simulate an HSCS for the purpose of collecting data. The response surface method (RSM was used to construct the regression model, and a genetic algorithm (GA was applied to obtain the optimal parameter settings for the HSCS. The results show that the best method of obtaining the optimal parameter settings for the HSCS is the simultaneous consideration of both the TSC and the PSL to measure performance. Also, the results of sensitivity analysis based on the optimal parameter settings were used to derive adjustable strategies for the decision-makers.
Use of multilevel modeling for determining optimal parameters of heat supply systems
Stennikov, V. A.; Barakhtenko, E. A.; Sokolov, D. V.
2017-07-01
The problem of finding optimal parameters of a heat-supply system (HSS) is in ensuring the required throughput capacity of a heat network by determining pipeline diameters and characteristics and location of pumping stations. Effective methods for solving this problem, i.e., the method of stepwise optimization based on the concept of dynamic programming and the method of multicircuit optimization, were proposed in the context of the hydraulic circuit theory developed at Melentiev Energy Systems Institute (Siberian Branch, Russian Academy of Sciences). These methods enable us to determine optimal parameters of various types of piping systems due to flexible adaptability of the calculation procedure to intricate nonlinear mathematical models describing features of used equipment items and methods of their construction and operation. The new and most significant results achieved in developing methodological support and software for finding optimal parameters of complex heat supply systems are presented: a new procedure for solving the problem based on multilevel decomposition of a heat network model that makes it possible to proceed from the initial problem to a set of interrelated, less cumbersome subproblems with reduced dimensionality; a new algorithm implementing the method of multicircuit optimization and focused on the calculation of a hierarchical model of a heat supply system; the SOSNA software system for determining optimum parameters of intricate heat-supply systems and implementing the developed methodological foundation. The proposed procedure and algorithm enable us to solve engineering problems of finding the optimal parameters of multicircuit heat supply systems having large (real) dimensionality, and are applied in solving urgent problems related to the optimal development and reconstruction of these systems. The developed methodological foundation and software can be used for designing heat supply systems in the Central and the Admiralty regions in
Honório, Leonardo M; Costa, Exuperry Barros; Oliveira, Edimar J; Fernandes, Daniel de Almeida; Moreira, Antonio Paulo G M
2018-04-13
This work presents a novel methodology for Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation of constrained nonlinear systems. It is proposed that the evaluation of each signal must also account for the difference between real and estimated system parameters. However, this metric is not directly obtained once the real parameter values are not known. The alternative presented here is to adopt the hypothesis that, if a system can be approximated by a white box model, this model can be used as a benchmark to indicate the impact of a signal over the parametric estimation. In this way, the proposed method uses a dual layer optimization methodology: (i) Inner Level; For a given excitation signal a nonlinear optimization method searches for the optimal set of parameters that minimizes the error between the outputs of the optimized and benchmark models. (ii) At the outer level, a metaheuristic optimization method is responsible for constructing the best excitation signal, considering the fitness coming from the inner level, the quadratic difference between its parameters and the cost related to the time and space required to execute the experiment. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Cho, Ming-Yuan; Hoang, Thi Thom
2017-01-01
Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary seque...
Optimization of AVR Parameters of a Multi-machine Power System ...
African Journals Online (AJOL)
In this paper, a method for optimizing the parameters of Automatic Voltage Regulation (AVR) system installed on the generators of a multi-machine power system using Artificial Intelligence (AI) techniques is presented. Each AVR system is equipped with a PID (Proportional, Integral and Derivative) controller and a Power ...
Remondo Bueno, D.; Srinivasan, R.; Nicola, V.F.; van Etten, Wim; Tattje, H.E.P.
2000-01-01
We present new adaptive importance sampling techniques based on stochastic Newton recursions. Their applicability to the performance evaluation of communication systems is studied. Besides bit-error rate (BER) estimation, the techniques are used for system parameter optimization. Two system models
Optimization of AVR Parameters of a Multi-machine Power System ...
African Journals Online (AJOL)
user1
In this paper, a method for optimizing the parameters of Automatic Voltage Regulation (AVR) system installed on the generators of a multi-machine power system using Artificial Intelligence. (AI) techniques is presented. Each AVR system is equipped with a PID (Proportional, Integral and Derivative) controller and a Power ...
Infrared Drying Parameter Optimization
Jackson, Matthew R.
In recent years, much research has been done to explore direct printing methods, such as screen and inkjet printing, as alternatives to the traditional lithographic process. The primary motivation is reduction of the material costs associated with producing common electronic devices. Much of this research has focused on developing inkjet or screen paste formulations that can be printed on a variety of substrates, and which have similar conductivity performance to the materials currently used in the manufacturing of circuit boards and other electronic devices. Very little research has been done to develop a process that would use direct printing methods to manufacture electronic devices in high volumes. This study focuses on developing and optimizing a drying process for conductive copper ink in a high volume manufacturing setting. Using an infrared (IR) dryer, it was determined that conductive copper prints could be dried in seconds or minutes as opposed to tens of minutes or hours that it would take with other drying devices, such as a vacuum oven. In addition, this study also identifies significant parameters that can affect the conductivity of IR dried prints. Using designed experiments and statistical analysis; the dryer parameters were optimized to produce the best conductivity performance for a specific ink formulation and substrate combination. It was determined that for an ethylene glycol, butanol, 1-methoxy 2- propanol ink formulation printed on Kapton, the optimal drying parameters consisted of a dryer height of 4 inches, a temperature setting between 190 - 200°C, and a dry time of 50-65 seconds depending on the printed film thickness as determined by the number of print passes. It is important to note that these parameters are optimized specifically for the ink formulation and substrate used in this study. There is still much research that needs to be done into optimizing the IR dryer for different ink substrate combinations, as well as developing a
Directory of Open Access Journals (Sweden)
Yu Huang
Full Text Available Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.
International Nuclear Information System (INIS)
Ahmadi, Mohamadreza; Mojallali, Hamed
2012-01-01
Highlights: ► A new meta-heuristic optimization algorithm. ► Integration of invasive weed optimization and chaotic search methods. ► A novel parameter identification scheme for chaotic systems. - Abstract: This paper introduces a novel hybrid optimization algorithm by taking advantage of the stochastic properties of chaotic search and the invasive weed optimization (IWO) method. In order to deal with the weaknesses associated with the conventional method, the proposed chaotic invasive weed optimization (CIWO) algorithm is presented which incorporates the capabilities of chaotic search methods. The functionality of the proposed optimization algorithm is investigated through several benchmark multi-dimensional functions. Furthermore, an identification technique for chaotic systems based on the CIWO algorithm is outlined and validated by several examples. The results established upon the proposed scheme are also supplemented which demonstrate superior performance with respect to other conventional methods.
Factorization and the synthesis of optimal feedback gains for distributed parameter systems
Milman, Mark H.; Scheid, Robert E.
1990-01-01
An approach based on Volterra factorization leads to a new methodology for the analysis and synthesis of the optimal feedback gain in the finite-time linear quadratic control problem for distributed parameter systems. The approach circumvents the need for solving and analyzing Riccati equations and provides a more transparent connection between the system dynamics and the optimal gain. The general results are further extended and specialized for the case where the underlying state is characterized by autonomous differential-delay dynamics. Numerical examples are given to illustrate the second-order convergence rate that is derived for an approximation scheme for the optimal feedback gain in the differential-delay problem.
Parameter optimization method for longitudinal vibration absorber of ship shaft system
Directory of Open Access Journals (Sweden)
LIU Jinlin
2017-05-01
Full Text Available The longitudinal vibration of the ship shaft system is the one of the most important factors of hull stern vibration, and it can be effectively minimized by installing a longitudinal vibration absorber. In this way, the vibration and noise of ships can be brought under control. However, the parameters of longitudinal vibration absorbers have a great influence on the vibration characteristics of the shaft system. As such, a certain shafting testing platform was studied as the object on which a finite model was built, and the relationship between longitudinal stiffness and longitudinal vibration in the shaft system was analyzed in a straight alignment state. Furthermore, a longitudinal damping model of the shaft system was built in which the parameters of the vibration absorber were non-dimensionalized, the weight of the vibration absorber was set as a constant, and an optimizing algorithm was used to calculate the optimized stiffness and damping coefficient of the vibration absorber. Finally, the longitudinal vibration frequency response of the shafting testing platform before and after optimizing the parameters of the longitudinal vibration absorber were compared, and the results indicated that the longitudinal vibration of the shafting testing platform was decreased effectively, which suggests that it could provide a theoretical foundation for the parameter optimization of longitudinal vibration absorbers.
Optimization of Temperature Schedule Parameters on Heat Supply in Power-and-Heat Supply Systems
Directory of Open Access Journals (Sweden)
V. A. Sednin
2009-01-01
Full Text Available The paper considers problems concerning optimization of a temperature schedule in the district heating systems with steam-turbine thermal power stations having average initial steam parameters. It has been shown in the paper that upkeeping of an optimum network water temperature permits to increase an energy efficiency of heat supply due to additional systematic saving of fuel.
Directory of Open Access Journals (Sweden)
Guozhen Hu
2017-12-01
Full Text Available A loosely coupled inductive power transfer (IPT system for industrial track applications has been researched in this paper. The IPT converter using primary Inductor-Capacitor-Inductor (LCL network and secondary parallel-compensations is analyzed combined coil design for optimal operating efficiency. Accurate mathematical analytical model and expressions of self-inductance and mutual inductance are proposed to achieve coil parameters. Furthermore, the optimization process is performed combined with the proposed resonant compensations and coil parameters. The results are evaluated and discussed using finite element analysis (FEA. Finally, an experimental prototype is constructed to verify the proposed approach and the experimental results show that the optimization can be better applied to industrial track distributed IPT system.
Model Predictive Optimal Control of a Time-Delay Distributed-Parameter Systems
Nguyen, Nhan
2006-01-01
This paper presents an optimal control method for a class of distributed-parameter systems governed by first order, quasilinear hyperbolic partial differential equations that arise in many physical systems. Such systems are characterized by time delays since information is transported from one state to another by wave propagation. A general closed-loop hyperbolic transport model is controlled by a boundary control embedded in a periodic boundary condition. The boundary control is subject to a nonlinear differential equation constraint that models actuator dynamics of the system. The hyperbolic equation is thus coupled with the ordinary differential equation via the boundary condition. Optimality of this coupled system is investigated using variational principles to seek an adjoint formulation of the optimal control problem. The results are then applied to implement a model predictive control design for a wind tunnel to eliminate a transport delay effect that causes a poor Mach number regulation.
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.
International Nuclear Information System (INIS)
Frolov, A.M.
1986-01-01
The problem of exact variational calculations of few-particle systems in the exponential basis of the relative coordinates using nonlinear parameters is studied. The techniques of stepwise optimization and global chaos of nonlinear parameters are used to calculate the S and P states of homonuclear muonic molecules with an error of no more than +0.001 eV. The global-chaos technique also has proved to be successful in the case of the nuclear systems 3 H and 3 He
Optimal allocation of sensors for state estimation of distributed parameter systems
International Nuclear Information System (INIS)
Sunahara, Yoshifumi; Ohsumi, Akira; Mogami, Yoshio.
1978-01-01
The purpose of this paper is to present a method for finding the optimal allocation of sensors for state estimation of linear distributed parameter systems. This method is based on the criterion that the error covariance associated with the state estimate becomes minimal with respect to the allocation of the sensors. A theorem is established, giving the sufficient condition for optimizing the allocation of sensors to make minimal the error covariance approximated by a modal expansion. The remainder of this paper is devoted to illustrate important phases of the general theory of the optimal measurement allocation problem. To do this, several examples are demonstrated, including extensive discussions on the mutual relation between the optimal allocation and the dynamics of sensors. (author)
Optimizing parameters of a technical system using quality function deployment method
Baczkowicz, M.; Gwiazda, A.
2015-11-01
The article shows the practical use of Quality Function Deployment (QFD) on the example of a mechanized mining support. Firstly it gives a short description of this method and shows how the designing process, from the constructor point of view, looks like. The proposed method allows optimizing construction parameters and comparing them as well as adapting to customer requirements. QFD helps to determine the full set of crucial construction parameters and then their importance and difficulty of their execution. Secondly it shows chosen technical system and presents its construction with figures of the existing and future optimized model. The construction parameters were selected from the designer point of view. The method helps to specify a complete set of construction parameters, from the point of view, of the designed technical system and customer requirements. The QFD matrix can be adjusted depending on designing needs and not every part of it has to be considered. Designers can choose which parts are the most important. Due to this QFD can be a very flexible tool. The most important is to define relationships occurring between parameters and that part cannot be eliminated from the analysis.
State and parameter estimation in nonlinear systems as an optimal tracking problem
International Nuclear Information System (INIS)
Creveling, Daniel R.; Gill, Philip E.; Abarbanel, Henry D.I.
2008-01-01
In verifying and validating models of nonlinear processes it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, we present a framework for connecting a data signal with a model in a way that minimizes the required coupling yet allows the estimation of unknown parameters in the model. The need to evaluate unknown parameters in models of nonlinear physical, biophysical, and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. Our approach builds on existing work that uses synchronization as a tool for parameter estimation. We address some of the critical issues in that work and provide a practical framework for finding an accurate solution. In particular, we show the equivalence of this problem to that of tracking within an optimal control framework. This equivalence allows the application of powerful numerical methods that provide robust practical tools for model development and validation
Cho, Ming-Yuan; Hoang, Thi Thom
2017-01-01
Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.
Directory of Open Access Journals (Sweden)
Ming-Yuan Cho
2017-01-01
Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.
Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System.
Lu, Baofeng; Wang, Qiuying; Yu, Chunmei; Gao, Wei
2015-06-25
Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.
Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System
Directory of Open Access Journals (Sweden)
Baofeng Lu
2015-06-01
Full Text Available Two different coarse alignment algorithms for Fiber Optic Gyro (FOG Inertial Navigation System (INS based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.
Helicopter TEM parameters analysis and system optimization based on time constant
Xiao, Pan; Wu, Xin; Shi, Zongyang; Li, Jutao; Liu, Lihua; Fang, Guangyou
2018-03-01
Helicopter transient electromagnetic (TEM) method is a kind of common geophysical prospecting method, widely used in mineral detection, underground water exploration and environment investigation. In order to develop an efficient helicopter TEM system, it is necessary to analyze and optimize the system parameters. In this paper, a simple and quantitative method is proposed to analyze the system parameters, such as waveform, power, base frequency, measured field and sampling time. A wire loop model is used to define a comprehensive 'time constant domain' that shows a range of time constant, analogous to a range of conductance, after which the characteristics of the system parameters in this domain is obtained. It is found that the distortion caused by the transmitting base frequency is less than 5% when the ratio of the transmitting period to the target time constant is greater than 6. When the sampling time window is less than the target time constant, the distortion caused by the sampling time window is less than 5%. According to this method, a helicopter TEM system, called CASHTEM, is designed, and flight test has been carried out in the known mining area. The test results show that the system has good detection performance, verifying the effectiveness of the method.
Directory of Open Access Journals (Sweden)
Rongxin Tang
2015-10-01
Full Text Available Mobile sensor networks are an important part of modern robotics systems and are widely used in robotics applications. Therefore, sensor deployment is a key issue in current robotics systems research. Since it is one of the most popular deployment methods, in recent years the virtual force algorithm has been studied in detail by many scientists. In this paper, we focus on the virtual force algorithm and present a corresponding parameter investigation for mobile sensor deployment. We introduce an optimized virtual force algorithm based on the exchange force, in which a new shielding rule grounded in Delaunay triangulation is adopted. The algorithm employs a new performance metric called ‘pair-correlation diversion', designed to evaluate the uniformity and topology of the sensor distribution. We also discuss the implementation of the algorithm's computation and analyse the influence of experimental parameters on the algorithm. Our results indicate that the area ratio, φs, and the exchange force constant, G, influence the final performance of the sensor deployment in terms of the coverage rate, the convergence time and topology uniformity. Using simulations, we were able to verify the effectiveness of our algorithm and we obtained an optimal region for the (φs, G-parameter space which, in the future, could be utilized as an aid for experiments in robotic sensor deployment.
A self-adaptive parameter optimization algorithm in a real-time parallel image processing system.
Li, Ge; Zhang, Xuehe; Zhao, Jie; Zhang, Hongli; Ye, Jianwei; Zhang, Weizhe
2013-01-01
Aiming at the stalemate that precision, speed, robustness, and other parameters constrain each other in the parallel processed vision servo system, this paper proposed an adaptive load capacity balance strategy on the servo parameters optimization algorithm (ALBPO) to improve the computing precision and to achieve high detection ratio while not reducing the servo circle. We use load capacity functions (LC) to estimate the load for each processor and then make continuous self-adaptation towards a balanced status based on the fluctuated LC results; meanwhile, we pick up a proper set of target detection and location parameters according to the results of LC. Compared with current load balance algorithm, the algorithm proposed in this paper is proceeded under an unknown informed status about the maximum load and the current load of the processors, which means it has great extensibility. Simulation results showed that the ALBPO algorithm has great merits on load balance performance, realizing the optimization of QoS for each processor, fulfilling the balance requirements of servo circle, precision, and robustness of the parallel processed vision servo system.
Sue-Ann, Goh; Ponnambalam, S. G.
This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.
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.
Optimal Tracking Performance of MIMO Discrete-Time Systems with Network Parameters
Directory of Open Access Journals (Sweden)
Chao-Yang Chen
2016-01-01
Full Text Available The optimal regulation properties of multi-input and multioutput (MIMO discrete-time networked control systems (NCSs, over additive white Gaussian noise (AWGN fading channels, based on state space representation, are investigated. The average performance index is introduced. Moreover, the regulation performance is measured by the control energy and the error energy of the system, and fundamental limitations are obtained. Two kinds of network parameters, fading and the additive white Gaussian noise, are considered. The best attainable regulation performance limitations can be obtained by the limiting steady state solution of the corresponding algebraic Riccati equation (ARE. The simulation results are given to demonstrate the main results of the theoretical development.
International Nuclear Information System (INIS)
Berrazouane, S.; Mohammedi, K.
2014-01-01
Highlights: • Optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. • Comparison between optimized fuzzy logic controller based on cuckoo search and swarm intelligent. • Loss of power supply probability and levelized energy cost are introduced. - Abstract: This paper presents the development of an optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. The FLC inputs are batteries state of charge (SOC) and net power flow, FLC outputs are the power rate of batteries, photovoltaic and diesel generator. Data for weekly solar irradiation, ambient temperature and load profile are used to tune the proposed controller by using cuckoo search algorithm. The optimized FLC is able to minimize loss of power supply probability (LPSP), excess energy (EE) and levelized energy cost (LEC). Moreover, the results of CS optimization are better than of particle swarm optimization PSO for fuzzy system controller
Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm
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V. Rajinikanth
2012-01-01
Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.
Application of particle swarm optimization to parameter search in dynamical systems
Matsushita, Haruna; Saito, Toshimichi
This paper proposes an application of the particle swarm optimization (PSO) to analysis of switched dynamical systems (SDS). This is the first application of PSO to bifurcation analysis. We consider the application to an example of the SDS which relates to a simplified model of photovoltaic systems such that the input is a single solar cell and is converted to the output via a boost converter. Our SDS includes a piecewise linear current-controlled voltage source that is a simplified model of the solar cell and the switching rule is a variant of peak-current-controlled switching. We derive two equations that give period-doubling bifurcation set and the maximum power point (MPP) for the parameter: they are objective of the analysis. The two equations are transformed into an multi objective problem (MOP) described by the hybrid fitness function consisting of two functions evaluating the validity of parameters and criteria. The proposed method permits increase (deteriorate) of some component below the criterion and the increase can help to exclude the bad component. This criteria effect helps an improvement of trade-off problems in existing MOP solvers. Furthermore, by using the piecewise exact solution and return map for the simulation, the MOP is described exactly and the PSO can find the precise (approximate) solution. From simulation results, we confirm that the PSO for the MOP can easily find the solution parameters although a standard numerical calculation needs huge calculation amount. The efficiency of the proposed algorithm is confirmed by measuring in terms of accuracy, computation amount and robustness.
Parameters optimization in a fission-fusion system with a mirror machine based neutron source
Yurov, D. V.; Anikeev, A. V.; Bagryansky, P. A.; Brednikhin, S. A.; Frolov, S. A.; Lezhnin, S. I.; Prikhodko, V. V.
2012-06-01
Long-lived fission products utilization is a problem of high importance for the modern nuclear reactor technology. BINP jointly with NSI RAS develops a conceptual design of a hybrid sub-critical minor actinides burner with a neutron source based on the gas dynamic mirror machine (GDT) to resolve the stated task. A number of modelling tools was created to calculate the main parameters of the device. First of the codes, GENESYS, is a zero-dimensional code, designed for plasma dynamics numerical investigation in a GDT-based neutron source. The code contains a Monte-Carlo module for the determination of linear neutron emission intensity along the machine axis. Fuel blanket characteristics calculation was implemented by means of a static Monte-Carlo code NMC. Subcritical core, which has been previously analyzed by OECD-NEA, was used as a template for the fuel blanket of the modelled device. This article represents the codes used and recent results of the described system parameters optimization. Particularly, optimum emission zone length of the source and core multiplicity dependence on buffer zone thickness were defined.
G.Venkatkumar; S.Mohanamurugan; R.Vijay
2014-01-01
In this paper, a suitable approach for the optimization of spring parameters, namely spring wire diameter, spring index and number of active coils of two wheeler rear suspension systems with multiple responses such as strain energy and weight based on orthogonal arrays with grey relational analysis is used. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters...
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-25
This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.
Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems
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Erdem Demircioglu
2015-01-01
Full Text Available This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN. To achieve the best ANN performance, Particle Swarm Optimization (PSO and Differential Evolution (DE are applied with ANN’s conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.
Torres-Pomales, Wilfredo
2015-01-01
This report documents a case study on the application of Reliability Engineering techniques to achieve an optimal balance between performance and robustness by tuning the functional parameters of a complex non-linear control system. For complex systems with intricate and non-linear patterns of interaction between system components, analytical derivation of a mathematical model of system performance and robustness in terms of functional parameters may not be feasible or cost-effective. The demonstrated approach is simple, structured, effective, repeatable, and cost and time efficient. This general approach is suitable for a wide range of systems.
Control and optimization system
Xinsheng, Lou
2013-02-12
A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan
2018-03-01
The conventional engineering optimization problems considering uncertainties are based on the probabilistic model. However, the probabilistic model may be unavailable because of the lack of sufficient objective information to construct the precise probability distribution of uncertainties. This paper proposes a possibility-based robust design optimization (PBRDO) framework for the uncertain structural-acoustic system based on the fuzzy set model, which can be constructed by expert opinions. The objective of robust design is to optimize the expectation and variability of system performance with respect to uncertainties simultaneously. In the proposed PBRDO, the entropy of the fuzzy system response is used as the variability index; the weighted sum of the entropy and expectation of the fuzzy response is used as the objective function, and the constraints are established in the possibility context. The computations for the constraints and objective function of PBRDO are a triple-loop and a double-loop nested problem, respectively, whose computational costs are considerable. To improve the computational efficiency, the target performance approach is introduced to transform the calculation of the constraints into a double-loop nested problem. To further improve the computational efficiency, a Chebyshev fuzzy method (CFM) based on the Chebyshev polynomials is proposed to estimate the objective function, and the Chebyshev interval method (CIM) is introduced to estimate the constraints, thereby the optimization problem is transformed into a single-loop one. Numerical results on a shell structural-acoustic system verify the effectiveness and feasibility of the proposed methods.
Fan, Shuwei; Zhang, Yan; Shen, Yuting
2010-06-01
DPL coupling system was researched in this paper. First, the mathematic model of 3D and 2D light transmission in hollow duct was analyzed and compared. Then the 3D simulation software for all rays of the coupling system -lens duct was developed. The influence of various structural parameters of the hollow lens duct to the energy and the beam distribution were discussed with the help of developed software. The structural parameters such as the duct length, the lens radius, the size of the input and output ends were researched and were optimized to get higher efficiency and better beam distribution. Finally, the energy conversion efficiency and the beam spatial distribution of before and after optimization were compared. The results showed that the efficiency and the distribution of energy were well improved after the optimization.
International Nuclear Information System (INIS)
Kotarski, W.; Kowalewski, A.
1982-03-01
In this paper we consider an optimal control problem for a system described by a linear partial differential equation of the parabolic type with Dirichlet's boundary condition. We impose some constraints on the control. The performance functional has the integral form. The control time T is fixed. The initial condition is not given by a known function but belongs to a certain set (incomplete information about the initial state). The problem formulated in this paper describes the process of optimal heating, of which we do not have exact information about the initial temperature on the heated object. We present an example in which the set of admissible controls and one of initial conditions are given by means of the norm constraints too. The application of the well-known projective gradient method in the Hilbert space allows us to obtain the numerical solution for our optimization problem. (author)
Drake, R. L.; Duvoisin, P. F.; Asthana, A.; Mather, T. W.
1971-01-01
High speed automated identification and design of dynamic systems, both linear and nonlinear, are discussed. Special emphasis is placed on developing hardware and techniques which are applicable to practical problems. The basic modeling experiment and new results are described. Using the improvements developed successful identification of several systems, including a physical example as well as simulated systems, was obtained. The advantages of parameter signature analysis over signal signature analysis in go-no go testing of operational systems were demonstrated. The feasibility of using these ideas in failure mode prediction in operating systems was also investigated. An improved digital controlled nonlinear function generator was developed, de-bugged, and completely documented.
Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System
Lu, Baofeng; Wang, Qiuying; Yu, Chunmei; Gao, Wei
2015-01-01
Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parame...
International Nuclear Information System (INIS)
Frolov, A.M.
1986-01-01
Exact variational calculations are treated for few-particle systems in the exponential basis of relative coordinates using nonlinear parameters. The methods of step-by-step optimization and global chaos of nonlinear parameters are applied to calculate the S and P states of ppμ, ddμ, ttμ homonuclear mesomolecules within the error ≤±0.001 eV. The global chaos method turned out to be well applicable to nuclear 3 H and 3 He systems
Alghoul, M. A.; Ali, Amer; Kannanaikal, F. V.; Amin, N.; Sopian, K.
2017-11-01
PV power systems have been commercially available and widely used for decades. The performance of a reliable PV system that fulfils the expectations requires correct input data and careful design. Inaccurate input data of the techno-economic feasibility would affect the size, cost aspects, stability and performance of PV power system on the long run. The annual capacity shortage is one of the main input data that should be selected with careful attention. The aim of this study is to reveal the effect of different annual capacity shortages on the techno-economic feasibility parameters and determining the optimal value for Baghdad city location using HOMER simulation tool. Six values of annual capacity shortage percentages (0%, 1%, 2%, 3%, 4%, and 5%), and wide daily load profile range (10 kWh - 100 kWh) are implemented. The optimal annual capacity shortage is the value that always "wins" when each techno-economic feasibility parameter is at its optimal/ reasonable criteria. The results showed that the optimal annual capacity shortage that reduces significantly the cost of PV power system while keeping the PV system with reasonable technical feasibility is 3%. This capacity shortage value can be carried as a reference value in future works for Baghdad city location. Using this approach of analysis at other locations, annual capacity shortage can be always offered as a reference value for those locations.
Optimizing the Air Dissolution Parameters in an Unpacked Dissolved Air Flotation System
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Adam Dassey
2011-12-01
Full Text Available Due to the various parameters that influence air solubility and microbubble production in dissolved air flotation (DAF, a multitude of values that cover a large range for these parameters are suggested for field systems. An unpacked saturator and an air quantification unit were designed to specify the effects of power, pressure, temperature, hydraulic retention time, and air flow on the DAF performance. It was determined that a pressure of 621 kPa, hydraulic retention time of 18.2 min, and air flow of 8.5 L/h would be the best controlled parameters for maximum efficiency in this unit. A temperature of 7 °C showed the greatest microbubble production, but temperature control would not be expected in actual application. The maximum microbubble flow from the designed system produced 30 mL of air (±1.5 per L of water under these conditions with immediate startup. The maximum theoretical dissolved air volume of 107 mL (±6 was achieved at a retention time of 2 h and a pressure of 621 kPa. To isolate and have better control over the various DAF operational parameters, the DAF unit was operated without the unsaturated flow stream. This mode of operation led to the formation of large bubbles at peak bubble production rates. In a real-world application, the large bubble formation will be avoided by mixing with raw unsaturated stream and by altering the location of dissolved air output flow.
International Nuclear Information System (INIS)
Banerjee, Amit; Abu-Mahfouz, Issam
2014-01-01
The use of evolutionary algorithms has been popular in recent years for solving the inverse problem of identifying system parameters given the chaotic response of a dynamical system. The inverse problem is reformulated as a minimization problem and population-based optimizers such as evolutionary algorithms have been shown to be efficient solvers of the minimization problem. However, to the best of our knowledge, there has been no published work that evaluates the efficacy of using the two most popular evolutionary techniques – particle swarm optimization and differential evolution algorithm, on a wide range of parameter estimation problems. In this paper, the two methods along with their variants (for a total of seven algorithms) are applied to fifteen different parameter estimation problems of varying degrees of complexity. Estimation results are analyzed using nonparametric statistical methods to identify if an algorithm is statistically superior to others over the class of problems analyzed. Results based on parameter estimation quality suggest that there are significant differences between the algorithms with the newer, more sophisticated algorithms performing better than their canonical versions. More importantly, significant differences were also found among variants of the particle swarm optimizer and the best performing differential evolution algorithm
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)
Directory of Open Access Journals (Sweden)
A.О. Khorolskiy
2017-12-01
Full Text Available The article deals with solving the scientific problem of select mining equipment for selection longwall faces of mining venture. The main goal of the paper is to study technology of coal extraction of mining venture. The paper proposes a new approach to solve a problem of mining equipment selection for longwall faces of mining venture. The article describes new method for selection of mining equipment based on theory graph. Special attention is given to technological aspects; they are lenght of longwall faces, depth of coal stratum. Predictions obtained for daily production of mining equipment are compared with design outputs. Conclusions regarding the main reason of instability of longwall faces workings are made. It is found that to be able to use standard algorithms find the shortest path between vertices, you must perform matrix description of the constructed graphs that illustrate the structure of the interaction of different types of control equipment. Using classical optimization of method of discrete mathematics and algorithms for finding the shortest path between two vertices of network models obtained from the formalization of graphs with maximum results of specific types of equipment production chains, solved the problem of rational choice cleaning equipment for the new site with the cost parameters of the mining equipment and cost of mining coal. The authors developed effective and appropriate variants for the mine development for different coal deposits of mining venture.
One shot methods for optimal control of distributed parameter systems 1: Finite dimensional control
Taasan, Shlomo
1991-01-01
The efficient numerical treatment of optimal control problems governed by elliptic partial differential equations (PDEs) and systems of elliptic PDEs, where the control is finite dimensional is discussed. Distributed control as well as boundary control cases are discussed. The main characteristic of the new methods is that they are designed to solve the full optimization problem directly, rather than accelerating a descent method by an efficient multigrid solver for the equations involved. The methods use the adjoint state in order to achieve efficient smoother and a robust coarsening strategy. The main idea is the treatment of the control variables on appropriate scales, i.e., control variables that correspond to smooth functions are solved for on coarse grids depending on the smoothness of these functions. Solution of the control problems is achieved with the cost of solving the constraint equations about two to three times (by a multigrid solver). Numerical examples demonstrate the effectiveness of the method proposed in distributed control case, pointwise control and boundary control problems.
D'Agnese, F. A.; Faunt, C.C.; Hill, M.C.; Turner, A.K.
1996-01-01
A three-layer Death Valley regional groundwater flow model was constructed to evaluate potential regional groundwater flow paths in the vicinity of Yucca Mountain, Nevada. Geoscientific information systems were used to characterize the complex surface and subsurface hydrogeological conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. The high contrasts and abrupt contacts of the different hydrogeological units in the subsurface make zonation the logical choice for representing the hydraulic conductivity distribution. Hydraulic head and spring flow data were used to test different conceptual models by using nonlinear regression to determine parameter values that currently provide the best match between the measured and simulated heads and flows.
Parameter Optimization and Operating Strategy of a TEG System for Railway Vehicles
Heghmanns, A.; Wilbrecht, S.; Beitelschmidt, M.; Geradts, K.
2016-03-01
A thermoelectric generator (TEG) system demonstrator for diesel electric locomotives with the objective of reducing the mechanical load on the thermoelectric modules (TEM) is developed and constructed to validate a one-dimensional thermo-fluid flow simulation model. The model is in good agreement with the measurements and basis for the optimization of the TEG's geometry by a genetic multi objective algorithm. The best solution has a maximum power output of approx. 2.7 kW and does not exceed the maximum back pressure of the diesel engine nor the maximum TEM hot side temperature. To maximize the reduction of the fuel consumption, an operating strategy regarding the system power output for the TEG system is developed. Finally, the potential consumption reduction in passenger and freight traffic operating modes is estimated under realistic driving conditions by means of a power train and lateral dynamics model. The fuel savings are between 0.5% and 0.7%, depending on the driving style.
D'Agnese, F. A.; Faunt, C.C.; Hill, M.C.; Turner, A.K.
1999-01-01
A regional-scale, steady-state, saturated-zone ground-water flow model was constructed to evaluate potential regional ground-water flow in the vicinity of Yucca Mountain, Nevada. The model was limited to three layers in an effort to evaluate the characteristics governing large-scale subsurface flow. Geoscientific information systems (GSIS) were used to characterize the complex surface and subsurface hydrogeologic conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. Subsurface properties in this system vary dramatically, producing high contrasts and abrupt contacts. This characteristic, combined with the large scale of the model, make zonation the logical choice for representing the hydraulic-conductivity distribution. Different conceptual models were evaluated using sensitivity analysis and were tested by using nonlinear regression to determine parameter values that are optimal, in that they provide the best match between the measured and simulated heads and flows. The different conceptual models were judged based both on the fit achieved to measured heads and spring flows, and the plausibility of the optimal parameter values. One of the conceptual models considered appears to represent the system most realistically. Any apparent model error is probably caused by the coarse vertical and horizontal discretization.A regional-scale, steady-state, saturated-zone ground-water flow model was constructed to evaluate potential regional ground-water flow in the vicinity of Yucca Mountain, Nevada. The model was limited to three layers in an effort to evaluate the characteristics governing large-scale subsurface flow. Geoscientific information systems (GSIS) were used to characterize the complex surface and subsurface hydrogeologic conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. Subsurface properties in this system vary dramatically, producing
Khavekar, Rajendra; Vasudevan, Hari, Dr.; Modi, Bhavik
2017-08-01
Two well-known Design of Experiments (DoE) methodologies, such as Taguchi Methods (TM) and Shainin Systems (SS) are compared and analyzed in this study through their implementation in a plastic injection molding unit. Experiments were performed at a perfume bottle cap manufacturing company (made by acrylic material) using TM and SS to find out the root cause of defects and to optimize the process parameters for minimum rejection. Experiments obtained the rejection rate to be 8.57% from 40% (appx.) during trial runs, which is quiet low, representing successful implementation of these DoE methods. The comparison showed that both methodologies gave same set of variables as critical for defect reduction, but with change in their significance order. Also, Taguchi methods require more number of experiments and consume more time compared to the Shainin System. Shainin system is less complicated and is easy to implement, whereas Taguchi methods is statistically more reliable for optimization of process parameters. Finally, experimentations implied that DoE methods are strong and reliable in implementation, as organizations attempt to improve the quality through optimization.
Chen, Zhuoqi; Chen, Jing M.; Zhang, Shupeng; Zheng, Xiaogu; Ju, Weiming; Mo, Gang; Lu, Xiaoliang
2017-12-01
The Global Carbon Assimilation System that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C (Vmax25), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at middle-high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.
Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing
2018-02-01
Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.
International Nuclear Information System (INIS)
Ma, Guoyuan; Li, Xianguo
2007-01-01
The heat pump system with economizer coupled with scroll compressor can extend effectively its operating ranges and provide a technological method to enable the heat pump to run steadily and efficiently in severe weather conditions. The intermediate pressure, namely the working pressure of the refrigerant in the economizer, is an essential design parameter and affects crucially the performances of the heat pump system. According to the exergetic model setup for the heat pump system based on the second law of thermodynamics, the influences of the intermediate pressure on the performances are comprehensively analyzed using experimental data of the heat pump prototype. It is found that the optimal relative intermediate pressure (RIP) is between 1.1 and 1.3
Directory of Open Access Journals (Sweden)
Luquan Ren
2017-03-01
Full Text Available Recently, with a broadening range of available materials and alteration of feeding processes, several extrusion-based 3D printing processes for metal materials have been developed. An emerging process is applicable for the fabrication of metal parts into electronics and composites. In this paper, some critical parameters of extrusion-based 3D printing processes were optimized by a series of experiments with a melting extrusion printer. The raw materials were copper powder and a thermoplastic organic binder system and the system included paraffin wax, low density polyethylene, and stearic acid (PW–LDPE–SA. The homogeneity and rheological behaviour of the raw materials, the strength of the green samples, and the hardness of the sintered samples were investigated. Moreover, the printing and sintering parameters were optimized with an orthogonal design method. The influence factors in regard to the ultimate tensile strength of the green samples can be described as follows: infill degree > raster angle > layer thickness. As for the sintering process, the major factor on hardness is sintering temperature, followed by holding time and heating rate. The highest hardness of the sintered samples was very close to the average hardness of commercially pure copper material. Generally, the extrusion-based printing process for producing metal materials is a promising strategy because it has some advantages over traditional approaches for cost, efficiency, and simplicity.
Ren, Luquan; Zhou, Xueli; Song, Zhengyi; Zhao, Che; Liu, Qingping; Xue, Jingze; Li, Xiujuan
2017-03-16
Recently, with a broadening range of available materials and alteration of feeding processes, several extrusion-based 3D printing processes for metal materials have been developed. An emerging process is applicable for the fabrication of metal parts into electronics and composites. In this paper, some critical parameters of extrusion-based 3D printing processes were optimized by a series of experiments with a melting extrusion printer. The raw materials were copper powder and a thermoplastic organic binder system and the system included paraffin wax, low density polyethylene, and stearic acid (PW-LDPE-SA). The homogeneity and rheological behaviour of the raw materials, the strength of the green samples, and the hardness of the sintered samples were investigated. Moreover, the printing and sintering parameters were optimized with an orthogonal design method. The influence factors in regard to the ultimate tensile strength of the green samples can be described as follows: infill degree > raster angle > layer thickness. As for the sintering process, the major factor on hardness is sintering temperature, followed by holding time and heating rate. The highest hardness of the sintered samples was very close to the average hardness of commercially pure copper material. Generally, the extrusion-based printing process for producing metal materials is a promising strategy because it has some advantages over traditional approaches for cost, efficiency, and simplicity.
Directory of Open Access Journals (Sweden)
Fukang Ma
2018-04-01
Full Text Available Based on the introduction of opposed-piston two-stroke (OP2S gasoline direct injection (GDI engines, the OP2S-GDI engine working principle and scavenging process were analyzed. GT-Power software was employed to model the working process based on the structural style and principle of OP2S-GDI engine. The tracer gas method and OP2S-GDI engine experiment were employed for model validation at full load of 6000 rpm. The OP2S-GDI engine scavenging system parameters were optimized, including intake port height stroke ratio, intake port circumference ratio, exhaust port height stroke ratio, exhaust port circumference ratio, and opposed-piston motion phase difference. At the same time, the effect of the port height stroke ratio and opposed-piston motion phase difference on effective compression ratio and expansion ratio were considered, and the indicated work was employed as the optimization objective. A three-level orthogonal experiment was applied in the calculation process to reduce the calculation work. The influence and correlation coefficient on the scavenging efficiency and delivery ratio were investigated by the orthogonal experiment analysis of intake and exhaust port height stroke ratio and circular utilization. The effect of the scavenging system parameters on delivery ratio, scavenging efficiency and indicated work were calculated to obtain the best parameters. The results show that intake port height stroke ratio is the main factor for the delivery ratio, while exhaust port height stroke ratio is the main factor to engine delivery ratio and scavenging efficiency.
Mixed integer evolution strategies for parameter optimization.
Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C
2013-01-01
Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems.
Nekrylov, Ivan; Korotaev, Valery; Blokhina, Anastasia; Kleshchenok, Maksim
2017-06-01
In the world is the widespread adoption of measuring equipment of new generation, which is characterized by small size, high automation level, a multi-channel, digital filtering, satellite synchronization, wireless communication, digital record in long-term memory with great resource, powered by long-lived sources, etc. However, modern equipment base of the Russian institutions and the level of development of technical facilities and measuring technologies lag far behind developed countries. For this reason, the vacated niches are actively developed by foreign companies. For example, more than 70% instrumentation performing works on the territory of Russia, equipped with imported equipment (products of Sweden and Germany companies); the amount of work performed with German equipment is more than 70% of the total volume of these works; more than 80% of industrial measurements are performed using HEXAGON equipment (Sweden). These trends show that the Russian sector of measuring technology gradually become import-dependent, which poses a threat to the economic security of the country and consistent with national priorities. The results of the research will allow to develop the theory of formation of control systems of the displacement with high accuracy and unattainable for the existing analogue ergonomic and weight characteristics combined with a comparable or lower cost. These advantages will allow you to be successful competition, and eventually to supplant the existing system, which had no fundamental changes in the last 20 years and, therefore, retained all the drawbacks: large size and weight, high power consumption, the dependence on magnetic fields
International Nuclear Information System (INIS)
Kahi, A.K.; Thorpe, W.
1999-01-01
An individual animal model was fitted to data from crossbred herd in the lowland coastal tropics of Kenya to estimate breed cross means for a total of 25 genotypes having different proportions of Ayrshire (A), Brown swiss (B), Friesian (F) and Sahiwal (S) genes. These means were then regressed on gene proportion of breeds and on the coefficients of heterosis and recombination loss. Per lactation, the S contributed 1802 kg and 18.4 kg less milk and milk per unit of metabolic weight (MW), respectively, than the F. The performance of A and B were intermediate. The contribution of the F breed for most traits was superior to that of the other Bos taurus breeds. the heterosis effect between B and S was large for lactation milk yield (MY) (296 kg) and calving interval (CL) (-36 days). The heterosis between A and B for most traits was small, which is consistent with other studies in the literature. The estimates of recombination loss were negative in crosses A * B and B * S for MY,daily milk yield (DMY) and MY expressed per unit MW. Modelling of alternative crossbreeding strategy should be considered in relation to the ecological and socio-economic characteristics of the target production systems, which vary markedly. Therefore, the generalisation that the first cross (F 1 ) is best suited for dairying in the tropics could be misleading, and there is the need to evaluate the potential of synthetic breeds
Directory of Open Access Journals (Sweden)
MARWAN M. SHAMEL
2006-12-01
Full Text Available This paper reports on the use of pilot scale membrane separation system coupled with another pilot scale plate heat exchanger to investigate the possibilities of sweetening seawater from Telok Kalong Beach, Terengganu, Malaysia. Reverse osmosis (RO membrane of a surface area of 0.5 m2 was used during the experimental runs. Experiments were conducted at different transmembrane pressures (TMP ranged from 40 to 55 bars, operation temperature ranged from 35 to 45oC, feed concentration (TDS ranged from 34900 to 52500 ppm and cross flow velocities ranged from 1.4 to 2.1 m/s. The result show that the flux values increased linearly with TMP as well as sodium ion rejection. Permeate flux values increased proportionally with the temperature and the later effect was more significant at high pressures. The temperature changing has also influenced the rejection of sodium ion. The minerals content especially NaCl and total dissolved solid (TDS in the drinking water produced in this research are conforming to the standards of World Health Organization (WHO.
Optimization of parameters of heat exchangers vehicles
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Andrei MELEKHIN
2014-09-01
Full Text Available The relevance of the topic due to the decision of problems of the economy of resources in heating systems of vehicles. To solve this problem we have developed an integrated method of research, which allows to solve tasks on optimization of parameters of heat exchangers vehicles. This method decides multicriteria optimization problem with the program nonlinear optimization on the basis of software with the introduction of an array of temperatures obtained using thermography. The authors have developed a mathematical model of process of heat exchange in heat exchange surfaces of apparatuses with the solution of multicriteria optimization problem and check its adequacy to the experimental stand in the visualization of thermal fields, an optimal range of managed parameters influencing the process of heat exchange with minimal metal consumption and the maximum heat output fin heat exchanger, the regularities of heat exchange process with getting generalizing dependencies distribution of temperature on the heat-release surface of the heat exchanger vehicles, defined convergence of the results of research in the calculation on the basis of theoretical dependencies and solving mathematical model.
International Nuclear Information System (INIS)
Qi, Ronghui; Lu, Lin; Huang, Yu
2015-01-01
Highlights: • Operation conditions significantly affect energy & economic performance of SLDCS. • Control parameters in three areas were optimized by Multi-Population Genetic Algorithm. • Solar collector area showed the greatest effect on system performance for humid areas. • Desiccant concentration showed greatest effect on system performance for dry areas. • Requirement of collector area, heating water and desiccant flow rates for humid areas is highest. - Abstract: Operation conditions significantly affect the energy and economic performance of solar-assisted liquid desiccant cooling systems. This study optimized the system control parameters for buildings in different climates, i.e., Singapore (hot and humid), Beijing (moderate) and Boulder (hot and dry), with a multi-parameter optimization based on the Multi-Population Genetic Algorithm to obtain optimal system performance in terms of relatively maximum electricity saving rate with a minimum cost payback period. The results indicated that the selection of operation parameters is significantly influenced by climatic conditions. The solar collector installation area exhibited the greatest effect on both energy and economic performance in humid areas, and the heating water flow rate was also important. For dry areas, a change in desiccant concentration had the largest effect on system performance. Although the effect of the desiccant flow rate was significant in humid cities, it appeared to have little influence over buildings in dry areas. Furthermore, the requirements of the solar collector installation area in humid areas were much higher. The optimized area was up to 70 m 2 in Singapore compared with 27.5 m 2 in Boulder. Similar results were found for the flow rates of heating water and the desiccant solution. Applying the optimization, humid cities could achieve an electricity saving of more than 40% with a six-year payback period. The optimal performance for hot and dry areas of a 38% electricity
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; Zhang, Jun; Gao, Wenzhong
2016-01-01
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.
Evaluation of GCC optimization parameters
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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.
Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization
Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha
2017-01-01
With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626
Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization
Directory of Open Access Journals (Sweden)
MadhuSudana Rao Nalluri
2017-01-01
Full Text Available With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM and multilayer perceptron (MLP technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs. Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.
Parameters control in GAs for dynamic optimization
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Khalid Jebari
2013-02-01
Full Text Available The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters.
Miyamoto, Naoki; Ishikawa, Masayori; Bengua, Gerard; Sutherland, Kenneth; Suzuki, Ryusuke; Kimura, Suguru; Shimizu, Shinichi; Onimaru, Rikiya; Shirato, Hiroki
2011-08-07
In the real-time tumor-tracking radiotherapy system, fluoroscopy is used to determine the real-time position of internal fiducial markers. The pattern recognition score (PRS) ranging from 0 to 100 is computed by a template pattern matching technique in order to determine the marker position on the fluoroscopic image. The PRS depends on the quality of the fluoroscopic image. However, the fluoroscopy parameters such as tube voltage, current and exposure duration are selected manually and empirically in the clinical situation. This may result in an unnecessary imaging dose from the fluoroscopy or loss of the marker because of too much or insufficient x-ray exposure. In this study, a novel optimization method is proposed in order to minimize the fluoroscopic dose while keeping the image quality usable for marker tracking. The PRS can be predicted in a region where the marker appears to move in the fluoroscopic image by the proposed method. The predicted PRS can be utilized to judge whether the marker can be tracked with accuracy. In this paper, experiments were performed to show the feasibility of the PRS prediction method under various conditions. The predicted PRS showed good agreement with the measured PRS. The root mean square error between the predicted PRS and the measured PRS was within 1.44. An experiment using a motion controller and an anthropomorphic chest phantom was also performed in order to imitate a clinical fluoroscopy situation. The result shows that the proposed prediction method is expected to be applicable in a real clinical situation.
Eloot, Sunny; Peperstraete, Harlinde; De Somer, Filip; Hoste, Eric
2017-01-13
Lung protective ventilation is recommended in patients with acute respiratory distress syndrome (ARDS) needing mechanical ventilation. This can however be associated with hypercapnia and respiratory acidosis, such that extracorporeal CO2 removal (ECCO2R) can be applied. The aim of this study was to derive optimal operating parameters for the ECCO2R Abylcap® system (Bellco, Italy). We included 4 ARDS patients with a partial arterial oxygen tension over the fraction of inspired oxygen (PaO2/FiO2) lower than 150 mmHg, receiving lung-protective ventilation and treated with the Abylcap® via a double lumen 13.5-Fr dialysis catheter in the femoral vein. Every 24 hours during 5 consecutive days, blood was sampled at the Abylcap® inlet and outlet for different blood flows (QB:200-300-400 mL/min) with 100% O2 gas flow (QG) of 7 L/min, and for different QG (QG: 0.5-1-1.5-3-6-8 L/min) with QB400 mL/min. CO2 and O2 transfer remained constant over 5 days for a fixed QB. We found that, for a fixed QG of 7 L/min, CO2 transfer linearly and significantly increased with QB (i.e. from 58 ± 8 to 98 ± 16 mL/min for QB 200 to 400 mL/min). For a fixed QB of 400 mL/min, CO2 transfer non-linearly increased with QG (i.e. from 39 ± 9 to 98 ± 16 mL/min for QG 0.5 to 8 L/min) reaching a plateau at QG of 6 L/min. Hence, when using the Abylcap® ECCO2R in the treatment of ARDS patients the O2 flow should be at least 6 L/min while QB should be set at its maximum.
Powell, I
2000-05-01
I describe a practical method for facilitating the construction of a complex optical arrangement that was extremely sensitive to manufacturing defects. I discuss dealing with the actual tolerancing process employed and outline the reverse optimization technique adopted to take the necessary corrective action with regard to the lens to yield the performance specified. The optical design to which the techniques are addressed is that of a high-performance color-corrected scanner lens, capable of resolving 200 line pairs/mm over a 10-mm(2) object.
Guía-Tello, J. C.; Pech-Canul, M. A.; Trujillo-Vázquez, E.; Pech-Canul, M. I.
2017-08-01
Controlled atmosphere brazing has a widespread industrial use in the production of aluminum automotive heat exchangers. Good-quality joints between the components depend on the initial condition of materials as well as on the brazing process parameters. In this work, the Taguchi method was used to optimize the brazing parameters with respect to corrosion performance for tube-fin mini-assemblies of an automotive condenser. The experimental design consisted of five factors (micro-channel tube type, flux type, peak temperature, heating rate and dwell time), with two levels each. The corrosion behavior in acidified seawater solution pH 2.8 was evaluated through potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) measurements. Scanning electron microscope (SEM) and energy-dispersive x-ray spectroscopy (EDS) were used to analyze the microstructural features in the joint zone. The results showed that the parameters that most significantly affect the corrosion rate are the type of flux and the peak temperature. The optimal conditions were: micro-channel tube with 4.2 g/m2 of zinc coating, standard flux, 610 °C peak temperature, 5 °C/min heating rate and 4 min dwell time. The corrosion current density value of the confirmation experiment is in excellent agreement with the predicted value. The electrochemical characterization for selected samples gave indication that the brazing conditions had a more significant effect on the kinetics of the hydrogen evolution reaction than on the kinetics of the metal dissolution reaction.
Directory of Open Access Journals (Sweden)
Li Wang
2017-02-01
Full Text Available The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO algorithm that is based on random perturbation (RP-PSO. The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.
Directory of Open Access Journals (Sweden)
Fu-Kang Ma
2017-03-01
Full Text Available In this paper, an opposed-piston two-stroke (OP2S gasoline direct injection (GDI engine is introduced and its working principles and scavenging process were analyzed. An optimization function was established to optimize the scavenging system parameters, include intake port height, exhaust port height, intake port circumference ratio, the exhaust port circumference ratio and opposed-piston motion phase difference. The effect of the port height on the effective compression ratio and effective expansion ratio were considered, and indicated mean effective pressure (IMEP was employed as the optimization objective instead of scavenging efficiency. Orthogonal experiments were employed to reduce the calculation work. The effect of the scavenging parameters on delivery ratio, trapping ratio, scavenging efficiency and indicated thermal efficiency were calculated, and the best parameters were also obtained by the optimization function. The results show that IMEP can be used as the optimization objective in the uniflow scavenging system; intake port height is the main factor to the delivery ratio, while exhaust port height is the main to engine trapping ratio, scavenging efficiency and indicated thermal efficiency; exhaust port height is the most important factor to effect the gas exchange process of OP2S-GDI engine.
Optimization of Camera Parameters in Volume Intersection
Sakamoto, Sayaka; Shoji, Kenji; Toyama, Fubito; Miyamichi, Juichi
Volume intersection is one of the simplest techniques for reconstructing 3-D shape from 2-D silhouettes. 3D shapes can be reconstructed from multiple view images by back-projecting them from the corresponding viewpoints and intersecting the resulting solid cones. The camera position and orientation (extrinsic camera parameters) of each viewpoint with respect to the object are needed to accomplish reconstruction. However, even a little variation in the camera parameters makes the reconstructed 3-D shape smaller than that with the exact parameters. The problem of optimizing camera parameters deals with determining exact ones based on multiple silhouette images and approximate ones. This paper examines attempts to optimize camera parameters by reconstructing a 3-D shape via the concept of volume intersection and then maximizing the volume of the 3-D shape. We have tested the proposed method to optimize the camera parameters using a VRML model. In experiments we apply the downhill simplex method to optimize them. The results of experiments show that the maximized volume of the reconstructed 3-D shape is one of the criteria to optimize camera parameters in camera arrangement like this experiment.
Optimal Formation Trajectory-Planning Using Parameter Optimization Technique
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Hyung-Chul Lim
2004-09-01
Full Text Available Some methods have been presented to get optimal formation trajectories in the step of configuration or reconfiguration, which subject to constraints of collision avoidance and final configuration. In this study, a method for optimal formation trajectory-planning is introduced in view of fuel/time minimization using parameter optimization technique which has not been applied to optimal trajectory-planning for satellite formation flying. New constraints of nonlinear equality are derived for final configuration and constraints of nonlinear inequality are used for collision avoidance. The final configuration constraints are that three or more satellites should be placed in an equilateral polygon of the circular horizontal plane orbit. Several examples are given to get optimal trajectories based on the parameter optimization problem which subjects to constraints of collision avoidance and final configuration. They show that the introduced method for trajectory-planning is well suited to trajectory design problems of formation flying missions.
Tucker, Andrew; Qian, Xin; Gidcumb, Emily; Spronk, Derrek; Sprenger, Frank; Kuo, Johnny; Ng, Susan; Lu, Jianping; Zhou, Otto
2012-03-01
The stationary Digital Breast Tomosynthesis System (s-DBT) has the advantage over the conventional DBT systems as there is no motion blurring in the projection images associated with the x-ray source motion. We have developed a prototype s-DBT system by retrofitting a Hologic Selenia Dimensions rotating gantry tomosynthesis system with a distributed carbon nanotube (CNT) x-ray source array. The linear array consists of 31 x-ray generating focal spots distributed over a 30 degree angle. Each x-ray beam can be electronically activated allowing the flexibility and easy implementation of novel tomosynthesis scanning with different scanning parameters and configurations. Here we report the initial results of investigation on the imaging quality of the s-DBT system and its dependence on the acquisition parameters including the number of projections views, the total angular span of the projection views, the dose distribution between different projections, and the total dose. A mammography phantom is used to visually assess image quality. The modulation transfer function (MTF) of a line wire phantom is used to evaluate the system spatial resolution. For s-DBT the in-plan system resolution, as measured by the MTF, does not change for different configurations. This is in contrast to rotating gantry DBT systems, where the MTF degrades for increased angular span due to increased focal spot blurring associated with the x-ray source motion. The overall image quality factor, a composite measure of the signal difference to noise ratio (SdNR) for mass detection and the z-axis artifact spread function for microcalcification detection, is best for the configuration with a large angular span, an intermediate number of projection views, and an even dose distribution. These results suggest possible directions for further improvement of s-DBT systems for high quality breast cancer imaging.
Optimal Laser Phototherapy Parameters for Pain Relief.
Kate, Rohit J; Rubatt, Sarah; Enwemeka, Chukuka S; Huddleston, Wendy E
2018-03-27
Studies on laser phototherapy for pain relief have used parameters that vary widely and have reported varying outcomes. The purpose of this study was to determine the optimal parameter ranges of laser phototherapy for pain relief by analyzing data aggregated from existing primary literature. Original studies were gathered from available sources and were screened to meet the pre-established inclusion criteria. The included articles were then subjected to meta-analysis using Cohen's d statistic for determining treatment effect size. From these studies, ranges of the reported parameters that always resulted into large effect sizes were determined. These optimal ranges were evaluated for their accuracy using leave-one-article-out cross-validation procedure. A total of 96 articles met the inclusion criteria for meta-analysis and yielded 232 effect sizes. The average effect size was highly significant: d = +1.36 (confidence interval [95% CI] = 1.04-1.68). Among all the parameters, total energy was found to have the greatest effect on pain relief and had the most prominent optimal ranges of 120-162 and 15.36-20.16 J, which always resulted in large effect sizes. The cross-validation accuracy of the optimal ranges for total energy was 68.57% (95% CI = 53.19-83.97). Fewer and less-prominent optimal ranges were obtained for the energy density and duration parameters. None of the remaining parameters was found to be independently related to pain relief outcomes. The findings of meta-analysis indicate that laser phototherapy is highly effective for pain relief. Based on the analysis of parameters, total energy can be optimized to yield the largest effect on pain relief.
Cosmological parameter estimation using Particle Swarm Optimization
International Nuclear Information System (INIS)
Prasad, J; Souradeep, T
2014-01-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite
Cosmological parameter estimation using Particle Swarm Optimization
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Directory of Open Access Journals (Sweden)
Francisco Javier Fernández
2017-11-01
Full Text Available Water loop heat pump (WLHP air conditioning systems use heat pumps connected to a common water circuit to fulfill the energy demands of different thermal zones in a building. In this study, the energy consumption was analyzed for the air conditioning of an office building in the typical climate of four important cities of the Iberian Peninsula. The energy consumption of one water loop heat pump system was compared with a conventional water system. Two design parameters, the range in the control temperatures and the water loop thermal storage size, were tested. Energy redistribution is an important advantage of the WLHP system, but significant savings came from high efficiency parameters in the heat pumps and minor air flow rates in the cooling tower. The low thermal level in the water loop makes this technology appropriate to combine with renewable sources. Using natural gas as the thermal energy source, a mean decrease in CO2 emissions of 8.1% was reached. Simulations showed that the installation of big thermal storage tanks generated small energy savings. Besides, the total annual consumption in buildings with high internal loads can be reduced by keeping the water loop as cool as possible.
Huang, Zhujian; Zhang, Xianning; Cui, Lihua; Yu, Guangwei
2016-09-15
In this work, three hybrid vertical down-flow constructed wetland (HVDF-CW) systems with different compound substrates were fed with domestic sewage and their pollutants removal performance under different hydraulic loading and step-feeding ratio was investigated. The results showed that the hydraulic loading and step-feeding ratio were two crucial factors determining the removal efficiency of most pollutants, while substrate types only significantly affected the removal of COD and NH4(+)-N. Generally, the lower the hydraulic loading, the better removal efficiency of all contaminants, except for TN. By contrast, the increase of step-feeding ratio would slightly reduce the removal rate of ammonium and TP but obviously promoted the TN removal. Therefore, the optimal operation of this CWs could be achieved with low hydraulic loading combined with 50% of step-feeding ratio when TN removal is the priority, whereas medium or low hydraulic loading without step-feeding would be suitable when TN removal is not taken into consideration. The obtained results in this study can provide us with a guideline for design and optimization of hybrid vertical flow constructed wetland systems to improve the pollutants removal from domestic sewage. Copyright © 2016 Elsevier Ltd. All rights reserved.
Multi-objective optimization in quantum parameter estimation
Gong, BeiLi; Cui, Wei
2018-04-01
We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.
Control parameter optimization for AP1000 reactor using Particle Swarm Optimization
International Nuclear Information System (INIS)
Wang, Pengfei; Wan, Jiashuang; Luo, Run; Zhao, Fuyu; Wei, Xinyu
2016-01-01
Highlights: • The PSO algorithm is applied for control parameter optimization of AP1000 reactor. • Key parameters of the MSHIM control system are optimized. • Optimization results are evaluated though simulations and quantitative analysis. - Abstract: The advanced mechanical shim (MSHIM) core control strategy is implemented in the AP1000 reactor for core reactivity and axial power distribution control simultaneously. The MSHIM core control system can provide superior reactor control capabilities via automatic rod control only. This enables the AP1000 to perform power change operations automatically without the soluble boron concentration adjustments. In this paper, the Particle Swarm Optimization (PSO) algorithm has been applied for the parameter optimization of the MSHIM control system to acquire better reactor control performance for AP1000. System requirements such as power control performance, control bank movement and AO control constraints are reflected in the objective function. Dynamic simulations are performed based on an AP1000 reactor simulation platform in each iteration of the optimization process to calculate the fitness values of particles in the swarm. The simulation platform is developed in Matlab/Simulink environment with implementation of a nodal core model and the MSHIM control strategy. Based on the simulation platform, the typical 10% step load decrease transient from 100% to 90% full power is simulated and the objective function used for control parameter tuning is directly incorporated in the simulation results. With successful implementation of the PSO algorithm in the control parameter optimization of AP1000 reactor, four key parameters of the MSHIM control system are optimized. It has been demonstrated by the calculation results that the optimized MSHIM control system parameters can improve the reactor power control capability and reduce the control rod movement without compromising AO control. Therefore, the PSO based optimization
Optimal filtering, parameter tracking, and control of nonlinear nuclear reactors
International Nuclear Information System (INIS)
March-Leuba, C.; March-Leuba, J.; Perez, R.B.
1988-01-01
This paper presents a new formulation of a class of nonlinear optimal control problems in which the system's signals are noisy and some system parameters are changing arbitrarily with time. The methodology is validated with an application to a nonlinear nuclear reactor model. A variational technique based on Pontryagin's Maximum Principle is used to filter the noisy signals, estimate the time-varying parameters, and calculate the optimal controls. The reformulation of the variational technique as an initial value problem allows this microprocessor-based algorithm to perform on-line filtering, parameter tracking, and control
Directory of Open Access Journals (Sweden)
Chien-Lin Huang
2015-01-01
Full Text Available This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.
Parameter optimization for surface flux transport models
Whitbread, T.; Yeates, A. R.; Muñoz-Jaramillo, A.; Petrie, G. J. D.
2017-11-01
Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams, weighted by a latitude-dependent error structure which reflects uncertainty in observations. Due to the easily adaptable nature of the 2D model, the optimization process is repeated for Cycles 21, 22, and 24 in order to analyse cycle-to-cycle variation of the optimal solution. We find that the ranges and optimal solutions for the various regimes are in reasonable agreement with results from the literature, both theoretical and observational. The optimal meridional flow profiles for each regime are almost entirely within observational bounds determined by magnetic feature tracking, with the 2D model being able to accommodate the mean observed profile more successfully. Differences between models appear to be important in deciding values for the diffusive and decay terms. In like fashion, differences in the behaviours of different solar cycles lead to contrasts in parameters defining the meridional flow and initial field strength.
Optimization of hydraulic turbine governor parameters based on WPA
Gao, Chunyang; Yu, Xiangyang; Zhu, Yong; Feng, Baohao
2018-01-01
The parameters of hydraulic turbine governor directly affect the dynamic characteristics of the hydraulic unit, thus affecting the regulation capacity and the power quality of power grid. The governor of conventional hydropower unit is mainly PID governor with three adjustable parameters, which are difficult to set up. In order to optimize the hydraulic turbine governor, this paper proposes wolf pack algorithm (WPA) for intelligent tuning since the good global optimization capability of WPA. Compared with the traditional optimization method and PSO algorithm, the results show that the PID controller designed by WPA achieves a dynamic quality of hydraulic system and inhibits overshoot.
Optimization of machining parameters for green manufacturing
Directory of Open Access Journals (Sweden)
Y. Anand
2016-12-01
Full Text Available Energy crisis is affecting the world badly. While the production in developed countries stabilizes, in the developing world it continues to expand. This results in higher energy use, thereby releasing higher CO2. Thus, a pilot experiment was conducted to check and subsequently take corrective measures to reduce the energy consumption of manufacturing industry. Here, the emphasis is laid particularly on the turning operation for the cutting parameters, and effort has been made to optimize them, using Design Expert, with regard to the energy consumed. Also the optimized values, from the, for the different parameters under study have been checked and compared by those being generally used. For experimental studies, the machining was first carried on mild steel and then after aluminum and brass were also considered for study. All the values show an appreciable reduction in the energy consumption, thus reducing the carbon emission, for all the materials.
Optimization of machining parameters for green manufacturing
Y. Anand; A. Gupta; A. Abrol; Ayush Gupta; V. Kumar; S.K. Tyagi; S. Anand
2016-01-01
Energy crisis is affecting the world badly. While the production in developed countries stabilizes, in the developing world it continues to expand. This results in higher energy use, thereby releasing higher CO2. Thus, a pilot experiment was conducted to check and subsequently take corrective measures to reduce the energy consumption of manufacturing industry. Here, the emphasis is laid particularly on the turning operation for the cutting parameters, and effort has been made to optimize them...
Cosmological parameter estimation using particle swarm optimization
Prasad, Jayanti; Souradeep, Tarun
2012-06-01
Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.
Directory of Open Access Journals (Sweden)
Xiang-ming Gao
2017-01-01
Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.
Genetic Algorithm Optimizes Q-LAW Control Parameters
Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard
2008-01-01
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
Optimization of Bleaching Parameters for Soybean Oil
Directory of Open Access Journals (Sweden)
Tomislav Domijan
2012-01-01
Full Text Available The final stage of edible soybean oil manufacture is refining, the most delicate phase of which is bleaching. At this step, undesirable substances are removed, such as pigments, traces of metals, phospholipids and certain degradation products. However, certain valuable compounds such as tocopherols and sterols may also be removed, significant loss of oxidative stability can occur, and fatty acid content may increase. To avoid these negative oil changes, bleaching parameters such as the concentration of bleaching clay, temperature and duration should be optimized. Since bleaching conditions depend on the properties of the bleaching clay as well as on the type of crude oil, bleaching parameters should be optimized with different types of clay for each vegetable oil. Since such optimization has not yet been reported for soybean oil treated with Pure-Flo® Supreme Pro-Active bleaching adsorbent, this study investigates the effect of bleaching parameters on bleaching efficiency, oxidative stability and the content and composition of bioactive compounds (tocopherols and sterols using the above mentioned clay in this type of oil. Results show that the amount of clay had the greatest influence on bleaching efficiency, especially according to the Lovibond scale, on transparency, and on phosphorus content. Temperature and clay amount significantly affected oxidative stability, in particular the formation of secondary oxidation products. Increasing the amount of clay decreased tocopherol content of the bleached oil. Neutralized soybean oil bleached for 20 min at 95 °C with 1 % Pure-Flo® Supreme Pro-Active bleaching clay showed the highest oxidative stability, best bleaching efficiency, and most favourable sterol content, although tocopherol content was reduced.
Parameter optimization in the regularized kernel minimum noise fraction transformation
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack
2012-01-01
Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....
Directory of Open Access Journals (Sweden)
Hussien A. Elsayed
2017-12-01
Full Text Available One of the most significant procedures in oil refineries is naphtha catalytic reforming unit in which high octane gasoline is gained. Normally, in oil refineries, flow instability in the composition of feedstock can affect the product quality. The aim of the present work was focused on modifications of the final product flow rate and product’s octane number with respect to the modifications of the feedstock composition. The main three reforming reactions investigated, namely; dehydrogenation, dehydrocyclization, and hydrocracking were conducted employing silica supported bimetallic (Pt-Re patented catalyst. Optimization of the catalytic process reaction conditions, i.e.; temperature, hydrogen pressure and liquid hourly space velocity (LHSV was carried out with regard to conversion and selectivity. The optimization results indicated that heavy naphtha component conversion (paraffin’s and naphthenes increases with an increasing in reaction temperature and pressure while decreases with an increase in LHSV. The kinetic study of catalytic reforming reactions reported helped establishing the reaction model explicitly.
National Aeronautics and Space Administration — An optimal alarm system is simply an optimal level-crossing predictor that can be designed to elicit the fewest false alarms for a fixed detection probability. It...
Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...
With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th
International Nuclear Information System (INIS)
Vasseur, D.; Eid, M.
1996-01-01
One of EDF's current priorities is the optimisation of the preventive maintenance in all French nuclear power stations. This optimisation involves a rationalization of the choice of equipments to be maintained and maintenance tasks to be carried out, as well as a judicious choice of intervals between these tasks. This work is being carried out in cooperation between EDF and the CEA (Atomic Energy Commission), and suggests a procedure to provide assistance in optimising intervals between maintenance tasks respecting a global unavailability target. This work is based on the differential model for equivalent parameters (DMEP). (authors)
The Determination of Optimal Parameters of Fuzzy PI Sugeno Controller
Kudinov, Y. I.; Kudinov, I. Yu; Volkova, A. A.; Durgarjan, I. S.; Pashchenko, F. F.
2017-11-01
Describe the procedure for determining by means of Matlab and Simulink optimal parameters of the fuzzy PI controller Sugeno, where some indicators of the quality of the transition process in a closed system control with this controller satisfies the specified conditions.
System performance optimization
International Nuclear Information System (INIS)
Bednarz, R.J.
1978-01-01
The System Performance Optimization has become an important and difficult field for large scientific computer centres. Important because the centres must satisfy increasing user demands at the lowest possible cost. Difficult because the System Performance Optimization requires a deep understanding of hardware, software and workload. The optimization is a dynamic process depending on the changes in hardware configuration, current level of the operating system and user generated workload. With the increasing complication of the computer system and software, the field for the optimization manoeuvres broadens. The hardware of two manufacturers IBM and CDC is discussed. Four IBM and two CDC operating systems are described. The description concentrates on the organization of the operating systems, the job scheduling and I/O handling. The performance definitions, workload specification and tools for the system stimulation are given. The measurement tools for the System Performance Optimization are described. The results of the measurement and various methods used for the operating system tuning are discussed. (Auth.)
CSIR Research Space (South Africa)
Bembe, MJ
2010-11-01
Full Text Available receiver circuit required. The bias voltage is always reverse to the varicap diode (variable reactance) used, meaning that very little DC current is used in the parasitic elements [13]. The literature shows that there are various types of design... for new and effective ways to counter the above outlined obstacles. The increasing user demand requires a high transmission rate communication system. There are two main challenges for high transmission rate realization: multiple interferences...
Gusev, Sergey A.; Nikolaev, Vladimir N.
2018-01-01
The method for determination of an aircraft compartment thermal condition, based on a mathematical model of a compartment thermal condition was developed. Development of solution techniques for solving heat exchange direct and inverse problems and for determining confidence intervals of parametric identification estimations was carried out. The required performance of air-conditioning, ventilation systems and heat insulation depth of crew and passenger cabins were received.
Optimization of rotational arc station parameter optimized radiation therapy
International Nuclear Information System (INIS)
Dong, P.; Ungun, B.; Boyd, S.; Xing, L.
2016-01-01
Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was
A choice of the parameters of NPP steam generators on the basis of vector optimization
International Nuclear Information System (INIS)
Lemeshev, V.U.; Metreveli, D.G.
1981-01-01
The optimization problem of the parameters of the designed systems is considered as the problem of multicriterion optimization. It is proposed to choose non-dominant, optimal according to Pareto, parameters. An algorithm is built on the basis of the required and sufficient non-dominant conditions to find non-dominant solutions. This algorithm has been employed to solve the problem on a choice of optimal parameters for the counterflow shell-tube steam generator of NPP of BRGD type [ru
Energy Technology Data Exchange (ETDEWEB)
Costa, Geraldo R.M. da [Sao Paulo Univ., Sao Carlos, SP (Brazil). Escola de Engenharia
1994-12-31
This paper discusses, partially, the advantages and the disadvantages of the optimal power flow. It shows some of the difficulties of implementation and proposes solutions. An analysis is made comparing the power flow, BIGPOWER/CESP, and the optimal power flow, FPO/SEL, developed by the author, when applied to the CEPEL-ELETRONORTE and CESP systems. (author) 8 refs., 5 tabs.
Zhou, Zhi; de Bedout, Juan Manuel; Kern, John Michael; Biyik, Emrah; Chandra, Ramu Sharat
2013-01-22
A system for optimizing customer utility usage in a utility network of customer sites, each having one or more utility devices, where customer site is communicated between each of the customer sites and an optimization server having software for optimizing customer utility usage over one or more networks, including private and public networks. A customer site model for each of the customer sites is generated based upon the customer site information, and the customer utility usage is optimized based upon the customer site information and the customer site model. The optimization server can be hosted by an external source or within the customer site. In addition, the optimization processing can be partitioned between the customer site and an external source.
The solution of private problems for optimization heat exchangers parameters
Melekhin, A.
2017-11-01
The relevance of the topic due to the decision of problems of the economy of resources in heating systems of buildings. To solve this problem we have developed an integrated method of research which allows solving tasks on optimization of parameters of heat exchangers. This method decides multicriteria optimization problem with the program nonlinear optimization on the basis of software with the introduction of an array of temperatures obtained using thermography. The author have developed a mathematical model of process of heat exchange in heat exchange surfaces of apparatuses with the solution of multicriteria optimization problem and check its adequacy to the experimental stand in the visualization of thermal fields, an optimal range of managed parameters influencing the process of heat exchange with minimal metal consumption and the maximum heat output fin heat exchanger, the regularities of heat exchange process with getting generalizing dependencies distribution of temperature on the heat-release surface of the heat exchanger vehicles, defined convergence of the results of research in the calculation on the basis of theoretical dependencies and solving mathematical model.
GA BASED GLOBAL OPTIMAL DESIGN PARAMETERS FOR ...
African Journals Online (AJOL)
This article uses Genetic Algorithm (GA) for the global design optimization of consecutive reactions taking place in continuous stirred tank reactors (CSTRs) connected in series. GA based optimal design determines the optimum number of CSTRs in series to achieve the maximum conversion, fractional yield and selectivity ...
Distributed Optimization System
Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.
2004-11-30
A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.
System floorplanning optimization
Browning, David W.
2012-12-01
Notebook and Laptop Original Equipment Manufacturers (OEMs) place great emphasis on creating unique system designs to differentiate themselves in the mobile market. These systems are developed from the \\'outside in\\' with the focus on how the system is perceived by the end-user. As a consequence, very little consideration is given to the interconnections or power of the devices within the system with a mentality of \\'just make it fit\\'. In this paper we discuss the challenges of Notebook system design and the steps by which system floor-planning tools and algorithms can be used to provide an automated method to optimize this process to ensure all required components most optimally fit inside the Notebook system. © 2012 IEEE.
System floorplanning optimization
Browning, David W.
2013-01-10
Notebook and Laptop Original Equipment Manufacturers (OEMs) place great emphasis on creating unique system designs to differentiate themselves in the mobile market. These systems are developed from the \\'outside in\\' with the focus on how the system is perceived by the end-user. As a consequence, very little consideration is given to the interconnections or power of the devices within the system with a mentality of \\'just make it fit\\'. In this paper we discuss the challenges of Notebook system design and the steps by which system floor-planning tools and algorithms can be used to provide an automated method to optimize this process to ensure all required components most optimally fit inside the Notebook system.
Teradata Database System Optimization
Krejčík, Jan
2008-01-01
The Teradata database system is specially designed for data warehousing environment. This thesis explores the use of Teradata in this environment and describes its characteristics and potential areas for optimization. The theoretical part is tended to be a user study material and it shows the main principles Teradata system operation and describes factors significantly affecting system performance. Following sections are based on previously acquired information which is used for analysis and ...
Optimization of electrospinning parameters for chitosan nanofibres
CSIR Research Space (South Africa)
Jacobs, V
2011-06-01
Full Text Available uniform chitosan nanofibres. The parameters studied were electric field strength, ratio of solvents - trifluoroacetic acid (TFA)/ dichloromethane (DCM), concentration of chitosan in the spinning solution, their individual and interaction effects...
Physical parameter optimization by Response Surface Methodology ...
African Journals Online (AJOL)
Response Surface Methodology (RSM) is an empirical technique involving the use of Design Expert software to derive a predictive model similar to regression analysis. This present study explains the significant application of RSM in optimization of lipase production by Aspergillus niger. The experimental validation of the ...
Hammonds, J; Price, R; Donnelly, E; Pickens, D
2012-06-01
A laboratory-based phase-contrast radiography/tomosynthesis imaging system previously (Med. Phys. Vol. 38, 2353 May 2011) for improved detection of low-contrast soft-tissue masses was used to evaluate the sensitivity for detecting the presence of thin layers of corrosion on aluminum aircraft structures. The evaluation utilized a test object of aluminum (2.5 inch × 2.5 inch × 1/8 inch) on which different geometric patterns of 0.0038 inch thick anodized aluminum oxide was deposited. A circular area of radius 1 inch centered on the phantom's midpoint was milled to an approximate thickness of 0.022 inches. The x-ray source used for this investigation was a dual focal spot, tungsten anode x-ray tube. The focal used during the investigation has a nominal size of 0.010 mm. The active area of the imager is 17.1 cm × 23.9 cm (2016 × 2816 pixels) with a pixel pitch of 0.085 mm. X-ray tube voltages ranged from 20-40 kVp and source- to-object and object-to-image distances were varied from 20-100 cm. Performance of the phase-contrast mode was compared to conventional absorption-based radiography using contrast ratio and contrast-to-noise ratios (C/N). Phase-contrast performance was based on edge-enhancement index (EEI) and the edge-enhancement-to-noise (EE/N) ratio. for absorption-based radiography, the best C/N ratio was observed at the lowest kVp value (20 kVp). The optimum sampling angle for tomosynthesis was +/- 8 degrees. Comparing C/N to EE/N demonstrated the phase-contrast techniques improve the conspicuity of the oxide layer edges. This work provides the optimal parameters that a radiographic imaging system would need to differentiate the two different compounds of aluminum. Subcontractee from Positron Systems Inc. (Boise, Idaho) through United States Air Force grant (AF083-225). © 2012 American Association of Physicists in Medicine.
Nanohydroxyapatite synthesis using optimized process parameters ...
Indian Academy of Sciences (India)
ted, the solutions were vacuum filtered and washed with water and ethanol. The washed precipitates (NHA) were ... The sample was subjected to low vacuum at an accelerating voltage of 20 kV, current of 60–90 mA and ..... sidering the operability of the ultrasonication machine in the actual process, the parameters were ...
Nanohydroxyapatite synthesis using optimized process parameters ...
Indian Academy of Sciences (India)
2016-08-26
Aug 26, 2016 ... In this study, nanohydroxyapatite (NHA) was synthesized using calcium nitrate tetrahydrate and diammonium hydrogen phosphate via the precipitation method assisted with ultrasonication. Three independent process parameters: temperature () (70, 80 and 90°C), ultrasonication time () (20, 25 and 30 ...
Optimization of Experimental Parameters in preparing ...
African Journals Online (AJOL)
The anodic oxidation method has been applied to the preparation of multinanoporous TiO2 thin films. The experimental parameters, including the electrolyte nature, oxidation voltage, and oxidation time have been carefully controlled. Their influence on the structure, morphology and photocatalytic activity of the prepared ...
Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem
Skakov, E. S.; Malysh, V. N.
2018-03-01
The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.
International Nuclear Information System (INIS)
Yamada, T; Fujii, Y; Miyamoto, N; Matsuura, T; Takao, S; Matsuzaki, Y; Koyano, H; Shirato, H; Nihongi, H; Umezawa, M; Matsuda, K; Umegaki, K
2015-01-01
Purpose: We have developed a gated spot scanning proton beam therapy system with real-time tumor-tracking. This system has the ability of multiple-gated irradiation in a single synchrotron operation cycle controlling the wait-time for consecutive gate signals during a flat-top phase so that the decrease in irradiation efficiency induced by irregular variation of gate signal is reduced. Our previous studies have shown that a 200 ms wait-time is appropriate to increase the average irradiation efficiency, but the optimal wait-time can vary patient by patient and day by day. In this research, we have developed an evaluation system of the optimal wait-time in each irradiation based on the log data of the real-time-image gated proton beam therapy (RGPT) system. Methods: The developed system consists of logger for operation of RGPT system and software for evaluation of optimal wait-time. The logger records timing of gate on/off, timing and the dose of delivered beam spots, beam energy and timing of X-ray irradiation. The evaluation software calculates irradiation time in the case of different wait-time by simulating the multiple-gated irradiation operation using several timing information. Actual data preserved in the log data are used for gate on and off time, spot irradiation time, and time moving to the next spot. Design values are used for the acceleration and deceleration times. We applied this system to a patient treated with the RGPT system. Results: The evaluation system found the optimal wait-time of 390 ms that reduced the irradiation time by about 10 %. The irradiation time with actual wait-time used in treatment was reproduced with accuracy of 0.2 ms. Conclusion: For spot scanning proton therapy system with multiple-gated irradiation in one synchrotron operation cycle, an evaluation system of the optimal wait-time in each irradiation based on log data has been developed. Funding Support: Japan Society for the Promotion of Science (JSPS) through the FIRST
Telemetry System of Biological Parameters
Directory of Open Access Journals (Sweden)
Jan Spisak
2005-01-01
Full Text Available The mobile telemetry system of biological parameters serves for reading and wireless data transfer of measured values of selected biological parameters to an outlying computer. It concerns basically long time monitoring of vital function of car pilot.The goal of this projects is to propose mobile telemetry system for reading, wireless transfer and processing of biological parameters of car pilot during physical and psychical stress. It has to be made with respect to minimal consumption, weight and maximal device mobility. This system has to eliminate signal noise, which is created by biological artifacts and disturbances during the data transfer.
Optimizing incomplete sample designs for item response model parameters
van der Linden, Willem J.
Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with
Fučík, Ivan
2015-01-01
This thesis is focused on CRM solutions in small and medium-sized organizations with respect to the quality of their customer relationship. The main goal of this work is to design an optimal CRM solution in the environment of real organization. To achieve this goal it is necessary to understand the theoretical basis of several topics, such as organizations and their relationship with customers, CRM systems, their features and trends. On the basis of these theoretical topics it is possible to ...
International Nuclear Information System (INIS)
Bogdan, Zeljko; Cehil, Mislav
2007-01-01
Long-term gas purchase contracts usually determine delivery and payment for gas on the regular hourly basis, independently of demand side consumption. In order to use fuel gas in an economically viable way, optimization of gas distribution for covering consumption must be introduced. In this paper, a mathematical model of the electric utility system which is used for optimization of gas distribution over electric generators is presented. The utility system comprises installed capacity of 1500 MW of thermal power plants, 400 MW of combined heat and power plants, 330 MW of a nuclear power plant and 1600 MW of hydro power plants. Based on known demand curve the optimization model selects plants according to the prescribed criteria. Firstly it engages run-of-river hydro plants, then the public cogeneration plants, the nuclear plant and thermal power plants. Storage hydro plants are used for covering peak load consumption. In case of shortage of installed capacity, the cross-border purchase is allowed. Usage of dual fuel equipment (gas-oil), which is available in some thermal plants, is also controlled by the optimization procedure. It is shown that by using such a model it is possible to properly plan the amount of fuel gas which will be contracted. The contracted amount can easily be distributed over generators efficiently and without losses (no breaks in delivery). The model helps in optimizing of fuel gas-oil ratio for plants with combined burners and enables planning of power plants overhauls over a year in a viable and efficient way. (author)
Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
1993-01-01
Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...
Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd
2018-03-01
Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.
Optimization of surface roughness parameters in dry turning
R.A. Mahdavinejad; H. Sharifi Bidgoli
2009-01-01
Purpose: The precision of machine tools on one hand and the input setup parameters on the other hand, are strongly influenced in main output machining parameters such as stock removal, toll wear ratio and surface roughnes.Design/methodology/approach: There are a lot of input parameters which are effective in the variations of these output parameters. In CNC machines, the optimization of machining process in order to predict surface roughness is very important.Findings: From this point of view...
Krause, David; John, Werner; Weigel, Robert
2016-03-01
The implementation of electrical drive trains in modern vehicles is a new challenge for EMC development. This contribution depicts a variety of investigations on magnetic field coupling of automotive high-voltage (HV) systems in order to fulfil the requirements of an EMR-optimized designing. The theoretical background is discussed within the scope of current analysis, including the determination of current paths and spectral behaviour. It furthermore presents models of shielded HV cables with particular focus on the magnetic shielding efficiency. Derived findings are validated by experimental measurements of a state-of-the-art demonstrator on system level. Finally EMC design rules are discussed in the context of minimized magnetic fields.
Optimal parameters of the SVM for temperature prediction
Directory of Open Access Journals (Sweden)
X. Shi
2015-05-01
Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.
Setting of the Optimal Parameters of Melted Glass
Czech Academy of Sciences Publication Activity Database
Luptáková, Natália; Matejíčka, L.; Krečmer, N.
2015-01-01
Roč. 10, č. 1 (2015), s. 73-79 ISSN 1802-2308 Institutional support: RVO:68081723 Keywords : Striae * Glass * Glass melting * Regression * Optimal parameters Subject RIV: JH - Ceramics, Fire-Resistant Materials and Glass
The optimal extraction parameters and anti-diabetic activity of ...
African Journals Online (AJOL)
diabetic activity of FIBL on alloxan induced diabetic mice were studied. The optimal extraction parameters of FIBL were obtained by single factor test and orthogonal test, as follows: ethanol concentration 60 %, ratio of solvent to raw material 30 ...
de Roo, Arjan; Sözer, Hasan; Aksit, Mehmet
Customers of today's complex embedded systems demand the optimization of multiple system qualities under varying operational conditions. To be able to influence the system qualities, the system must have parameters that can be adapted. Constraints may be defined on the value of these parameters.
Optimization of space manufacturing systems
Akin, D. L.
1979-01-01
Four separate analyses are detailed: transportation to low earth orbit, orbit-to-orbit optimization, parametric analysis of SPS logistics based on earth and lunar source locations, and an overall program option optimization implemented with linear programming. It is found that smaller vehicles are favored for earth launch, with the current Space Shuttle being right at optimum payload size. Fully reusable launch vehicles represent a savings of 50% over the Space Shuttle; increased reliability with less maintenance could further double the savings. An optimization of orbit-to-orbit propulsion systems using lunar oxygen for propellants shows that ion propulsion is preferable by a 3:1 cost margin over a mass driver reaction engine at optimum values; however, ion engines cannot yet operate in the lower exhaust velocity range where the optimum lies, and total program costs between the two systems are ambiguous. Heavier payloads favor the use of a MDRE. A parametric model of a space manufacturing facility is proposed, and used to analyze recurring costs, total costs, and net present value discounted cash flows. Parameters studied include productivity, effects of discounting, materials source tradeoffs, economic viability of closed-cycle habitats, and effects of varying degrees of nonterrestrial SPS materials needed from earth. Finally, candidate optimal scenarios are chosen, and implemented in a linear program with external constraints in order to arrive at an optimum blend of SPS production strategies in order to maximize returns.
On the role of modeling parameters in IMRT plan optimization
International Nuclear Information System (INIS)
Krause, Michael; Scherrer, Alexander; Thieke, Christian
2008-01-01
The formulation of optimization problems in intensity-modulated radiotherapy (IMRT) planning comprises the choice of various values such as function-specific parameters or constraint bounds. In current inverse planning programs that yield a single treatment plan for each optimization, it is often unclear how strongly these modeling parameters affect the resulting plan. This work investigates the mathematical concepts of elasticity and sensitivity to deal with this problem. An artificial planning case with a horse-shoe formed target with different opening angles surrounding a circular risk structure is studied. As evaluation functions the generalized equivalent uniform dose (EUD) and the average underdosage below and average overdosage beyond certain dose thresholds are used. A single IMRT plan is calculated for an exemplary parameter configuration. The elasticity and sensitivity of each parameter are then calculated without re-optimization, and the results are numerically verified. The results show the following. (1) elasticity can quantify the influence of a modeling parameter on the optimization result in terms of how strongly the objective function value varies under modifications of the parameter value. It also can describe how strongly the geometry of the involved planning structures affects the optimization result. (2) Based on the current parameter settings and corresponding treatment plan, sensitivity analysis can predict the optimization result for modified parameter values without re-optimization, and it can estimate the value intervals in which such predictions are valid. In conclusion, elasticity and sensitivity can provide helpful tools in inverse IMRT planning to identify the most critical parameters of an individual planning problem and to modify their values in an appropriate way
Optimization and optimal control in automotive systems
Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...
Parameter optimization toward optimal microneedle-based dermal vaccination.
van der Maaden, Koen; Varypataki, Eleni Maria; Yu, Huixin; Romeijn, Stefan; Jiskoot, Wim; Bouwstra, Joke
2014-11-20
Microneedle-based vaccination has several advantages over vaccination by using conventional hypodermic needles. Microneedles are used to deliver a drug into the skin in a minimally-invasive and potentially pain free manner. Besides, the skin is a potent immune organ that is highly suitable for vaccination. However, there are several factors that influence the penetration ability of the skin by microneedles and the immune responses upon microneedle-based immunization. In this study we assessed several different microneedle arrays for their ability to penetrate ex vivo human skin by using trypan blue and (fluorescently or radioactively labeled) ovalbumin. Next, these different microneedles and several factors, including the dose of ovalbumin, the effect of using an impact-insertion applicator, skin location of microneedle application, and the area of microneedle application, were tested in vivo in mice. The penetration ability and the dose of ovalbumin that is delivered into the skin were shown to be dependent on the use of an applicator and on the microneedle geometry and size of the array. Besides microneedle penetration, the above described factors influenced the immune responses upon microneedle-based vaccination in vivo. It was shown that the ovalbumin-specific antibody responses upon microneedle-based vaccination could be increased up to 12-fold when an impact-insertion applicator was used, up to 8-fold when microneedles were applied over a larger surface area, and up to 36-fold dependent on the location of microneedle application. Therefore, these influencing factors should be considered to optimize microneedle-based dermal immunization technologies. Copyright © 2014 Elsevier B.V. All rights reserved.
Investigation and validation of optimal cutting parameters for least ...
African Journals Online (AJOL)
The cutting parameters were analyzed and optimized using Box Behnken procedure in the DESIGN EXPERT environment. The effect of process parameters with the output variable were predicted which indicates that the highest cutting speed has significant role in producing least surface roughness followed by feed and ...
Parameter optimization of electrochemical machining process using black hole algorithm
Singh, Dinesh; Shukla, Rajkamal
2017-12-01
Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.
Helical tomotherapy optimized planning parameters for nasopharyngeal cancer
Yawichai, K.; Chitapanarux, I.; Wanwilairat, S.
2016-03-01
Helical TomoTherapy(HT) planning depends on optimize parameters including field width (FW), pitch factor (PF) and modulation factor (MF). These optimize parameters are effect to quality of plans and treatment time. The aim of this study was to find the optimized parameters which compromise between plan quality and treatment times. Six nasopharyngeal cancer patients were used. For each patient data set, 18 treatment plans consisted of different optimize parameters combination (FW=5.0, 2.5, 1.0 cm; PF=0.43, 0.287, 0.215; MF2.0, 3.0) were created. The identical optimization procedure followed ICRU83 recommendations. The average D50 of both parotid glands and treatment times per fraction were compared for all plans. The study show treatment plan with FW1.0 cm showed the lowest average D50 of both parotid glands. The treatment time increased inversely to FW. The FW1.0 cm the average treatment time was 4 times longer than FW5.0 cm. PF was very little influence on the average D50 of both parotid glands. Finally, MF increased from 2.0 to 3.0 the average D50 of both parotid glands was slightly decreased. However, the average treatment time was increased 22.28%. For routine nasopharyngeal cancer patients with HT, we suggest the planning optimization parameters consist of FW=5.0 cm, PF=0.43 and MF=2.0.
Optimizing chirped laser pulse parameters for electron acceleration in vacuum
Energy Technology Data Exchange (ETDEWEB)
Akhyani, Mina; Jahangiri, Fazel; Niknam, Ali Reza; Massudi, Reza, E-mail: r-massudi@sbu.ac.ir [Laser and Plasma Research Institute, Shahid Beheshti University, Tehran 1983969411 (Iran, Islamic Republic of)
2015-11-14
Electron dynamics in the field of a chirped linearly polarized laser pulse is investigated. Variations of electron energy gain versus chirp parameter, time duration, and initial phase of laser pulse are studied. Based on maximizing laser pulse asymmetry, a numerical optimization procedure is presented, which leads to the elimination of rapid fluctuations of gain versus the chirp parameter. Instead, a smooth variation is observed that considerably reduces the accuracy required for experimentally adjusting the chirp parameter.
Parameter optimization of protein film production using microbial transglutaminase.
Patzsch, Katja; Riedel, Kristin; Pietzsch, Markus
2010-04-12
Sodium caseinate films were produced using microbial transglutaminase as a protein cross-linking biocatalyst. Basic parameters for the film production, such as buffer type and concentration, pH, temperature, plasticizer concentration and its influence on transglutaminase activity, mold material for film casting, specimen width, and cutting method, were investigated and compared with standardized methods (DIN EN ISO 527-3). Surprisingly, a previously described sodium phosphate buffer (50 mM, pH 8.0) resulted in crystals after drying the films for 48 h. To avoid this deteriorating effect, the buffer system was optimized and finally a Tris-HCl buffer (20 mM, pH 7.0) was chosen for the production of transparent, smooth films without crystallization. Incubation time and temperature during enzyme treatment had a considerable influence on the mechanical properties of the films.
Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization
Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin
2017-09-01
Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.
APPLICATION OF GENETIC ALGORITHMS FOR ROBUST PARAMETER OPTIMIZATION
Directory of Open Access Journals (Sweden)
N. Belavendram
2010-12-01
Full Text Available Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution.
Multi-parameter optimization of electrostatic micro-generators using design optimization algorithms
International Nuclear Information System (INIS)
Hoffmann, Daniel; Folkmer, Bernd; Manoli, Yiannos
2010-01-01
In this paper, the design of an electrostatic micro-generator with an in-plane area-overlap architecture is optimized in a six-dimensional parameter space using multi-parameter optimization algorithms. A parametric model is presented including four geometric and two electrical parameters. The constraints of the design parameters are discussed. The design optimization is carried out in modeFRONTIER using a genetic algorithm. The results show that the displacement limit and the number of electrode elements are essential parameters, which require optimization in the design process. The other parameters take values at the upper or lower bound of their design space. The results also demonstrate that a maximized power output will not be achieved by maximizing the capacitance change per unit displacement
Directory of Open Access Journals (Sweden)
Akatsuki eKimura
2015-03-01
Full Text Available Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE in a prediction or to maximize likelihood. A (local maximum of likelihood or (local minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.
Utama, D. N.; Ani, N.; Iqbal, M. M.
2018-03-01
Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.
International Nuclear Information System (INIS)
Jayalal, M.L.; Kumar, L. Satish; Jehadeesan, R.; Rajeswari, S.; Satya Murty, S.A.V.; Balasubramaniyan, V.; Chetal, S.C.
2011-01-01
Highlights: → We model design optimization of a vital reactor component using Genetic Algorithm. → Real-parameter Genetic Algorithm is used for steam condenser optimization study. → Comparison analysis done with various Genetic Algorithm related mechanisms. → The results obtained are validated with the reference study results. - Abstract: This work explores the use of Real-parameter Genetic Algorithm and analyses its performance in the steam condenser (or Circulating Water System) optimization study of a 500 MW fast breeder nuclear reactor. Choice of optimum design parameters for condenser for a power plant from among a large number of technically viable combination is a complex task. This is primarily due to the conflicting nature of the economic implications of the different system parameters for maximizing the capitalized profit. In order to find the optimum design parameters a Real-parameter Genetic Algorithm model is developed and applied. The results obtained are validated with the reference study results.
Complicated problem solution techniques in optimal parameter searching
International Nuclear Information System (INIS)
Gergel', V.P.; Grishagin, V.A.; Rogatneva, E.A.; Strongin, R.G.; Vysotskaya, I.N.; Kukhtin, V.V.
1992-01-01
An algorithm is presented of a global search for numerical solution of multidimentional multiextremal multicriteria optimization problems with complicated constraints. A boundedness of object characteristic changes is assumed at restricted changes of its parameters (Lipschitz condition). The algorithm was realized as a computer code. The algorithm was realized as a computer code. The programme was used to solve in practice the different applied optimization problems. 10 refs.; 3 figs
IPO: a tool for automated optimization of XCMS parameters.
Libiseller, Gunnar; Dvorzak, Michaela; Kleb, Ulrike; Gander, Edgar; Eisenberg, Tobias; Madeo, Frank; Neumann, Steffen; Trausinger, Gert; Sinner, Frank; Pieber, Thomas; Magnes, Christoph
2015-04-16
Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to
Optimal Design of Shock Tube Experiments for Parameter Inference
Bisetti, Fabrizio
2014-01-06
We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation energies in the reaction rate expressions. The control parameters are the initial mixture composition and the temperature. The approach is based on first building a polynomial based surrogate model for the observables relevant to the shock tube experiments. Based on these surrogates, a novel MAP based approach is used to estimate the expected information gain in the proposed experiments, and to select the best experimental set-ups yielding the optimal expected information gains. The validity of the approach is tested using synthetic data generated by sampling the PC surrogate. We finally outline a methodology for validation using actual laboratory experiments, and extending experimental design methodology to the cases where the control parameters are noisy.
OPTIMIZATION OF HEMISPHERICAL RESONATOR GYROSCOPE STANDING WAVE PARAMETERS
Directory of Open Access Journals (Sweden)
Olga Sergeevna Khalyutina
2017-01-01
Full Text Available Traditionally, the problem of autonomous navigation is solved by dead reckoning navigation flight parameters (NFP of the aircraft (AC. With increasing requirements to accuracy of definition NFP improved the sensors of the prima- ry navigation information: gyroscopes and accelerometers. the gyroscopes of a new type, the so-called solid-state wave gyroscopes (SSVG are currently developed and put into practice. The work deals with the problem of increasing the accu- racy of measurements of angular velocity of the hemispherical resonator gyroscope (HRG. The reduction in the accuracy characteristics of HRG is caused by the presence of defects in the distribution of mass in the volume of its design. The syn- thesis of control system for optimal damping of the distortion parameters of the standing wave due to the influence of the mass defect resonator is adapted. The research challenge was: to examine and analytically offset the impact of the standing wave (amplitude and frequency parameters defect. Research was performed by mathematical modeling in the environment of SolidWorks Simulation for the case when the characteristics of the sensitive element of the HRG met the technological drawings of a particular type of resonator. The method of the inverse dynamics was chosen for synthesis. The research re- sults are presented in graphs the amplitude-frequency characteristics (AFC of the resonator output signal. Simulation was performed for the cases: the perfect distribution of weight; the presence of the mass defect; the presence of the mass defects are shown using the synthesized control action. Evaluating the effectiveness of the proposed control algorithm is deter- mined by the results of the resonator output signal simulation provided the perfect constructive and its performance in the presence of a mass defect in it. It is assumed that the excitation signals are standing waves in the two cases are identical in both amplitude and frequency. In this
Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
Norlina Mohd Sabri
2016-06-01
Full Text Available This research is focusing on the radio frequency (RF magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO, Genetic Algorithm (GA, Artificial Immune System (AIS and Ant Colony Optimization (ACO. Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.
Brodusch, Nicolas; Demers, Hendrix; Trudeau, Michel; Gauvin, Raynald
2013-01-01
Transmission electron forward scatter diffraction (t-EFSD) is a new technique providing crystallographic information with high resolution on thin specimens by using a conventional electron backscatter diffraction (EBSD) system in a scanning electron microscope. In this study, the impact of tilt angle, working distance, and detector distance on the Kikuchi pattern quality were investigated in a cold-field emission scanning electron microscope (CFE-SEM). We demonstrated that t-EFSD is applicable for tilt angles ranging from -20° to -40°. Working distance (WD) should be optimized for each material by choosing the WD for which the EBSD camera screen illumination is the highest, as the number of detected electrons on the screen is directly dependent on the scattering angle. To take advantage of the best performances of the CFE-SEM, the EBSD camera should be close to the sample and oriented towards the bottom to increase forward scattered electron collection efficiency. However, specimen chamber cluttering and beam/mechanical drift are important limitations in the CFE-SEM used in this work. Finally, the importance of t-EFSD in materials science characterization was illustrated through three examples of phase identification and orientation mapping. © Wiley Periodicals, Inc.
Logistics systems optimization under competition
DEFF Research Database (Denmark)
Choi, Tsan Ming; Govindan, Kannan; Ma, Lijun
2015-01-01
Nowadays, optimization on logistics and supply chain systems is a crucial and critical issue in industrial and systems engineering. Important areas of logistics and supply chain systems include transportation control, inventory management, and facility location planning. Under a competitive market...
Statistical optimization of process parameters for the production of ...
African Journals Online (AJOL)
In this study, optimization of process parameters such as moisture content, incubation temperature and initial pH (fixed) for the improvement of citric acid production from oil palm empty fruit bunches through solid state bioconversion was carried out using traditional one-factor-at-a-time (OFAT) method and response surface ...
Optimization of process parameters for synthesis of silica–Ni ...
Indian Academy of Sciences (India)
Optimization of process parameters for synthesis of silica–Ni nanocomposite by design of experiment ... Sol–gel; Ni; design of experiments; nanocomposites. ... Jadavpur University, Kolkata 700 032, India; School of Material Science and Nano-Technology, Jadavpur University, Kolkata 700 032, India; Rustech Products Pvt.
Optimization of physico-chemical and nutritional parameters for ...
African Journals Online (AJOL)
Optimization of physico-chemical and nutritional parameters for pullulan production by a mutant of thermotolerant Aureobasidium pullulans, in fed batch ... minutes, having killing rate of 70% level, produced 6 g l-1 higher pullulan as compared to the wild type without loosing thermotolerant and non-melanin producing ability.
Optimization of machining parameters of hard porcelain on a CNC ...
African Journals Online (AJOL)
In order to build up a relationship between quality and productivity, the present work focuses an optimized approach to establishing the multi-objective machining parameters and mathematical models for Pressure and Voltage on CNC turning machine (SINUMERIK802D). The Pressure and Voltage seem to be known as ...
Optimization of CNC end milling process parameters using PCA ...
African Journals Online (AJOL)
Optimization of CNC end milling process parameters using PCA-based Taguchi method. ... International Journal of Engineering, Science and Technology ... To meet the basic assumption of Taguchi method; in the present work, individual response correlations have been eliminated first by means of Principal Component ...
Multi responses optimization of wire EDM process parameters using ...
African Journals Online (AJOL)
The wire EDM was known as for its better efficiency to machining hardest material and give precise and accurate result comparing to other machining process. The intent of this experimental paper is to optimize the machining parameters of Wire Electrical Discharge Machining (WEDM) on En45A Alloy Steel with the ...
Optimization of burnishing parameters and determination of select ...
Indian Academy of Sciences (India)
Optimization of burnishing parameters and determination of select surface characteristics in engineering materials. P RAVINDRA BABU1, K ANKAMMA2, T SIVA PRASAD3,. A V S RAJU4 and N ESWARA PRASAD5,∗. 1Mechanical Engineering Department, Gudlavalleru Engineering College,. Gudlavalleru 521 356, India.
Optimization of physical and biological parameters for transient ...
African Journals Online (AJOL)
STORAGESEVER
2009-08-18
Aug 18, 2009 ... Majid and Parveez (2007) optimized different physical and biological parameters for transient expression of. GUS and GFP reporter genes in oil palm through particle bombardment. Similar experiments were also conducted on selectable markers and reporter gene expressions in banana by Sreeramanan ...
Optimization of process parameter for synthesis of silicon quantum ...
Indian Academy of Sciences (India)
Home; Journals; Bulletin of Materials Science; Volume 36; Issue 3. Optimization of process parameter for synthesis of silicon quantum dots using low pressure chemical vapour deposition. Dipika Barbadikar Rashmi Gautam Sanjay Sahare Rajendra Patrikar Jatin Bhatt. Volume 36 Issue 3 June 2013 pp 483-490 ...
Network optimization including gas lift and network parameters under subsurface uncertainty
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)
2013-08-01
Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A
Optimal Control of Mechanical Systems
Directory of Open Access Journals (Sweden)
Vadim Azhmyakov
2007-01-01
Full Text Available In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some consistent numerical approximations of a multiobjective optimization problem to the initial optimal control problem. For solving the auxiliary problem, we propose an implementable numerical algorithm.
International Nuclear Information System (INIS)
Shishov, S.
1990-01-01
The model proposed helps to ground the quantitative evaluation of the maintenance parameters of the electrical equipment of the coal mine. In the process of simulation an assumption is made that no additional capital investments are needed - only the existing situation of the electric supply system is considered and the effect of the measures taken to increase its reliability. The model takes into account the annual operating expenses, the downtime costs, the power not supplied to the user due to breakdowns, the average total outage duration; the breakdowns intensity, the average time for serviceability restoration. The model is based on the statistical data from the equipment operation for a period of 3 years, as well as on the economical standards acting in this mine. The outage expenses calculations of two units of the Bobov Dol mine are presented as an illustrative example of the model testing. The model is realized as a software product written on PL-1. It can also simulate the effect of the personnel number as well as the annual reserves of spare parts of various equipment's makes. 2 tabs, 6 refs
Rajora, Manik; Zou, Pan; Yang, Yao Guang; Fan, Zhi Wen; Chen, Hung Yi; Wu, Wen Chieh; Li, Beizhi; Liang, Steven Y
2016-01-01
It can be observed from the experimental data of different processes that different process parameter combinations can lead to the same performance indicators, but during the optimization of process parameters, using current techniques, only one of these combinations can be found when a given objective function is specified. The combination of process parameters obtained after optimization may not always be applicable in actual production or may lead to undesired experimental conditions. In this paper, a split-optimization approach is proposed for obtaining multiple solutions in a single-objective process parameter optimization problem. This is accomplished by splitting the original search space into smaller sub-search spaces and using GA in each sub-search space to optimize the process parameters. Two different methods, i.e., cluster centers and hill and valley splitting strategy, were used to split the original search space, and their efficiency was measured against a method in which the original search space is split into equal smaller sub-search spaces. The proposed approach was used to obtain multiple optimal process parameter combinations for electrochemical micro-machining. The result obtained from the case study showed that the cluster centers and hill and valley splitting strategies were more efficient in splitting the original search space than the method in which the original search space is divided into smaller equal sub-search spaces.
Beyond bixels: Generalizing the optimization parameters for intensity modulated radiation therapy
International Nuclear Information System (INIS)
Markman, Jerry; Low, Daniel A.; Beavis, Andrew W.; Deasy, Joseph O.
2002-01-01
Intensity modulated radiation therapy (IMRT) treatment planning systems optimize fluence distributions by subdividing the fluence distribution into rectangular bixels. The algorithms typically optimize the fluence intensity directly, often leading to fluence distributions with sharp discontinuities. These discontinuities may yield difficulties in delivery of the fluence distribution, leading to inaccurate dose delivery. We have developed a method for decoupling the bixel intensities from the optimization parameters; either by introducing optimization control points from which the bixel intensities are interpolated or by parametrizing the fluence distribution using basis functions. In either case, the number of optimization search parameters is reduced from the direct bixel optimization method. To illustrate the concept, the technique is applied to two-dimensional idealized head and neck treatment plans. The interpolation algorithms investigated were nearest-neighbor, linear and cubic spline, and radial basis functions serve as the basis function test. The interpolation and basis function optimization techniques were compared against the direct bixel calculation. The number of optimization parameters were significantly reduced relative to the bixel optimization, and this was evident in the reduction of computation time of as much as 58% from the full bixel optimization. The dose distributions obtained using the reduced optimization parameter sets were very similar to the full bixel optimization when examined by dose distributions, statistics, and dose-volume histograms. To evaluate the sensitivity of the fluence calculations to spatial misalignment caused either by delivery errors or patient motion, the doses were recomputed with a 1 mm shift in each beam and compared to the unshifted distributions. Except for the nearest-neighbor algorithm, the reduced optimization parameter dose distributions were generally less sensitive to spatial shifts than the bixel
Network synthesis and parameter optimization for vehicle suspension with inerter
Directory of Open Access Journals (Sweden)
Long Chen
2016-12-01
Full Text Available In order to design a comfortable-oriented vehicle suspension structure, the network synthesis method was utilized to transfer the problem into solving a timing robust control problem and determine the structure of “inerter–spring–damper” suspension. Bilinear Matrix Inequality was utilized to obtain the timing transfer function. Then, the transfer function of suspension system can be physically implemented by passive elements such as spring, damper, and inerter. By analyzing the sensitivity and quantum genetic algorithm, the optimized parameters of inerter–spring–damper suspension were determined. A quarter-car model was established. The performance of the inerter–spring–damper suspension was verified under random input. The simulation results manifested that the dynamic performance of the proposed suspension was enhanced in contrast with traditional suspension. The root mean square of vehicle body acceleration decreases by 18.9%. The inerter–spring–damper suspension can inhibit the vertical vibration within the frequency of 1–3 Hz effectively and enhance the performance of ride comfort significantly.
Topology optimized permanent magnet systems
DEFF Research Database (Denmark)
Bjørk, Rasmus; Bahl, Christian; Insinga, Andrea Roberto
2017-01-01
Topology optimization of permanent magnet systems consisting of permanent magnets, high permeability iron and air is presented. An implementation of topology optimization for magnetostatics is discussed and three examples are considered. The Halbach cylinder is topology optimized with iron...... and an increase of 15% in magnetic efficiency is shown. A topology optimized structure to concentrate a homogeneous field is shown to increase the magnitude of the field by 111%. Finally, a permanent magnet with alternating high and low field regions is topology optimized and a ΛcoolΛcool figure of merit of 0...
Optimization of power system operation
Zhu, Jizhong
2015-01-01
This book applies the latest applications of new technologies topower system operation and analysis, including new and importantareas that are not covered in the previous edition. Optimization of Power System Operation covers both traditional andmodern technologies, including power flow analysis, steady-statesecurity region analysis, security constrained economic dispatch,multi-area system economic dispatch, unit commitment, optimal powerflow, smart grid operation, optimal load shed, optimalreconfiguration of distribution network, power system uncertaintyanalysis, power system sensitivity analysis, analytic hierarchicalprocess, neural network, fuzzy theory, genetic algorithm,evolutionary programming, and particle swarm optimization, amongothers. New topics such as the wheeling model, multi-areawheeling, the total transfer capability computation in multipleareas, are also addressed. The new edition of this book continues to provide engineers andac demics with a complete picture of the optimization of techn...
Energy Technology Data Exchange (ETDEWEB)
Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA
2016-10-01
The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.
Optimization of reserve lithium thionyl chloride battery electrochemical design parameters
Energy Technology Data Exchange (ETDEWEB)
Doddapaneni, N.; Godshall, N.A.
1987-01-01
The performance of Reserve Lithium Thionyl Chloride (RLTC) batteries was optimized by conducting a parametric study of seven electrochemical parameters: electrode compression, carbon thickness, presence of catalyst, temperature, electrode limitation, discharge rate, and electrolyte acidity. Increasing electrode compression (from 0 to 15%) improved battery performance significantly (10% greater carbon capacity density). Although thinner carbon cathodes yielded less absolute capacity than did thicker cathodes, they did so with considerably higher volume efficiencies. The effect of these parameters, and their synergistic interactions, on electrochemical cell peformance is illustrated. 5 refs., 9 figs., 3 tabs.
Optimization of reserve lithium thionyl chloride battery electrochemical design parameters
Doddapaneni, N.; Godshall, N. A.
The performance of Reserve Lithium Thionyl Chloride (RLTC) batteries was optimized by conducting a parametric study of seven electrochemical parameters: electrode compression, carbon thickness, presence of catalyst, temperature, electrode limitation, discharge rate, and electrolyte acidity. Increasing electrode compression (from 0 to 15 percent) improved battery performance significantly (10 percent greater carbon capacity density). Although thinner carbon cathodes yielded less absolute capacity than did thicker cathodes, they did so with considerably higher volume efficiencies. The effect of these parameters, and their synergistic interactions, on electrochemical cell performance is illustrated.
Directory of Open Access Journals (Sweden)
Bin He
2014-01-01
Full Text Available In city traffic, it is important to improve transportation efficiency and the spacing of platoon should be shortened when crossing the street. The best method to deal with this problem is automatic control of vehicles. In this paper, a mathematical model is established for the platoon’s longitudinal movement. A systematic analysis of longitudinal control law is presented for the platoon of vehicles. However, the parameter calibration for the platoon model is relatively difficult because the platoon model is complex and the parameters are coupled with each other. In this paper, the particle swarm optimization method is introduced to effectively optimize the parameters of platoon. The proposed method effectively finds the optimal parameters based on simulations and makes the spacing of platoon shorter.
Embedded Systems Design: Optimization Challenges
DEFF Research Database (Denmark)
Pop, Paul
2005-01-01
-to-market, and reduce development and manufacturing costs. In this paper, the author introduces several embedded systems design problems, and shows how they can be formulated as optimization problems. Solving such challenging design optimization problems are the key to the success of the embedded systems design...... of designing such systems is becoming increasingly important and difficult at the same time. New automated design optimization techniques are needed, which are able to: successfully manage the complexity of embedded systems, meet the constraints imposed by the application domain, shorten the time...
Optimization of process parameters for friction stir processing (FSP ...
Indian Academy of Sciences (India)
An Al-5 wt% TiC composite was processed in situ using K2TiF6 and graphite in Al melt and subjected to FSP. Processing parameters for FSP were optimized to get a defect free stir zone and homogenize the particle distribution. It was found that a rotation speed > 800 rpm is needed. A rotation speed of 1000 rpm and a ...
Optimization of process parameters for friction stir processing (FSP ...
Indian Academy of Sciences (India)
Administrator
An Al-5 wt% TiC composite was processed in situ using K2TiF6 and graphite in Al melt and subjected to FSP. Processing .... Optimization of process parameters for friction stir processing of Al–TiC in situ composite. 573. Table 1. FSP process ... (Model 3367) at a strain rate of 10–3 s–1. 3. Results and discussion. 3.1 XRD ...
Real-time parameter optimization based on neural network for smart injection molding
Lee, H.; Liau, Y.; Ryu, K.
2018-03-01
The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.
PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
S. Kalaivani
2012-07-01
Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.
Analysis of optimization parameters in chest radiographs procedures
International Nuclear Information System (INIS)
Silva, Davi A.; Oliveira, Karinne M.; Alves, Douglas R.M.; Maia, Ana F.
2009-01-01
The risks associated with ionizing radiation became evident soon after the discovery of the X radiation. Therefore, any medical practices that make use of any type of ionizing radiation should be subjected to the basic principles of radiological protection: justification, optimization of protection and application of dose limits. In diagnostic radiology, it means to seek the lowest dose reasonably practicable, without compromising the image quality. The purpose of this project was to evaluate optimization parameters, specifically image quality, exposure levels and radiographs rejection rates, in radiological chest examinations. The image quality evaluation was performed using two forms, one for adults and other for children, based on European standards. By the results, we can conclude that the evaluated sector is not in agreement to the principle of optimization and this reality is not different from most health institutions. The entrance surface air kerma (K a,e ) results were below the national diagnostic reference levels. However, the several image quality parameters showed insufficient ratings and the film rejection rates were high. The lack of optimization generates poor quality images, causing inaccurate diagnostic reports, and increasing operating costs. Therefore, the research warns of the urgency of implementing Quality Control Assurance Program in all radiology services in the country. (author)
Robustness of dynamic systems with parameter uncertainties
Balemi, S; Truöl, W
1992-01-01
Robust Control is one of the fastest growing and promising areas of research today. In many practical systems there exist uncertainties which have to be considered in the analysis and design of control systems. In the last decade methods were developed for dealing with dynamic systems with unstructured uncertainties such as HOO_ and £I-optimal control. For systems with parameter uncertainties, the seminal paper of V. L. Kharitonov has triggered a large amount of very promising research. An international workshop dealing with all aspects of robust control was successfully organized by S. P. Bhattacharyya and L. H. Keel in San Antonio, Texas, USA in March 1991. We organized the second international workshop in this area in Ascona, Switzer land in April 1992. However, this second workshop was restricted to robust control of dynamic systems with parameter uncertainties with the objective to concentrate on some aspects of robust control. This book contains a collection of papers presented at the International W...
An Optimal Lower Eigenvalue System
Directory of Open Access Journals (Sweden)
Yingfan Liu
2011-01-01
Full Text Available An optimal lower eigenvalue system is studied, and main theorems including a series of necessary and suffcient conditions concerning existence and a Lipschitz continuity result concerning stability are obtained. As applications, solvability results to some von-Neumann-type input-output inequalities, growth, and optimal growth factors, as well as Leontief-type balanced and optimal balanced growth paths, are also gotten.
Optimizing the processing parameters for modular production of ...
African Journals Online (AJOL)
. For a PCB processing system, the most important processing parameters that could impart on the quality and cost of the board are temperature of processing solutions and time duration of each processing stage. An evaluation of the ...
Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology
Directory of Open Access Journals (Sweden)
Rupert Faltermeier
2015-01-01
Full Text Available Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP and intracranial pressure (ICP. Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP, with the outcome of the patients represented by the Glasgow Outcome Scale (GOS. For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.
A Combined Method in Parameters Optimization of Hydrocyclone
Directory of Open Access Journals (Sweden)
Jing-an Feng
2016-01-01
Full Text Available To achieve efficient separation of calcium hydroxide and impurities in carbide slag by using hydrocyclone, the physical granularity property of carbide slag, hydrocyclone operation parameters for slurry concentration, and the slurry velocity inlet are designed to be optimized. The optimization methods are combined with the Design of Experiment (DOE method and the Computational Fluid Dynamics (CFD method. Based on Design Expert software, the central composite design (CCD with three factors and five levels amounting to five groups of 20 test responses was constructed, and the experiments were performed by numerical simulation software FLUENT. Through the analysis of variance deduced from numerical simulation experiment results, the regression equations of pressure drop, overflow concentration, purity, and separation efficiencies of two solid phases were, respectively, obtained. The influences of factors were analyzed by the responses, respectively. Finally, optimized results were obtained by the multiobjective optimization method through the Design Expert software. Based on the optimized conditions, the validation test by numerical simulation and separation experiment were separately proceeded. The results proved that the combined method could be efficiently used in studying the hydrocyclone and it has a good performance in application engineering.
Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A
2017-01-01
In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.
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.
Directory of Open Access Journals (Sweden)
Damilola Isaac Adebiyi
2016-06-01
Full Text Available The cold spray coating process involves many process parameters which make the process very complex, and highly dependent and sensitive to small changes in these parameters. This results in a small operational window of the parameters. Consequently, mathematical optimization of the process parameters is key, not only to achieving deposition but also improving the coating quality. This study focuses on the mathematical identification and experimental justification of the optimum process parameters for cold spray coating of titanium alloy with silicon carbide (SiC. The continuity, momentum and the energy equations governing the flow through the low-pressure cold spray nozzle were solved by introducing a constitutive equation to close the system. This was used to calculate the critical velocity for the deposition of SiC. In order to determine the input temperature that yields the calculated velocity, the distribution of velocity, temperature, and pressure in the cold spray nozzle were analyzed, and the exit values were predicted using the meshing tool of Solidworks. Coatings fabricated using the optimized parameters and some non-optimized parameters are compared. The coating of the CFD-optimized parameters yielded lower porosity and higher hardness.
Xue, Dingyü; Li, Tingxue
2017-04-27
The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Optimizing electrical distribution systems
International Nuclear Information System (INIS)
Scott, W.G.
1990-01-01
Electrical utility distribution systems are in the middle of an unprecedented technological revolution in planning, design, maintenance and operation. The prime movers of the revolution are the major economic shifts that affect decision making. The major economic influence on the revolution is the cost of losses (technical and nontechnical). The vehicle of the revolution is the computer, which enables decision makers to examine alternatives in greater depth and detail than their predecessors could. The more important elements of the technological revolution are: system planning, computers, load forecasting, analytical systems (primary systems, transformers and secondary systems), system losses and coming technology. The paper is directed towards the rather unique problems encountered by engineers of utilities in developing countries - problems that are being solved through high technology, such as the recent World Bank-financed engineering computer system for Sri Lanka. This system includes a DEC computer, digitizer, plotter and engineering software to model the distribution system via a digitizer, analyse the system and plot single-line diagrams. (author). 1 ref., 4 tabs., 6 figs
Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine
Directory of Open Access Journals (Sweden)
Bambang Wahono
2014-01-01
Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.
Optimization of nuclear safety systems
International Nuclear Information System (INIS)
Beninson, D.; Gonzalez, A.J.
1981-01-01
The paper presents an approach for selecting the level of ambition of nuclear safety by a process of optimization based on cost-benefit considerations. Optimization has been incorporated as a requirement for radiation protection, to keep doses ''as low as reasonably achievable''. In radiation protection, optimization takes account of the costs of protection and the costs of the detriment, minimizing the sum of both. Optimization of a nuclear safety system could conceptually treat similarly the cost of potential damages from nuclear accidents and the cost associated with achieving a given level of safety. Within the above framework a method of optimizing the design of nuclear safety systems is presented, and a simple case of redundancy by output voting techniques is given. (author)
Optimal correction and design parameter search by modern methods of rigorous global optimization
International Nuclear Information System (INIS)
Makino, K.; Berz, M.
2011-01-01
Frequently the design of schemes for correction of aberrations or the determination of possible operating ranges for beamlines and cells in synchrotrons exhibit multitudes of possibilities for their correction, usually appearing in disconnected regions of parameter space which cannot be directly qualified by analytical means. In such cases, frequently an abundance of optimization runs are carried out, each of which determines a local minimum depending on the specific chosen initial conditions. Practical solutions are then obtained through an often extended interplay of experienced manual adjustment of certain suitable parameters and local searches by varying other parameters. However, in a formal sense this problem can be viewed as a global optimization problem, i.e. the determination of all solutions within a certain range of parameters that lead to a specific optimum. For example, it may be of interest to find all possible settings of multiple quadrupoles that can achieve imaging; or to find ahead of time all possible settings that achieve a particular tune; or to find all possible manners to adjust nonlinear parameters to achieve correction of high order aberrations. These tasks can easily be phrased in terms of such an optimization problem; but while mathematically this formulation is often straightforward, it has been common belief that it is of limited practical value since the resulting optimization problem cannot usually be solved. However, recent significant advances in modern methods of rigorous global optimization make these methods feasible for optics design for the first time. The key ideas of the method lie in an interplay of rigorous local underestimators of the objective functions, and by using the underestimators to rigorously iteratively eliminate regions that lie above already known upper bounds of the minima, in what is commonly known as a branch-and-bound approach. Recent enhancements of the Differential Algebraic methods used in particle
DESIGN OPTIMIZATION OF ROTOR-BEARING SYSTEMS
Directory of Open Access Journals (Sweden)
Hamit SARUHAN
2003-03-01
Full Text Available This paper presents a brief study of the information from the published literature and author's works regarding rotor-bearing systems analysis with respect to optimization. The main goal of this work is to motivate and give an idea to designers who are willing to deal with optimization of rotor-bearing sytems. The results obtained and presented in this study are to provide a comparison with numerical optimum design methods such as gradientbased method, and to show the potential of genetic algorithms in optimization of rotor-bearing systems. Genetic algorithms have been used as optimization problem solving techniques. They are parameter search procedures based on the idea of natural selection and genetics. These robust methods have increasingly recognized and applied in many applications.
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
Energy Technology Data Exchange (ETDEWEB)
Lynch, Vickie E.; Borreguero, Jose M. [Neutron Data Analysis & Visualization Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 (United States); Bhowmik, Debsindhu [Computational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 (United States); Ganesh, Panchapakesan; Sumpter, Bobby G. [Center for Nanophase Material Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 (United States); Computational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 (United States); Proffen, Thomas E. [Neutron Data Analysis & Visualization Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 (United States); Goswami, Monojoy, E-mail: goswamim@ornl.gov [Center for Nanophase Material Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 (United States); Computational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 (United States)
2017-07-01
Graphical abstract: - Highlights: • An automated workflow to optimize force-field parameters. • Used the workflow to optimize force-field parameter for a system containing nanodiamond and tRNA. • The mechanism relies on molecular dynamics simulation and neutron scattering experimental data. • The workflow can be generalized to any other experimental and simulation techniques. - Abstract: Large-scale simulations and data analysis are often required to explain neutron scattering experiments to establish a connection between the fundamental physics at the nanoscale and data probed by neutrons. However, to perform simulations at experimental conditions it is critical to use correct force-field (FF) parameters which are unfortunately not available for most complex experimental systems. In this work, we have developed a workflow optimization technique to provide optimized FF parameters by comparing molecular dynamics (MD) to neutron scattering data. We describe the workflow in detail by using an example system consisting of tRNA and hydrophilic nanodiamonds in a deuterated water (D{sub 2}O) environment. Quasi-elastic neutron scattering (QENS) data show a faster motion of the tRNA in the presence of nanodiamond than without the ND. To compare the QENS and MD results quantitatively, a proper choice of FF parameters is necessary. We use an efficient workflow to optimize the FF parameters between the hydrophilic nanodiamond and water by comparing to the QENS data. Our results show that we can obtain accurate FF parameters by using this technique. The workflow can be generalized to other types of neutron data for FF optimization, such as vibrational spectroscopy and spin echo.
Optimization of vibratory welding process parameters using response surface methodology
Energy Technology Data Exchange (ETDEWEB)
Singh, Pravin Kumar; Kumar, S. Deepak; Patel, D.; Prasad, S. B. [National Institute of Technology Jamshedpur, Jharkhand (India)
2017-05-15
The current investigation was carried out to study the effect of vibratory welding technique on mechanical properties of 6 mm thick butt welded mild steel plates. A new concept of vibratory welding technique has been designed and developed which is capable to transfer vibrations, having resonance frequency of 300 Hz, into the molten weld pool before it solidifies during the Shielded metal arc welding (SMAW) process. The important process parameters of vibratory welding technique namely welding current, welding speed and frequency of the vibrations induced in molten weld pool were optimized using Taguchi’s analysis and Response surface methodology (RSM). The effect of process parameters on tensile strength and hardness were evaluated using optimization techniques. Applying RSM, the effect of vibratory welding parameters on tensile strength and hardness were obtained through two separate regression equations. Results showed that, the most influencing factor for the desired tensile strength and hardness is frequency at its resonance value, i.e. 300 Hz. The micro-hardness and microstructures of the vibratory welded joints were studied in detail and compared with those of conventional SMAW joints. Comparatively, uniform and fine grain structure has been found in vibratory welded joints.
Parameter estimation for chaotic systems using improved bird swarm algorithm
Xu, Chuangbiao; Yang, Renhuan
2017-12-01
Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.
The Structural Optimization System CAOS
DEFF Research Database (Denmark)
Rasmussen, John
1990-01-01
CAOS is a system for structural shape optimization. It is closely integrated in a Computer Aided Design environment and controlled entirely from the CAD-system AutoCAD. The mathematical foundation of the system is briefly presented and a description of the CAD-integration strategy is given together...
Optimal time window for measurement of renal output parameters.
Kuyvenhoven, Jacob D; Ham, Hamphrey R; Piepsz, Amy
2002-01-01
Although normalised residual activity (NORA) and output efficiency (OE) are usually measured at a fixed time t, their dependency on t may affect the prediction of mean transit time (MTT). This study aimed to evaluate their degree of dependency on t and to determine an optimal time of measurement by assessment of their relationship with MTT for various times t. A simulation model generated 232 cortical renograms by convolving one plasma disappearance curve with 232 created cortical retention functions. The results show that considerable changes are observed for NORA and OE, depending on the time of measurement t. The choice of this time significantly influences the predictive value of these parameters for estimating MTT. The optimal time for measurement of NORA and OE should be close to the MTT, at the moment when emptying takes place. In the clinical practice, it should be adapted to the clinical problem under investigation.
Optimization of a wearable power system
Energy Technology Data Exchange (ETDEWEB)
Kovacevic, I.; Round, S. D.; Kolar, J. W.; Boulouchos, K.
2008-07-01
In this paper the optimization of wearable power system comprising of an internal combustion engine, motor/generator, inverter/rectifier, Li-battery pack, DC/DC converters, and controller is performed. The Wearable Power System must have the capability to supply an average 20 W for 4 days with peak power of 200 W and have a system weight less then 4 kg. The main objectives are to select the engine, fuel and battery type, to match the weight of fuel and the number of battery cells, to find the optimal working point of engine and minimizing the system weight. The minimization problem is defined in Matlab as a nonlinear constrained optimization task. The optimization procedure returns the optimal system design parameters: the Li-polymer battery with eight cells connected in series for a 28 V DC output voltage, the selection of gasoline/oil fuel mixture and the optimal engine working point of 12 krpm for a 4.5 cm{sup 3} 4-stroke engine. (author)
Optimization of magnetic parameters for toggle magnetoresistance random access memory
International Nuclear Information System (INIS)
Wang Shengyuan; Fujiwara, Hideo
2005-01-01
The magnetic parameters of the synthetic antiferromagnetic (SAF) elements for toggle-mode magnetoresistance random access memories (Toggle-MRAMs) have been optimized using the critical field curves obtained by analytical method with the aid of numerical calculations, to maximize the operating field margin taking into account the required memory density, storage lifetime, half-select disturb robustness, and the available strength of operating field. The control of especially low-exchange coupling strength in the SAF in addition to the increase of the operating field has been found to be essential for the development of toggle-MRAM in near future
Optimization of some electrochemical etching parameters for cellulose derivatives
International Nuclear Information System (INIS)
Chowdhury, Annis; Gammage, R.B.
1978-01-01
Electrochemical etching of fast neutron induced recoil particle tracks in cellulose derivatives and other polymers provides an inexpensive and sensitive means of fast neutron personnel dosimetry. A study of the shape, clarity, and size of the tracks in Transilwrap polycarbonate indicated that the optimum normality of the potassium hydroxide etching solution is 9 N. Optimizations have also been attempted for cellulose nitrate, triacetate, and acetobutyrate with respect to such electrochemical etching parameters as frequency, voltage gradient, and concentration of the etching solution. The measurement of differential leakage currents between the undamaged and the neutron damaged foils aided in the selection of optimum frequencies. (author)
Maximum gradient method for optimization of some reactor operating parameters
International Nuclear Information System (INIS)
Miasnikov, A.
1976-03-01
The method and the algorithm ensuing therefrom are described for the determination of the optimum operating state of a reactor. The optimum operating state is considered to be the extreme of the selected functional of the radial power distribution. The functional extreme is determined numerically, using a method which is one of the possible variants of the maximum gradient method. The radial distribution of the neutron absorption in regulating rods and the fuel element burnup are considered to be the variable parameters used in the optimization. (author)
Power Saving Optimization for Linear Collider Interaction Region Parameters
International Nuclear Information System (INIS)
Seryi, Andrei
2009-01-01
Optimization of Interaction Region parameters of a TeV energy scale linear collider has to take into account constraints defined by phenomena such as beam-beam focusing forces, beamstrahlung radiation, and hour-glass effect. With those constraints, achieving a desired luminosity of about 2E34 would require use of e + e - beams with about 10 MW average power. Application of the 'travelling focus' regime may allow the required beam power to be reduced by at least a factor of two, helping reduce the cost of the collider, while keeping the beamstrahlung energy loss reasonably low. The technique is illustrated for the 500 GeV CM parameters of the International Linear Collider. This technique may also in principle allow recycling the e + e - beams and/or recuperation of their energy.
Degaussing System Design Optimization
Bekers, D.J.; Lepelaars, E.S.A.M.
2013-01-01
Steel ships with a magnetic signature requirement are equipped with a degaussing system to reduce their perceptibility for magnetic influence mines. To be able to reduce the magnetic signature accurately, a proper distribution of coils over the ship is essential. Finding the best distribution of
METHODS OF INTEGRATED OPTIMIZATION MAGLEV TRANSPORT SYSTEMS
Directory of Open Access Journals (Sweden)
A. Lasher
2013-09-01
Full Text Available Purpose. To demonstrate feasibility of the proposed integrated optimization of various MTS parameters to reduce capital investments as well as decrease any operational and maintenance expense. This will make use of MTS reasonable. At present, the Maglev Transport Systems (MTS for High-Speed Ground Transportation (HSGT almost do not apply. Significant capital investments, high operational and maintenance costs are the main reasons why Maglev Transport Systems (MTS are hardly currently used for the High-Speed Ground Transportation (HSGT. Therefore, this article justifies use of Theory of Complex Optimization of Transport (TCOT, developed by one of the co-authors, to reduce MTS costs. Methodology. According to TCOT, authors developed an abstract model of the generalized transport system (AMSTG. This model mathematically determines the optimal balance between all components of the system and thus provides the ultimate adaptation of any transport systems to the conditions of its application. To identify areas for effective use of MTS, by TCOT, the authors developed a dynamic model of distribution and expansion of spheres of effective use of transport systems (DMRRSEPTS. Based on this model, the most efficient transport system was selected for each individual track. The main estimated criterion at determination of efficiency of application of MTS is the size of the specific transportation tariff received from calculation of payback of total given expenses to a standard payback period or term of granting the credit. Findings. The completed multiple calculations of four types of MTS: TRANSRAPID, MLX01, TRANSMAG and TRANSPROGRESS demonstrated efficiency of the integrated optimization of the parameters of such systems. This research made possible expending the scope of effective usage of MTS in about 2 times. The achieved results were presented at many international conferences in Germany, Switzerland, United States, China, Ukraine, etc. Using MTS as an
Optimization of Neuro-Fuzzy System
Directory of Open Access Journals (Sweden)
M. Sarosa
2007-05-01
Full Text Available Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.
Optimization of multilayer neural network parameters for speaker recognition
Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka
2016-05-01
This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.
Worst-case tolerance optimization of antenna systems
DEFF Research Database (Denmark)
Schjær-Jacobsen, Hans
1980-01-01
The application of recently developed algorithms to antenna systems design is demonstrated by the worst-case tolerance optimization of linear broadside arrays, using both spacings and excitation coefficients as design parameters. The resulting arrays are optimally immunized against deviations...... of the design parameters from their nominal values....
Truss systems and shape optimization
Pricop, Mihai Victor; Bunea, Marian; Nedelcu, Roxana
2017-07-01
Structure optimization is an important topic because of its benefits and wide applicability range, from civil engineering to aerospace and automotive industries, contributing to a more green industry and life. Truss finite elements are still in use in many research/industrial codesfor their simple stiffness matrixand are naturally matching the requirements for cellular materials especially considering various 3D printing technologies. Optimality Criteria combined with Solid Isotropic Material with Penalization is the optimization method of choice, particularized for truss systems. Global locked structures areobtainedusinglocally locked lattice local organization, corresponding to structured or unstructured meshes. Post processing is important for downstream application of the method, to make a faster link to the CAD systems. To export the optimal structure in CATIA, a CATScript file is automatically generated. Results, findings and conclusions are given for two and three-dimensional cases.
Biohydrogen Production from Simple Carbohydrates with Optimization of Operating Parameters.
Muri, Petra; Osojnik-Črnivec, Ilja Gasan; Djinovič, Petar; Pintar, Albin
2016-01-01
Hydrogen could be alternative energy carrier in the future as well as source for chemical and fuel synthesis due to its high energy content, environmentally friendly technology and zero carbon emissions. In particular, conversion of organic substrates to hydrogen via dark fermentation process is of great interest. The aim of this study was fermentative hydrogen production using anaerobic mixed culture using different carbon sources (mono and disaccharides) and further optimization by varying a number of operating parameters (pH value, temperature, organic loading, mixing intensity). Among all tested mono- and disaccharides, glucose was shown as the preferred carbon source exhibiting hydrogen yield of 1.44 mol H(2)/mol glucose. Further evaluation of selected operating parameters showed that the highest hydrogen yield (1.55 mol H(2)/mol glucose) was obtained at the initial pH value of 6.4, T=37 °C and organic loading of 5 g/L. The obtained results demonstrate that lower hydrogen yield at all other conditions was associated with redirection of metabolic pathways from butyric and acetic (accompanied by H(2) production) to lactic (simultaneous H(2) production is not mandatory) acid production. These results therefore represent an important foundation for the optimization and industrial-scale production of hydrogen from organic substrates.
Optimization of seismic isolation systems via harmony search
Melih Nigdeli, Sinan; Bekdaş, Gebrail; Alhan, Cenk
2014-11-01
In this article, the optimization of isolation system parameters via the harmony search (HS) optimization method is proposed for seismically isolated buildings subjected to both near-fault and far-fault earthquakes. To obtain optimum values of isolation system parameters, an optimization program was developed in Matlab/Simulink employing the HS algorithm. The objective was to obtain a set of isolation system parameters within a defined range that minimizes the acceleration response of a seismically isolated structure subjected to various earthquakes without exceeding a peak isolation system displacement limit. Several cases were investigated for different isolation system damping ratios and peak displacement limitations of seismic isolation devices. Time history analyses were repeated for the neighbouring parameters of optimum values and the results proved that the parameters determined via HS were true optima. The performance of the optimum isolation system was tested under a second set of earthquakes that was different from the first set used in the optimization process. The proposed optimization approach is applicable to linear isolation systems. Isolation systems composed of isolation elements that are inherently nonlinear are the subject of a future study. Investigation of the optimum isolation system parameters has been considered in parametric studies. However, obtaining the best performance of a seismic isolation system requires a true optimization by taking the possibility of both near-fault and far-fault earthquakes into account. HS optimization is proposed here as a viable solution to this problem.
Optimization theory for large systems
Lasdon, Leon S
2002-01-01
Important text examines most significant algorithms for optimizing large systems and clarifying relations between optimization procedures. Much data appear as charts and graphs and will be highly valuable to readers in selecting a method and estimating computer time and cost in problem-solving. Initial chapter on linear and nonlinear programming presents all necessary background for subjects covered in rest of book. Second chapter illustrates how large-scale mathematical programs arise from real-world problems. Appendixes. List of Symbols.
Distributed optimization system and method
Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.
2003-06-10
A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.
LIFE CYCLE ASSESSMENT IN HEALTHCARE SYSTEM OPTIMIZATION. INTRODUCTION
Directory of Open Access Journals (Sweden)
V. Sarancha
2015-03-01
Full Text Available Article describes the life cycle assessment method and introduces opportunities for method performance in healthcare system settings. LSA draws attention to careful use of resources, environmental, human and social responsibility. Modelling of environmental and technological inputs allows optimizing performance of the system. Various factors and parameters that may influence effectiveness of different sectors in healthcare system are detected. Performance optimization of detected parameters could lead to better system functioning, higher patient safety, economic sustainability and reduce resources consumption.
Tower controller surveillance system parameters.
1972-03-01
A brief study of airport ground traffic control surveillance parameters has been conducted. The study addressed the following questions by means of a set of simple experiments: (1) Can vehicle ID be displayed in a suitable format; (2) What size displ...
Systems' optimization: Achieving the balance
Kraus, Peter
1994-04-01
Fuel cells for stationary power generation applications are being pursued on a large scale worldwide in an effort to achieve commercialization before the turn of the century. Some aspects of system optimization are discussed illustrating the influence of basic system design possibilities. Design variants investigated include alternatives for anode and cathode gas supply and gas recycling, methods to achieve self-sufficiency on water for the reforming of natural gas, and recovery of unspent fuel from the anode exhaust. Especially in small systems for decentralized applications, e.g., industrial cogeneration, system simplification is decisive to bring down the capital cost of the balance-of-plant. Trade-offs between system complexity and efficiency are possible to optimize the economy. In large plants, high-temperature fuel cell can be supplemented with bottoming cycles for best fuel utilization. Gas turbines and steam turbines can be evaluated, having strong influence on the system design pressures and, therefore, system cost.
Towards automatic parameter tuning of stream processing systems
Bilal, Muhammad
2017-09-27
Optimizing the performance of big-data streaming applications has become a daunting and time-consuming task: parameters may be tuned from a space of hundreds or even thousands of possible configurations. In this paper, we present a framework for automating parameter tuning for stream-processing systems. Our framework supports standard black-box optimization algorithms as well as a novel gray-box optimization algorithm. We demonstrate the multiple benefits of automated parameter tuning in optimizing three benchmark applications in Apache Storm. Our results show that a hill-climbing algorithm that uses a new heuristic sampling approach based on Latin Hypercube provides the best results. Our gray-box algorithm provides comparable results while being two to five times faster.
Murthy, Pappu L. N.; Naghipour Ghezeljeh, Paria; Bednarcyk, Brett A.
2018-01-01
This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is
Multivariate optimization of production systems
International Nuclear Information System (INIS)
Carroll, J.A.; Horne, R.N.
1992-01-01
This paper reports that mathematically, optimization involves finding the extreme values of a function. Given a function of several variables, Z = ∫(rvec x 1 , rvec x 2 ,rvec x 3 ,→x n ), an optimization scheme will find the combination of these variables that produces an extreme value in the function, whether it is a minimum or a maximum value. Many examples of optimization exist. For instance, if a function gives and investor's expected return on the basis of different investments, numerical optimization of the function will determine the mix of investments that will yield the maximum expected return. This is the basis of modern portfolio theory. If a function gives the difference between a set of data and a model of the data, numerical optimization of the function will produce the best fit of the model to the data. This is the basis for nonlinear parameter estimation. Similar examples can be given for network analysis, queuing theory, decision analysis, etc
Multiobjective insensitive design of airplane control systems with uncertain parameters
Schy, A. A.; Giesy, D. P.
1981-01-01
A multiobjective computer-aided design algorithm has been developed which minimizes the sensitivity of the design objectives to uncertainties in system parameters. The more important uncertain parameters are described by a gaussian random vector with known covariance matrix, and a vector sensitivity objective function is defined as the probabilities that the design objectives will violate specified requirements constraints. Control system parameters are found which minimize the sensitivity vector in a Pareto-optimal sense, using constrained minimization algorithms. Example results are shown for lateral stability augmentation system (SAS) design for three Shuttle flight conditions.
Air conditioning with methane: Efficiency and economics optimization parameters
International Nuclear Information System (INIS)
Mastrullo, R.; Sasso, M.; Sibilio, S.; Vanoli, R.
1992-01-01
This paper presents an efficiency and economics evaluation method for methane fired cooling systems. Focus is on direct flame two staged absorption systems and alternative engine driven compressor sets. Comparisons are made with conventional vapour compression plants powered by electricity supplied by the national grid. A first and second law based thermodynamics analysis is made in which fuel use coefficients and exergy yields are determined. The economics analysis establishes annual energy savings, unit cooling energy production costs, payback periods and economics/efficiency optimization curves useful for preliminary feasibility studies
A parameter estimation for DC servo motor by using optimization process
International Nuclear Information System (INIS)
Arjoni Amir
2010-01-01
Modeling and simulation parameters of DC servo motor using Matlab Simulink software have been done. The objective to define the DC servo motor parameter estimation is to get DC servo motor parameter values (B, La, Ra, Km, J) which are significant value that can be used for actuation process of control systems. In the analysis of control systems DC the servo motor expressed by transfer function equation to make faster to be analyzed as a component of the actuator. To obtain the data model parameters and initial conditions of the DC servo motors is then carried out the processor modeling and simulation in which the DC servo motor combined with other components. To obtain preliminary data of the DC servo motor parameters as estimated venue, it is obtained from the data factory of the DC servo motor. The initial data parameters of the DC servo motor are applied for the optimization process by using nonlinear least square algorithm and minimize the cost function value so that the DC servo motors parameter values are obtained significantly. The result of the optimization process of the DC servo motor parameter values are B = 0.039881, J= 1.2608e-007, Km = 0.069648, La = 2.3242e-006 and Ra = 1.8837. (author)
Parameter uncertainties in the design and optimization of cantilever piezoelectric energy harvesters
Franco, V. R.; Varoto, P. S.
2017-09-01
A crucial issue in piezoelectric energy harvesting is the efficiency of the mechanical to electrical conversion process. Several techniques have been investigated in order to obtain a set of optimum design parameters that will lead to the best performance of the harvester in terms of electrical power generation. Once an optimum design is reached it is also important to consider uncertainties in the selected parameters that in turn can lead to loss of performance in the energy conversion process. The main goal of this paper is to perform a comprehensive discussion of the effects of multi-parameter aleatory uncertainties on the performance and design optimization of a given energy harvesting system. For that, a typical energy harvester consisting of a cantilever beam carrying a tip mass and partially covered by piezoelectric layers on top and bottom surfaces is considered. A distributed parameter electromechanical modal of the harvesting system is formulated and validated through experimental tests. First, the SQP (Sequential Quadratic Planning) optimization is employed to obtain an optimum set of parameters that will lead to best performance of the harvester. Second, once the optimum harvester configuration is found random perturbations are introduced in the key parameters and Monte Carlo simulations are performed to investigate how these uncertainties propagate and affect the performance of the device studied. Numerically simulated results indicate that small variations in some design parameters can cause a significant variation in the output electrical power, what strongly suggests that uncertainties must be accounted for in the design of beam energy harvesting systems.
Data Mining and Optimization Tools for Developing Engine Parameters Tools
Dhawan, Atam P.
1998-01-01
This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. Tricia Erhardt and I studied the problem domain for developing an Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy, datasets. From the study and discussion with NASA LeRC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of tile data for GA based multi-resolution optimal search.
Optimization of resistance spot welding parameters for microalloyed steel sheets
Viňáš, Ján; Kaščák, Ľuboš; Greš, Miroslav
2016-11-01
The paper presents the results of resistance spot welding of hot-dip galvanized microalloyed steel sheets used in car body production. The spot welds were made with various welding currents and welding time values, but with a constant pressing force of welding electrodes. The welding current and welding time are the dominant characteristics in spot welding that affect the quality of spot welds, as well as their dimensions and load-bearing capacity. The load-bearing capacity of welded joints was evaluated by tensile test according to STN 05 1122 standard and dimensions and inner defects were evaluated by metallographic analysis by light optical microscope. Thewelding parameters of investigated microalloyed steel sheets were optimized for resistance spot welding on the pneumatic welding machine BPK 20.
OPTIMIZATION OF OPERATION PARAMETERS OF 80-KEV ELECTRON GUN
Directory of Open Access Journals (Sweden)
JEONG DONG KIM
2014-06-01
As a first step, the electron generator of an 80-keV electron gun was manufactured. In order to produce the high beam power from electron linear accelerator, a proper beam current is required form the electron generator. In this study, the beam current was measured by evaluating the performance of the electron generator. The beam current was determined by five parameters: high voltage at the electron gun, cathode voltage, pulse width, pulse amplitude, and bias voltage at the grid. From the experimental results under optimal conditions, the high voltage was determined to be 80 kV, the pulse width was 500 ns, and the cathode voltage was from 4.2 V to 4.6 V. The beam current was measured as 1.9 A at maximum. These results satisfy the beam current required for the operation of an electron linear accelerator.
Microbial alkaline proteases: Optimization of production parameters and their properties
Directory of Open Access Journals (Sweden)
Kanupriya Miglani Sharma
2017-06-01
Full Text Available Proteases are hydrolytic enzymes capable of degrading proteins into small peptides and amino acids. They account for nearly 60% of the total industrial enzyme market. Proteases are extensively exploited commercially, in food, pharmaceutical, leather and detergent industry. Given their potential use, there has been renewed interest in the discovery of proteases with novel properties and a constant thrust to optimize the enzyme production. This review summarizes a fraction of the enormous reports available on various aspects of alkaline proteases. Diverse sources for isolation of alkaline protease producing microorganisms are reported. The various nutritional and environmental parameters affecting the production of alkaline proteases in submerged and solid state fermentation are described. The enzymatic and physicochemical properties of alkaline proteases from several microorganisms are discussed which can help to identify enzymes with high activity and stability over extreme pH and temperature, so that they can be developed for industrial applications.
Acquisition system of tandem injector parameters
International Nuclear Information System (INIS)
Decourt, M.
1986-01-01
The system centralizes all the parameters belonging to the accelerator injector. The acquisition center system reinforces an original device made of cameras and video receivers. Besides giving access to all the parameters of the ion source, the new system allows, in the ''OSCILLO'' mode, to visualize in real time any channel on the oscilloscope [fr
Optimization of control parameters of a hot cold controller by means of Simplex type methods
Porte, C.; Caron-Poussin, M.; Carot, S.; Couriol, C.; Moreno, M. Martin; Delacroix, A.
1997-01-01
This paper describes a hot/cold controller for regulating crystallization operations. The system was identified with a common method (the Broida method) and the parameters were obtained by the Ziegler-Nichols method. The paper shows that this empirical method will only allow a qualitative approach to regulation and that, in some instances, the parameters obtained are unreliable and therefore cannot be used to cancel variations between the set point and the actual values. Optimization methods were used to determine the regulation parameters and solve this identcation problem. It was found that the weighted centroid method was the best one. PMID:18924791
High Temperature Epoxy Foam: Optimization of Process Parameters
Directory of Open Access Journals (Sweden)
Samira El Gazzani
2016-06-01
Full Text Available For many years, reduction of fuel consumption has been a major aim in terms of both costs and environmental concerns. One option is to reduce the weight of fuel consumers. For this purpose, the use of a lightweight material based on rigid foams is a relevant choice. This paper deals with a new high temperature epoxy expanded material as substitution of phenolic resin, classified as potentially mutagenic by European directive Reach. The optimization of thermoset foam depends on two major parameters, the reticulation process and the expansion of the foaming agent. Controlling these two phenomena can lead to a fully expanded and cured material. The rheological behavior of epoxy resin is studied and gel time is determined at various temperatures. The expansion of foaming agent is investigated by thermomechanical analysis. Results are correlated and compared with samples foamed in the same temperature conditions. The ideal foaming/gelation temperature is then determined. The second part of this research concerns the optimization of curing cycle of a high temperature trifunctional epoxy resin. A two-step curing cycle was defined by considering the influence of different curing schedules on the glass transition temperature of the material. The final foamed material has a glass transition temperature of 270 °C.
Optimizing Technology-Oriented Constructional Paramour's of complex dynamic systems
International Nuclear Information System (INIS)
Novak, S.M.
1998-01-01
Creating optimal vibro systems requires sequential solving of a few problems: selecting the basic pattern of dynamic actions, synthesizing the dynamic active systems, optimizing technological, technical, economic and design parameters. This approach is illustrated by an example of a high-efficiency vibro system synthesized for forming building structure components. When using only one single source to excite oscillations, resonance oscillations are imparted to the product to be formed in the horizontal and vertical planes. In order to obtain versatile and dynamically optimized parameters, a factor is introduced into the differential equations of the motion, accounting for the relationship between the parameters, which determine the frequency characteristics of the system and the parameter variation range. This results in obtaining non-sophisticated mathematical models of the system under investigation, convenient for optimization and for engineering design and calculations as well
International Nuclear Information System (INIS)
Gao, Hao
2016-01-01
For the treatment planning during intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), beam fluence maps can be first optimized via fluence map optimization (FMO) under the given dose prescriptions and constraints to conformally deliver the radiation dose to the targets while sparing the organs-at-risk, and then segmented into deliverable MLC apertures via leaf or arc sequencing algorithms. This work is to develop an efficient algorithm for FMO based on alternating direction method of multipliers (ADMM). Here we consider FMO with the least-square cost function and non-negative fluence constraints, and its solution algorithm is based on ADMM, which is efficient and simple-to-implement. In addition, an empirical method for optimizing the ADMM parameter is developed to improve the robustness of the ADMM algorithm. The ADMM based FMO solver was benchmarked with the quadratic programming method based on the interior-point (IP) method using the CORT dataset. The comparison results suggested the ADMM solver had a similar plan quality with slightly smaller total objective function value than IP. A simple-to-implement ADMM based FMO solver with empirical parameter optimization is proposed for IMRT or VMAT. (paper)
Laser Processing of Multilayered Thermal Spray Coatings: Optimal Processing Parameters
Tewolde, Mahder; Zhang, Tao; Lee, Hwasoo; Sampath, Sanjay; Hwang, David; Longtin, Jon
2017-12-01
Laser processing offers an innovative approach for the fabrication and transformation of a wide range of materials. As a rapid, non-contact, and precision material removal technology, lasers are natural tools to process thermal spray coatings. Recently, a thermoelectric generator (TEG) was fabricated using thermal spray and laser processing. The TEG device represents a multilayer, multimaterial functional thermal spray structure, with laser processing serving an essential role in its fabrication. Several unique challenges are presented when processing such multilayer coatings, and the focus of this work is on the selection of laser processing parameters for optimal feature quality and device performance. A parametric study is carried out using three short-pulse lasers, where laser power, repetition rate and processing speed are varied to determine the laser parameters that result in high-quality features. The resulting laser patterns are characterized using optical and scanning electron microscopy, energy-dispersive x-ray spectroscopy, and electrical isolation tests between patterned regions. The underlying laser interaction and material removal mechanisms that affect the feature quality are discussed. Feature quality was found to improve both by using a multiscanning approach and an optional assist gas of air or nitrogen. Electrically isolated regions were also patterned in a cylindrical test specimen.
Optimization of Robotic Spray Painting process Parameters using Taguchi Method
Chidhambara, K. V.; Latha Shankar, B.; Vijaykumar
2018-02-01
Automated spray painting process is gaining interest in industry and research recently due to extensive application of spray painting in automobile industries. Automating spray painting process has advantages of improved quality, productivity, reduced labor, clean environment and particularly cost effectiveness. This study investigates the performance characteristics of an industrial robot Fanuc 250ib for an automated painting process using statistical tool Taguchi’s Design of Experiment technique. The experiment is designed using Taguchi’s L25 orthogonal array by considering three factors and five levels for each factor. The objective of this work is to explore the major control parameters and to optimize the same for the improved quality of the paint coating measured in terms of Dry Film thickness(DFT), which also results in reduced rejection. Further Analysis of Variance (ANOVA) is performed to know the influence of individual factors on DFT. It is observed that shaping air and paint flow are the most influencing parameters. Multiple regression model is formulated for estimating predicted values of DFT. Confirmation test is then conducted and comparison results show that error is within acceptable level.
Real parameter optimization by an effective differential evolution algorithm
Directory of Open Access Journals (Sweden)
Ali Wagdy Mohamed
2013-03-01
Full Text Available This paper introduces an Effective Differential Evolution (EDE algorithm for solving real parameter optimization problems over continuous domain. The proposed algorithm proposes a new mutation rule based on the best and the worst individuals among the entire population of a particular generation. The mutation rule is combined with the basic mutation strategy through a linear decreasing probability rule. The proposed mutation rule is shown to promote local search capability of the basic DE and to make it faster. Furthermore, a random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme are merged to avoid stagnation and/or premature convergence. Additionally, the scaling factor and crossover of DE are introduced as uniform random numbers to enrich the search behavior and to enhance the diversity of the population. The effectiveness and benefits of the proposed modifications used in EDE has been experimentally investigated. Numerical experiments on a set of bound-constrained problems have shown that the new approach is efficient, effective and robust. The comparison results between the EDE and several classical differential evolution methods and state-of-the-art parameter adaptive differential evolution variants indicate that the proposed EDE algorithm is competitive with , and in some cases superior to, other algorithms in terms of final solution quality, efficiency, convergence rate, and robustness.
OPTIMIZATION OF DYEING PARAMETERS TO DYE COTTON WITH CARROT EXTRACTION
Directory of Open Access Journals (Sweden)
MIRALLES Verónica
2017-05-01
Full Text Available Natural dyes derived from flora and fauna are believed to be safe because of non-toxic, non-carcinogenic and biodegradable nature. Furthermore, natural dyes do not cause pollution and waste water problems. Natural dyes as well as synthetic dyes need the optimum parameters to get a good dyeing. On some occasions, It is necessary the use of mordants to increase the affinity between cellulose fiber and natural dye, but there are other conditions to optimize in the dyeing process, like time, temperature, auxiliary porducts, etc. In addition, the optimum conditions are different depends on the type of dye and the fiber nature. The aim of this work is the use of carrot extract to dye cotton fabric by exhaustion at diverse dyeing conditions. Diffferent dyeing processes were carried out to study the effect of pH condition and the temperature, using 7, 6 and 4 pH values and 95 ºC and 130ºC for an hour. As a result some images of dyed samples are shown. Moreover, to evaluate the colour of each sample CIELAB parameters are analysed obtained by reflexion spectrophotometre. The results showed that the temperature used has an important influence on the colour of the dyed sample.
Machining parameter optimization in turning process for sustainable manufacturing
Directory of Open Access Journals (Sweden)
S. G. Dambhare
2015-09-01
Full Text Available There is an increase in awareness about sustainable manufacturing process. Manufacturing industries are backbone of a country’s economy. Although it is important but there is a great concern about consumption of resources and waste creation. The primary aim of this study was to explore sustainability concern in turning process in an Indian machining industry. The effect of cutting parameters, Speed/Feed/Depth of Cut, the machining environment, Dry/MQL/Wet, and the type of cutting tool on sustainability factors under study were observed. Analysis of Variance (ANOVA was used to analyse the data obtained from experimentation in a small scale machining industry. The process is modelled mathematically using response surface methodology (RSM.The economic and environmental aspect like surface roughness, material removal rate and energy consumption were considered as sustainability factors. The model helps to understand the effect of the cutting parameters and conditions on surface finish, energy consumption, and material removal rate. The process was optimized for minimum power consumption considering environmental concern as prime importance. Studies suggest that the cutting environment and tool type influenced on the power consumption during turning process. Extended form of the proposed model could be useful to predict the environmental impact due to machining process, which would bring environmental concern into conventional machining.
Managing physicochemical parameters in compost systems to ...
African Journals Online (AJOL)
Physical, chemical and biological parameters were optimized during composting to enhance degradation of oil sludge. Mixtures of oil sludge, garden soil, poultry manure and the bulking agents were co-composted in static piles of about 1 m3 on wooden pallets overlaid with nylon fibre sheets. Temperature, moisture ...
Managing physicochemical parameters in compost systems to ...
African Journals Online (AJOL)
user
2014-02-12
Feb 12, 2014 ... Physical, chemical and biological parameters were optimized during composting to enhance degradation of oil sludge. Mixtures of oil sludge, garden soil, poultry manure and the bulking agents were co-composted in static piles of about 1 m3 on wooden pallets overlaid with nylon fibre sheets. Temperature ...
Directory of Open Access Journals (Sweden)
Tashkova Katerina
2011-10-01
Full Text Available Abstract Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA, particle-swarm optimization (PSO, and differential evolution (DE, as well as a local-search derivative-based algorithm 717 (A717 to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE clearly and significantly outperform the local derivative-based method (A717. Among the three meta-heuristics, differential evolution (DE performs best in terms of the objective function, i.e., reconstructing the output, and in terms of
2011-01-01
Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These
Using a 4D-Variational Method to Optimize Model Parameters in an Intermediate Coupled Model of ENSO
Gao, C.; Zhang, R. H.
2017-12-01
Large biases exist in real-time ENSO prediction, which is attributed to uncertainties in initial conditions and model parameters. Previously, a four dimentional variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation, written as Te=αTe×FTe (SL). The introduced parameter, αTe, represents the strength of the thermocline effect on sea surface temperature (SST; referred as the thermocline effect). A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having initial condition optimized only and having initial condition plus this additional model parameter optimized both are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameter and initial condition together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.
Gao, Chuan; Zhang, Rong-Hua; Wu, Xinrong; Sun, Jichang
2018-04-01
Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer ( T e), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as "the thermocline effect") is represented by an introduced parameter, α Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.
Importance theory for lumped-parameter systems
International Nuclear Information System (INIS)
Cady, K.B.; Kenton, M.A.; Ward, J.C.; Piepho, M.G.
1981-01-01
A general sensitivity theory has been developed for nonlinear lumped parameter system simulations. The point of departure is general perturbation theory for nonlinear systems. Importance theory as developed here allows the calculation of the sensitivity of a response function to any physical or design parameter; importance of any equation or term or physical effect in the system model on the response function; variance of the response function caused by the variances and covariances of all physical parameters; and approximate effect on the response function of missing physical phenomena or incorrect parameters
Q-Learning Multi-Objective Sequential Optimal Sensor Parameter Weights
Directory of Open Access Journals (Sweden)
Raquel Cohen
2016-04-01
Full Text Available The goal of our solution is to deliver trustworthy decision making analysis tools which evaluate situations and potential impacts of such decisions through acquired information and add efficiency for continuing mission operations and analyst information.We discuss the use of cooperation in modeling and simulation and show quantitative results for design choices to resource allocation. The key contribution of our paper is to combine remote sensing decision making with Nash Equilibrium for sensor parameter weighting optimization. By calculating all Nash Equilibrium possibilities per period, optimization of sensor allocation is achieved for overall higher system efficiency. Our tool provides insight into what are the most important or optimal weights for sensor parameters and can be used to efficiently tune those weights.
Embedded Systems Design: Optimization Challenges
DEFF Research Database (Denmark)
Pop, Paul
2005-01-01
Summary form only given. Embedded systems are everywhere: from alarm clocks to PDAs, from mobile phones to cars, almost all the devices we use are controlled by embedded systems. Over 99% of the microprocessors produced today are used in embedded systems, and recently the number of embedded systems...... in use has become larger than the number of humans on the planet. The complexity of embedded systems is growing at a very high pace and the constraints in terms of functionality, performance, low energy consumption, reliability, cost and time-to-market are getting tighter. Therefore, the task...... of designing such systems is becoming increasingly important and difficult at the same time. New automated design optimization techniques are needed, which are able to: successfully manage the complexity of embedded systems, meet the constraints imposed by the application domain, shorten the time...
Optimal parameters of monolithic high-contrast grating mirrors.
Marciniak, Magdalena; Gębski, Marcin; Dems, Maciej; Haglund, Erik; Larsson, Anders; Riaziat, Majid; Lott, James A; Czyszanowski, Tomasz
2016-08-01
In this Letter a fully vectorial numerical model is used to search for the construction parameters of monolithic high-contrast grating (MHCG) mirrors providing maximal power reflectance. We determine the design parameters of highly reflecting MHCG mirrors where the etching depth of the stripes is less than two wavelengths in free space. We analyze MHCGs in a broad range of real refractive index values corresponding to most of the common optoelectronic materials in use today. Our results comprise a complete image of possible highly reflecting MHCG mirror constructions for potential use in optoelectronic devices and systems. We support the numerical analysis by experimental verification of the high reflectance via a GaAs MHCG designed for a wavelength of 980 nm.
Crosstalk of High-precision Optical Pickup Actuator with Optimal Structure Parameters
Directory of Open Access Journals (Sweden)
Qingxi JIA
2014-02-01
Full Text Available he crosstalk characteristic is a key factor that affects the pickup actuator dynamic property and consequently the accuracy of reading and writing operation in the future ultra-high density optical storage systems. In this paper, the actuator spatial magnetic field distribution model is first established. Then the crosstalk movement phenomenon of the actuator is analyzed and simulated in CST software based on FDTD principle. Moreover, the crosstalk degree in both tracking and focusing directions are defined with respect to the produced crosstalk forces. The relationship between the crosstalk degree and the structure parameters of the actuator such as the height and width of the permanent magnet is analyzed. Taguchi orthogonal method is further used to obtain the optimal structure parameters. It is concluded that the crosstalk can be effectively reduced by an optimal design of the structure parameters, thereby, the dynamic performance of the actuator can be improved.
Avdagic, Aja; Begic Fazlic, Lejla
2017-01-01
The aim of this study is to present novel algorithms for prediction of dermatological disease using only dermatological clinical features and diagnoses collected in real conditions. A combination of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Genetic algorithm (GA) for ANFIS subtractive clustering parameter optimization has been suggested for the first level of fuzzy model optimization. After that, a genetic optimized ANFIS fuzzy structure is used as input in GA for the second level of fuzzy model optimization. We used double 2-fold Cross validation for generating different validation sets for model improvements. Our approach is performed in the MATLAB environment. We compared results with the other studies. The results confirm that the proposed model achieves accuracy rates which are higher than the one with the previous model.
An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection
Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail
2013-01-01
One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This
An improved swarm optimization for parameter estimation and biological model selection.
Directory of Open Access Journals (Sweden)
Afnizanfaizal Abdullah
Full Text Available One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete
Zeng, Rongping; Badano, Aldo; Myers, Kyle J.
2017-04-01
We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.
Directory of Open Access Journals (Sweden)
Zhiqiang GENG
2014-01-01
Full Text Available Output noise is strongly related to input in closed-loop control system, which makes model identification of closed-loop difficult, even unidentified in practice. The forward channel model is chosen to isolate disturbance from the output noise to input, and identified by optimization the dynamic characteristics of the process based on closed-loop operation data. The characteristics parameters of the process, such as dead time and time constant, are calculated and estimated based on the PI/PID controller parameters and closed-loop process input/output data. And those characteristics parameters are adopted to define the search space of the optimization identification algorithm. PSO-SQP optimization algorithm is applied to integrate the global search ability of PSO with the local search ability of SQP to identify the model parameters of forward channel. The validity of proposed method has been verified by the simulation. The practicability is checked with the PI/PID controller parameter turning based on identified forward channel model.
Optimizing gelling parameters of gellan gum for fibrocartilage tissue engineering.
Lee, Haeyeon; Fisher, Stephanie; Kallos, Michael S; Hunter, Christopher J
2011-08-01
Gellan gum is an attractive biomaterial for fibrocartilage tissue engineering applications because it is cell compatible, can be injected into a defect, and gels at body temperature. However, the gelling parameters of gellan gum have not yet been fully optimized. The aim of this study was to investigate the mechanics, degradation, gelling temperature, and viscosity of low acyl and low/high acyl gellan gum blends. Dynamic mechanical analysis showed that increased concentrations of low acyl gellan gum resulted in increased stiffness and the addition of high acyl gellan gum resulted in greatly decreased stiffness. Degradation studies showed that low acyl gellan gum was more stable than low/high acyl gellan gum blends. Gelling temperature studies showed that increased concentrations of low acyl gellan gum and CaCl₂ increased gelling temperature and low acyl gellan gum concentrations below 2% (w/v) would be most suitable for cell encapsulation. Gellan gum blends were generally found to have a higher gelling temperature than low acyl gellan gum. Viscosity studies showed that increased concentrations of low acyl gellan gum increased viscosity. Our results suggest that 2% (w/v) low acyl gellan gum would have the most appropriate mechanics, degradation, and gelling temperature for use in fibrocartilage tissue engineering applications. Copyright © 2011 Wiley Periodicals, Inc.
Relationships among various parameters for decision tree optimization
Hussain, Shahid
2014-01-14
In this chapter, we study, in detail, the relationships between various pairs of cost functions and between uncertainty measure and cost functions, for decision tree optimization. We provide new tools (algorithms) to compute relationship functions, as well as provide experimental results on decision tables acquired from UCI ML Repository. The algorithms presented in this paper have already been implemented and are now a part of Dagger, which is a software system for construction/optimization of decision trees and decision rules. The main results presented in this chapter deal with two types of algorithms for computing relationships; first, we discuss the case where we construct approximate decision trees and are interested in relationships between certain cost function, such as depth or number of nodes of a decision trees, and an uncertainty measure, such as misclassification error (accuracy) of decision tree. Secondly, relationships between two different cost functions are discussed, for example, the number of misclassification of a decision tree versus number of nodes in a decision trees. The results of experiments, presented in the chapter, provide further insight. © 2014 Springer International Publishing Switzerland.
Fiedler, Anna; Raeth, Sebastian; Theis, Fabian J; Hausser, Angelika; Hasenauer, Jan
2016-08-22
Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the
Optimization of design parameters of low-energy buildings
Vala, Jiří; Jarošová, Petra
2017-07-01
Evaluation of temperature development and related consumption of energy required for heating, air-conditioning, etc. in low-energy buildings requires the proper physical analysis, covering heat conduction, convection and radiation, including beam and diffusive components of solar radiation, on all building parts and interfaces. The system approach and the Fourier multiplicative decomposition together with the finite element technique offers the possibility of inexpensive and robust numerical and computational analysis of corresponding direct problems, as well as of the optimization ones with several design variables, using the Nelder-Mead simplex method. The practical example demonstrates the correlation between such numerical simulations and the time series of measurements of energy consumption on a small family house in Ostrov u Macochy (35 km northern from Brno).
Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control
Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.
2015-01-01
The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.
Lobato, Fran Sérgio
2017-01-01
This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
Cogeneration system simulation/optimization
International Nuclear Information System (INIS)
Puppa, B.A.; Chandrashekar, M.
1992-01-01
Companies are increasingly turning to computer software programs to improve and streamline the analysis o cogeneration systems. This paper introduces a computer program which originated with research at the University of Waterloo. The program can simulate and optimize any type of layout of cogeneration plant. An application of the program to a cogeneration feasibility study for a university campus is described. The Steam and Power Plant Optimization System (SAPPOS) is a PC software package which allows users to model any type of steam/power plant on a component-by-component basis. Individual energy/steam balances can be done quickly to model any scenario. A typical days per month cogeneration simulation can also be carried out to provide a detailed monthly cash flow and energy forecast. This paper reports that SAPPOS can be used for scoping, feasibility, and preliminary design work, along with financial studies, gas contract studies, and optimizing the operation of completed plants. In the feasibility study presented, SAPPOS is used to evaluate both diesel engine and gas turbine combined cycle options
Parameter estimation applied to physiological systems
Rideout, V.C.; Beneken, J.E.W.
Parameter estimation techniques are of ever-increasing interest in the fields of medicine and biology, as greater efforts are currently being made to describe physiological systems in explicit quantitative form. Although some of the techniques of parameter estimation as developed for use in other
Saturne II synchroton injector parameters operation and control: computerization and optimization
International Nuclear Information System (INIS)
Lagniel, J.M.
1983-01-01
The injector control system has been studied, aiming at the beam quality improvement, the increasing of the versatility, and a better machine availability. It has been choosen to realize the three following functions: - acquisition of the principal parameters of the process, so as to control them quickly and to be warned if one of them is wrong (monitoring); - the control of those parameters, one by one or by families (starting, operating point); - the research of an optimal control (on a model or on the process itself) [fr
Li, Rui
2009-01-01
The target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is necessary because there exist numerous practical optimization problems from industry in which the set of
Analysis and optimization of machining parameters of laser cutting for polypropylene composite
Deepa, A.; Padmanabhan, K.; Kuppan, P.
2017-11-01
Present works explains about machining of self-reinforced Polypropylene composite fabricated using hot compaction method. The objective of the experiment is to find optimum machining parameters for Polypropylene (PP). Laser power and Machining speed were the parameters considered in response to tensile test and Flexure test. Taguchi method is used for experimentation. Grey Relational Analysis (GRA) is used for multiple process parameter optimization. ANOVA (Analysis of Variance) is used to find impact for process parameter. Polypropylene has got the great application in various fields like, it is used in the form of foam in model aircraft and other radio-controlled vehicles, thin sheets (∼2-20μm) used as a dielectric, PP is also used in piping system, it is also been used in hernia and pelvic organ repair or protect new herrnis in the same location.
Overall Optimization for Offshore Wind Farm Electrical System
DEFF Research Database (Denmark)
Hou, Peng; Hu, Weihao; Chen, Cong
2017-01-01
Based on particle swarm optimization (PSO), an optimization platform for offshore wind farm electrical system (OWFES) is proposed in this paper, where the main components of an offshore wind farm and key technical constraints are considered as input parameters. The offshore wind farm electrical s...
Fuel efficiency optimization of tanker with focus on hull parameters
Directory of Open Access Journals (Sweden)
Pedram Edalat
2017-06-01
Full Text Available Fuel efficiency optimization is of crucial importance in industries. Marine transportation industry is no exception. Multi-disciplinary optimization is a branch of engineering which uses optimization methods for solving problems in which the objective function is simultaneously affected by several different factors. As one of the tools for this type of optimization, genetic algorithm has a high quality and validity. The objective of the present study is to optimize fuel efficiency in tankers. All presented equations and conditions are valid for tankers. Fuel consumption efficiency of tankers is a function of various influential factors. Given the lack of equations for describing and modeling these factors and unavailability of valid performance database for inferring the equations as well as the lack of literature in this field, the preset study includes five optimizing factors affecting the fuel consumption efficiency of a tanker in genetic algorithm by using the genetic algorithm toolbox of MATLAB software package.
TLM modeling and system identification of optimized antenna structures
Directory of Open Access Journals (Sweden)
N. Fichtner
2008-05-01
Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.
International Nuclear Information System (INIS)
Portnoy, David; Feuerbach, Robert; Heimberg, Jennifer
2011-01-01
Today there is a tremendous amount of interest in systems that can detect radiological or nuclear threats. Many of these systems operate in extremely high throughput situations where delays caused by false alarms can have a significant negative impact. Thus, calculating the tradeoff between detection rates and false alarm rates is critical for their successful operation. Receiver operating characteristic (ROC) curves have long been used to depict this tradeoff. The methodology was first developed in the field of signal detection. In recent years it has been used increasingly in machine learning and data mining applications. It follows that this methodology could be applied to radiological/nuclear threat detection systems. However many of these systems do not fit into the classic principles of statistical detection theory because they tend to lack tractable likelihood functions and have many parameters, which, in general, do not have a one-to-one correspondence with the detection classes. This work proposes a strategy to overcome these problems by empirically finding parameter values that maximize the probability of detection for a selected number of probabilities of false alarm. To find these parameter values a statistical global optimization technique that seeks to estimate portions of a ROC curve is proposed. The optimization combines elements of simulated annealing with elements of genetic algorithms. Genetic algorithms were chosen because they can reduce the risk of getting stuck in local minima. However classic genetic algorithms operate on arrays of Booleans values or bit strings, so simulated annealing is employed to perform mutation in the genetic algorithm. The presented initial results were generated using an isotope identification algorithm developed at Johns Hopkins University Applied Physics Laboratory. The algorithm has 12 parameters: 4 real-valued and 8 Boolean. A simulated dataset was used for the optimization study; the 'threat' set of spectra
Portnoy, David; Feuerbach, Robert; Heimberg, Jennifer
2011-10-01
Today there is a tremendous amount of interest in systems that can detect radiological or nuclear threats. Many of these systems operate in extremely high throughput situations where delays caused by false alarms can have a significant negative impact. Thus, calculating the tradeoff between detection rates and false alarm rates is critical for their successful operation. Receiver operating characteristic (ROC) curves have long been used to depict this tradeoff. The methodology was first developed in the field of signal detection. In recent years it has been used increasingly in machine learning and data mining applications. It follows that this methodology could be applied to radiological/nuclear threat detection systems. However many of these systems do not fit into the classic principles of statistical detection theory because they tend to lack tractable likelihood functions and have many parameters, which, in general, do not have a one-to-one correspondence with the detection classes. This work proposes a strategy to overcome these problems by empirically finding parameter values that maximize the probability of detection for a selected number of probabilities of false alarm. To find these parameter values a statistical global optimization technique that seeks to estimate portions of a ROC curve is proposed. The optimization combines elements of simulated annealing with elements of genetic algorithms. Genetic algorithms were chosen because they can reduce the risk of getting stuck in local minima. However classic genetic algorithms operate on arrays of Booleans values or bit strings, so simulated annealing is employed to perform mutation in the genetic algorithm. The presented initial results were generated using an isotope identification algorithm developed at Johns Hopkins University Applied Physics Laboratory. The algorithm has 12 parameters: 4 real-valued and 8 Boolean. A simulated dataset was used for the optimization study; the "threat" set of spectra
WE-AB-209-09: Optimization of Rotational Arc Station Parameter Optimized Radiation Therapy
International Nuclear Information System (INIS)
Dong, P; Xing, L; Ungun, B; Boyd, S
2016-01-01
Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of improving VMAT in both plan quality and delivery efficiency. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based Proximal Operator Graph Solver (POGS) within seconds. Apertures with zero or low weight were thrown out. To avoid being trapped in a local minimum, a stochastic gradient descent method was employed which also greatly increased the convergence rate of the objective function. The above procedure repeated until the plan could not be improved any further. A weighting factor associated with the total plan MU also indirectly controlled the complexities of aperture shapes. The number of apertures for VMAT and SPORT was confined to 180. The SPORT allowed the coexistence of multiple apertures in a single SP. The optimization technique was assessed by using three clinical cases (prostate, H&N and brain). Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. Prostate case: the volume of the 50% prescription dose was decreased by 22% for the rectum. H&N case: SPORT improved the mean dose for the left and right parotids by 15% each. Brain case: the doses to the eyes, chiasm and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the H&N case. Conclusion: The superior dosimetric quality and delivery efficiency presented here indicates that SPORT is an intriguing alternative treatment modality.
WE-AB-209-09: Optimization of Rotational Arc Station Parameter Optimized Radiation Therapy
Energy Technology Data Exchange (ETDEWEB)
Dong, P; Xing, L [Stanford University School of Medicine, Stanford, CA (United States); Ungun, B [Stanford University School of Medicine, Stanford, CA (United States); Stanford University School of Engineering, Stanford, CA (United States); Boyd, S [Stanford University School of Engineering, Stanford, CA (United States)
2016-06-15
Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of improving VMAT in both plan quality and delivery efficiency. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based Proximal Operator Graph Solver (POGS) within seconds. Apertures with zero or low weight were thrown out. To avoid being trapped in a local minimum, a stochastic gradient descent method was employed which also greatly increased the convergence rate of the objective function. The above procedure repeated until the plan could not be improved any further. A weighting factor associated with the total plan MU also indirectly controlled the complexities of aperture shapes. The number of apertures for VMAT and SPORT was confined to 180. The SPORT allowed the coexistence of multiple apertures in a single SP. The optimization technique was assessed by using three clinical cases (prostate, H&N and brain). Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. Prostate case: the volume of the 50% prescription dose was decreased by 22% for the rectum. H&N case: SPORT improved the mean dose for the left and right parotids by 15% each. Brain case: the doses to the eyes, chiasm and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the H&N case. Conclusion: The superior dosimetric quality and delivery efficiency presented here indicates that SPORT is an intriguing alternative treatment modality.
Optimal concentrations in transport systems
Jensen, Kaare H.; Kim, Wonjung; Holbrook, N. Michele; Bush, John W. M.
2013-01-01
Many biological and man-made systems rely on transport systems for the distribution of material, for example matter and energy. Material transfer in these systems is determined by the flow rate and the concentration of material. While the most concentrated solutions offer the greatest potential in terms of material transfer, impedance typically increases with concentration, thus making them the most difficult to transport. We develop a general framework for describing systems for which impedance increases with concentration, and consider material flow in four different natural systems: blood flow in vertebrates, sugar transport in vascular plants and two modes of nectar drinking in birds and insects. The model provides a simple method for determining the optimum concentration copt in these systems. The model further suggests that the impedance at the optimum concentration μopt may be expressed in terms of the impedance of the pure (c = 0) carrier medium μ0 as μopt∼2αμ0, where the power α is prescribed by the specific flow constraints, for example constant pressure for blood flow (α = 1) or constant work rate for certain nectar-drinking insects (α = 6). Comparing the model predictions with experimental data from more than 100 animal and plant species, we find that the simple model rationalizes the observed concentrations and impedances. The model provides a universal framework for studying flows impeded by concentration, and yields insight into optimization in engineered systems, such as traffic flow. PMID:23594815
Performance Evaluation and Parameter Optimization of SoftCast Wireless Video Broadcast
Directory of Open Access Journals (Sweden)
Dongxue Yang
2015-08-01
Full Text Available Wireless video broadcast plays an imp ortant role in multimedia communication with the emergence of mobile video applications. However, conventional video broadcast designs suffer from a cliff effect due to separated source and channel encoding. The newly prop osed SoftCast scheme employs a cross-layer design, whose reconstructed video quality is prop ortional to the channel condition. In this pap er, we provide the p erformance evaluation and the parameter optimization of the SoftCast system. Optimization principles on parameter selection are suggested to obtain a b etter video quality, o ccupy less bandwidth and/or utilize lower complexity. In addition, we compare SoftCast with H.264 in the LTE EPA scenario. The simulation results show that SoftCast provides a b etter p erformance in the scalability to channel conditions and the robustness to packet losses.
Optimization of Laser Beam Transformation Hardening by One Single Parameter
Meijer, J.; van Sprang, I.
1991-01-01
The process of laser beam transformation hardening is principally controlled by two independent parameters, the absorbed laser power on a given area and the interaction time. These parameters can be transformed into two functional parameters: the maximum surface temperature and the hardening depth.
Design and Simulation of PID parameters self-tuning based on DC speed regulating system
Directory of Open Access Journals (Sweden)
Feng Wei Jie
2016-01-01
Full Text Available The DC speed regulating system has many difficult issues such as system parameters and PID control parameters are difficult to determine. On the basis of model for a single closed-loop DC speed regulating system, this paper puts forward a method of PID parameters self-tuning based on the step response detection and reduced order equivalent. First, detect system step response and get response parameters. Then equal it to a second order system model, and achieve optimal PID control parameters based on optimal second order system to realize of PID parameters self-tuning. The PID parameters self-tuning process of DC speed regulating system is simulated with the help of MATLAB/Simulink. The simulation results show that the method is simple and effective. The system can obtain good dynamic and static performance when the PID parameters are applied to DC speed regulating system.
Shamsipour, Majid; Pahlevani, Zahra; Shabani, Mohsen Ostad; Mazahery, Ali
2016-04-01
Understanding of the electromagnetic stirrer (EMS) process parameters-wear relation in nanocomposite is required for further creation of tailored modifications of process in accordance with the demands for various applications. This study depicts the performance of hybrid algorithm for optimization of the parameters in EMS compocasting of nano-TiC-reinforced Al-Si alloys. Adaptive neuro-fuzzy inference system (ANFIS) coupled with particle swarm optimization (PSO) was applied to find the optimum combination of the inputs including mold temperature, mix time, impeller speed, powder temperature, cast temperature and average particle size. The optimized condition was obtained in minimization of objective function. The objective function is calculated by ANFIS and then minimized by PSO. The optimized parameters were used to produce semisolid cast aluminum matrix composites reinforced with nano-TiC particles. The optimized nanocomposites were then studied for their tribological properties.
System parameter identification information criteria and algorithms
Chen, Badong; Hu, Jinchun; Principe, Jose C
2013-01-01
Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research pr
Chen, Z.; Chen, J.; Zheng, X.; Jiang, F.; Zhang, S.; Ju, W.; Yuan, W.; Mo, G.
2014-12-01
In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation pattern of the net carbon flux. An optimization scheme is proposed to estimate two key parameters (Vcmax and Q10) by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate Vcmax and Q10 of the Boreal Ecosystem Productivity Simulator (BEPS) to improve its NEP simulation in the Boreal North America (BNA) region. Simultaneously, in-situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results have the implication on using atmospheric CO2 data for optimizing ecosystem parameters through atmospheric inversion or data assimilation techniques.
Auto-tuning of PID controller parameters with supervised receding horizon optimization.
Xu, Min; Li, Shaoyuan; Qi, Chenkun; Cai, Wenjian
2005-10-01
In this paper, a novel two-layer online auto-tuning algorithm is presented for a nonlinear time-varying system. The lower layer consists of a conventional proportional-integral-derivative (PID) controller and a plant process, while the upper layer is composed of identification and tuning modules. The purpose of the upper layer is to find a set of optimal PID parameters for the lower layer via an online receding horizon optimization approach, which result in a time-varying PID controller. Through mathematical analysis, the proposed system performance is equivalent to that of a standard generalized predictive control. Simulation and experiment demonstrate that the new method has a better control system performance compared with conventional PID controllers.
Distributed Robust Optimization in Networked System.
Wang, Shengnan; Li, Chunguang
2016-10-11
In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem. We prove that the primal and dual optimal solutions of the problem are restricted in some specific sets, and we give a method to construct these sets. Then, we propose a DRO algorithm to find the primal-dual optimal solutions of the Lagrangian function, which consists of a subgradient step, a projection step, and a diffusion step, and in the projection step of the algorithm, the optimized variables are projected onto the specific sets to guarantee the boundedness of the subgradients. Convergence analysis and numerical simulations verifying the performance of the proposed algorithm are then provided. Further, for nonconvex DRO problem, the corresponding approach and algorithm framework are also provided.
Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model
Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr
2017-10-01
Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations
Modelling and parameter estimation of dynamic systems
Raol, JR; Singh, J
2004-01-01
Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimization of error between the model response and actual system response. However, with the proliferation of high speed digital computers, elegant and innovative techniques like filter error method, H-infinity and Artificial Neural Networks are finding more and mor
Integrated multi-parameter flow measurement system
Lötters, Joost Conrad; van der Wouden, E.J.; Groenesteijn, Jarno; Sparreboom, Wouter; Lammerink, Theodorus S.J.; Wiegerink, Remco J.
2014-01-01
We have designed and realized an integrated multi-parameter flow measurement system, consisting of an integrated Coriolis and thermal flow sensor, and a pressure sensor. The integrated system enables on-chip measurement, analysis and determination of flow and several physical properties of both
Characterization of PV panel and global optimization of its model parameters using genetic algorithm
International Nuclear Information System (INIS)
Ismail, M.S.; Moghavvemi, M.; Mahlia, T.M.I.
2013-01-01
Highlights: • Genetic Algorithm optimization ability had been utilized to extract parameters of PV panel model. • Effect of solar radiation and temperature variations was taken into account in fitness function evaluation. • We used Matlab-Simulink to simulate operation of the PV-panel to validate results. • Different cases were analyzed to ascertain which of them gives more accurate results. • Accuracy and applicability of this approach to be used as a valuable tool for PV modeling were clearly validated. - Abstract: This paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach. The accurate modeling of any PV module is incumbent upon the values of these parameters, as it is imperative in the context of any further studies concerning different PV applications. Simulation, optimization and the design of the hybrid systems that include PV are examples of these applications. The global optimization of the parameters and the applicability for the entire range of the solar radiation and a wide range of temperatures are achievable via this approach. The Manufacturer’s Data Sheet information is used as a basis for the purpose of parameter optimization, with an average absolute error fitness function formulated; and a numerical iterative method used to solve the voltage-current relation of the PV module. The results of single-diode and two-diode models are evaluated in order to ascertain which of them are more accurate. Other cases are also analyzed in this paper for the purpose of comparison. The Matlab–Simulink environment is used to simulate the operation of the PV module, depending on the extracted parameters. The results of the simulation are compared with the Data Sheet information, which is obtained via experimentation in order to validate the reliability of the approach. Three types of PV modules
Parameters Describing Earth Observing Remote Sensing Systems
Zanoni, Vicki; Ryan, Robert E.; Pagnutti, Mary; Davis, Bruce; Markham, Brian; Storey, Jim
2003-01-01
The Earth science community needs to generate consistent and standard definitions for spatial, spectral, radiometric, and geometric properties describing passive electro-optical Earth observing sensors and their products. The parameters used to describe sensors and to describe their products are often confused. In some cases, parameters for a sensor and for its products are identical; in other cases, these parameters vary widely. Sensor parameters are bound by the fundamental performance of a system, while product parameters describe what is available to the end user. Products are often resampled, edge sharpened, pan-sharpened, or compressed, and can differ drastically from the intrinsic data acquired by the sensor. Because detailed sensor performance information may not be readily available to an international science community, standardization of product parameters is of primary performance. Spatial product parameters described include Modulation Transfer Function (MTF), point spread function, line spread function, edge response, stray light, edge sharpening, aliasing, ringing, and compression effects. Spectral product parameters discussed include full width half maximum, ripple, slope edge, and out-of-band rejection. Radiometric product properties discussed include relative and absolute radiometry, noise equivalent spectral radiance, noise equivalent temperature diffenence, and signal-to-noise ratio. Geometric product properties discussed include geopositional accuracy expressed as CE90, LE90, and root mean square error. Correlated properties discussed include such parameters as band-to-band registration, which is both a spectral and a spatial property. In addition, the proliferation of staring and pushbroom sensor architectures requires new parameters to describe artifacts that are different from traditional cross-track system artifacts. A better understanding of how various system parameters affect product performance is also needed to better ascertain the
Kalabukhov, D. S.; Radko, V. M.; Grigoriev, V. A.
2018-01-01
Ultra-low power turbine drives are used as energy sources in auxiliary power systems, energy units, terrestrial, marine, air and space transport within the confines of shaft power N td = 0.01…10 kW. In this paper we propose a new approach to the development of surrogate models for evaluating the integrated efficiency of multistage ultra-low power impulse turbine with pressure stages. This method is based on the use of existing mathematical models of ultra-low power turbine stage efficiency and mass. It has been used in a method for selecting the rational parameters of two-stage axial ultra-low power turbine. The article describes the basic features of an algorithm for two-stage turbine parameters optimization and for efficiency criteria evaluating. Pledged mathematical models are intended for use at the preliminary design of turbine drive. The optimization method was tested at preliminary design of an air starter turbine. Validation was carried out by comparing the results of optimization calculations and numerical gas-dynamic simulation in the Ansys CFX package. The results indicate a sufficient accuracy of used surrogate models for axial two-stage turbine parameters selection
Multiobjective optimization in structural design with uncertain parameters and stochastic processes
Rao, S. S.
1984-01-01
The application of multiobjective optimization techniques to structural design problems involving uncertain parameters and random processes is studied. The design of a cantilever beam with a tip mass subjected to a stochastic base excitation is considered for illustration. Several of the problem parameters are assumed to be random variables and the structural mass, fatigue damage, and negative of natural frequency of vibration are considered for minimization. The solution of this three-criteria design problem is found by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It is observed that the game theory approach is superior in finding a better optimum solution, assuming the proper balance of the various objective functions. The procedures used in the present investigation are expected to be useful in the design of general dynamic systems involving uncertain parameters, stochastic process, and multiple objectives.
Parameter optimization method for the water quality dynamic model based on data-driven theory.
Liang, Shuxiu; Han, Songlin; Sun, Zhaochen
2015-09-15
Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB). Copyright © 2015 Elsevier Ltd. All rights reserved.
Optimal parameters for the FFA-Beddoes dynamic stall model
Energy Technology Data Exchange (ETDEWEB)
Bjoerck, A.; Mert, M. [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H.A. [Risoe National Lab., Roskilde (Denmark)
1999-03-01
Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)
Justification of antenna parameters for automatic systems of radiomonitoring
Пархомей, Ігор Ростиславович; Цьопа, Наталія Володимирівна; Батрак, Євгеній Олександрович
2016-01-01
Improving the efficiency of radar objects with artificially low effective area reflect to a large extent compounded by uncertainty about the information of motion parameters of the object. In terms of information it is impossible to eliminate the uncertainty appropriate to apply artificial intelligence, which have the ability to generalize to accumulate and use knowledge to optimize management. For the synthesis of control systems with artificial intelligence using fuzzy mathematics, it is ba...
Directory of Open Access Journals (Sweden)
Jingxian Hao
2016-11-01
Full Text Available The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.
Directory of Open Access Journals (Sweden)
Krushnaraj Bodana
2016-08-01
Full Text Available The quality of surface finish is always an application based and higher the surface finish higher is the manufacturing cost. This paper exhibits an application of the Taguchi parameter design approach in selecting the major influencing factors on the study of face milling operation of an automobile chassis component and optimization of the same parameters for achieving required surface finish and cycle time in a CNC face milling operation. The Taguchi’s parameter design approach is an efficient trial strategy by which different parameters that are effecting the process were analyzed. An orthogonal L9 array was utilized and experiments were carried out to optimize machining parameters based on the signal to noise ratio. At last, validation tests was also conducted to verify process capability.
Accuracy Analysis and Parameters Optimization in Urban Flood Simulation by PEST Model
Keum, H.; Han, K.; Kim, H.; Ha, C.
2017-12-01
The risk of urban flooding has been increasing due to heavy rainfall, flash flooding and rapid urbanization. Rainwater pumping stations, underground reservoirs are used to actively take measures against flooding, however, flood damage from lowlands continues to occur. Inundation in urban areas has resulted in overflow of sewer. Therefore, it is important to implement a network system that is intricately entangled within a city, similar to the actual physical situation and accurate terrain due to the effects on buildings and roads for accurate two-dimensional flood analysis. The purpose of this study is to propose an optimal scenario construction procedure watershed partitioning and parameterization for urban runoff analysis and pipe network analysis, and to increase the accuracy of flooded area prediction through coupled model. The establishment of optimal scenario procedure was verified by applying it to actual drainage in Seoul. In this study, optimization was performed by using four parameters such as Manning's roughness coefficient for conduits, watershed width, Manning's roughness coefficient for impervious area, Manning's roughness coefficient for pervious area. The calibration range of the parameters was determined using the SWMM manual and the ranges used in the previous studies, and the parameters were estimated using the automatic calibration method PEST. The correlation coefficient showed a high correlation coefficient for the scenarios using PEST. The RPE and RMSE also showed high accuracy for the scenarios using PEST. In the case of RPE, error was in the range of 13.9-28.9% in the no-parameter estimation scenarios, but in the scenario using the PEST, the error range was reduced to 6.8-25.7%. Based on the results of this study, it can be concluded that more accurate flood analysis is possible when the optimum scenario is selected by determining the appropriate reference conduit for future urban flooding analysis and if the results is applied to various
Optimizing the IAEA safeguards system
International Nuclear Information System (INIS)
Drobysz, Sonia; Sitt, Bernard
2011-09-01
During the 2010 Non-Proliferation Treaty Review Conference, States parties recognized that the Additional Protocol (AP) provides increased confidence about the absence of undeclared nuclear material and activities in a State as a whole. They agreed in action 28 of the final document to encourage 'all States parties that have not yet done so to conclude and bring into force an AP as soon as possible and to implement them provisionally pending their entry into force'. Today, 109 out of 189 States parties to the NPT have brought an AP in force. The remaining outliers have not yet done so for three types of reasons: they do not clearly understand what the AP entails; when they do, they refuse to accept new non-proliferation obligations either on the ground of lack of progress in the realm of disarmament, or simply because they are not ready to bear the burden of additional safeguards measures. Strong incentives are thus needed in order to facilitate universalization of the AP. While external incentives would help make the AP a de facto norm and encourage its conclusion by reducing the deplored imbalanced implementation of non-proliferation and disarmament obligations, internal incentives developed by the Agency and its member States can also play an important role. In this respect, NPT States parties recommended in action 32 of the Review Conference final document 'that IAEA safeguards should be assessed and evaluated regularly. Decisions adopted by the IAEA policy bodies aimed at further strengthening the effectiveness and improving the efficiency of IAEA safeguards should be supported and implemented'. The safeguards system should therefore be optimized: the most effective use of safeguards measures as well as safeguards human, financial and technical resources would indeed help enhance the acceptability and even attractiveness of the AP. Optimization can be attractive for States committed to a stronger verification regime independently from other
Sequential parameter estimation for stochastic systems
Directory of Open Access Journals (Sweden)
G. A. Kivman
2003-01-01
Full Text Available The quality of the prediction of dynamical system evolution is determined by the accuracy to which initial conditions and forcing are known. Availability of future observations permits reducing the effects of errors in assessment the external model parameters by means of a filtering algorithm. Usually, uncertainties in specifying internal model parameters describing the inner system dynamics are neglected. Since they are characterized by strongly non-Gaussian distributions (parameters are positive, as a rule, traditional Kalman filtering schemes are badly suited to reducing the contribution of this type of uncertainties to the forecast errors. An extension of the Sequential Importance Resampling filter (SIR is proposed to this aim. The filter is verified against the Ensemble Kalman filter (EnKF in application to the stochastic Lorenz system. It is shown that the SIR is capable of estimating the system parameters and to predict the evolution of the system with a remarkably better accuracy than the EnKF. This highlights a severe drawback of any Kalman filtering scheme: due to utilizing only first two statistical moments in the analysis step it is unable to deal with probability density functions badly approximated by the normal distribution.
Optimization Program for Drinking Water Systems
The Area-Wide Optimization Program (AWOP) provides tools and approaches for drinking water systems to meet water quality optimization goals and provide an increased – and sustainable – level of public health protection to their consumers.
Optimization of process and solution parameters in electrospinning polyethylene oxide
CSIR Research Space (South Africa)
Jacobs, V
2011-11-01
Full Text Available , applied voltage and polyallylamine hydrochloride (PAH) concentration in the spinning solution and its influence on nanofiber diameter. The selected parameters were varied at three levels using Box and Behnken factorial design. The interaction effect...
Jeevanandham Arumugam; Thanushkodi Gowder Keppana
2009-01-01
In this paper a classical lead-lag power system stabilizer is used for demonstration. The stabilizer parameters are selected in such a manner to damp the rotor oscillations. The problem of selecting the stabilizer parameters is converted to a simple optimization problem with an eigen value based objective function and it is proposed to employ simulated annealing and particle swarm optimization for solving the optimization problem. The objective function allows the selection of the stabilizer ...
Particle Swarm Optimization with Power-Law Parameter Based on the Cross-Border Reset Mechanism
Directory of Open Access Journals (Sweden)
WANG, H.
2017-11-01
Full Text Available In order to improve the performance of traditional particle swarm optimization, this paper introduces the principle of Levy flight and cross-border reset mechanism. In the proposed particle swarm optimization, the dynamic variation of parameters meets the power-law distribution and the pattern of particles transition conforms to the Levy flight in the process of algorithm optimization. It means the particles make long distance movements in the search space with a small probability and make short distance movements with a large probability. Therefore, the particles can jump out of local optimum more easily and coordinate the global search and local search of particle swarm optimization. This paper also designs the cross-border reset mechanism to make particles regain optimization ability when stranding on the border of search space after a long distance movement. The simulation results demonstrate the proposed algorithms are easier to jump out of local optimum and have higher accuracy when compared with the existing similar algorithms based on benchmark test functions and handwriting character recognition system.
Directory of Open Access Journals (Sweden)
Jiang Tieying
2015-06-01
Full Text Available This paper describes a longitudinal parameter identification procedure for a small unmanned aerial vehicle (UAV through modified particle swam optimization (PSO. The procedure is demonstrated using a small UAV equipped with only an micro-electro-mechanical systems (MEMS inertial measuring element and a global positioning system (GPS receiver to provide test information. A small UAV longitudinal parameter mathematical model is derived and the modified method is proposed based on PSO with selective particle regeneration (SRPSO. Once modified PSO is applied to the mathematical model, the simulation results show that the mathematical model is correct, and aerodynamic parameters and coefficients of the propeller can be identified accurately. Results are compared with those of PSO and SRPSO and the comparison shows that the proposed method is more robust and faster than the other methods for the longitudinal parameter identification of the small UAV. Some parameter identification results are affected slightly by noise, but the identification results are very good overall. Eventually, experimental validation is employed to test the proposed method, which demonstrates the usefulness of this method.
Optimization of Injection Moulding Process Parameters in the ...
African Journals Online (AJOL)
ADOWIE PERE
ABSTRACT: In this study, optimal injection moulding conditions for minimum shrinkage during moulding of. High Density Polyethylene (HDPE) were obtained by Taguchi method. The result showed that melting temperature of 190OC, injection pressure of 55 MPa, refilling pressure of 85 MPa and cooling time of 11 seconds ...
Optimization of injection moulding process parameters in the ...
African Journals Online (AJOL)
In this study, optimal injection moulding conditions for minimum shrinkage during moulding of High Density Polyethylene (HDPE) were obtained by Taguchi method. The result showed that melting temperature of 190OC, injection pressure of 55 MPa, refilling pressure of 85 MPa and cooling time of 11 seconds gave ...
Optimization of machining parameters of hard porcelain on a CNC ...
African Journals Online (AJOL)
(Taguchi Analysis and RSM) was efficient and effective for multi-attribute decision making problem in Hard Turning. References. Aggarwal A., Singh H., Kumar P., Singh M., 2008. Optimizing power consumption for CNC turned parts using response surface methodology, Taguchi's technique – a comparative analysis, ...
Air Compressor Driving with Synchronous Motors at Optimal Parameters
Directory of Open Access Journals (Sweden)
Iuliu Petrica
2010-10-01
Full Text Available In this paper a method of optimal compensation of the reactive load by the synchronous motors, driving the air compressors, used in mining enterprises is presented, taking into account that in this case, the great majority of the equipment (compressors, pumps are generally working a constant load.
Boundary feedback stabilization of distributed parameter systems
DEFF Research Database (Denmark)
Pedersen, Michael
1988-01-01
The author introduces the method of pseudo-differential stabilization. He notes that the theory of pseudo-differential boundary operators is a fruitful approach to problems arising in control and stabilization theory of distributed-parameter systems. The basic pseudo-differential calculus can...
Advances in Modelling, System Identification and Parameter ...
Indian Academy of Sciences (India)
models determined from flight test data by using parameter estimation methods find extensive use in design/modification of flight control systems, high fidelity flight simulators and evaluation of handling qualitites of aircraft and rotorcraft. R K Mehra et al present new algorithms and results for flutter tests and adaptive notching ...
Automated process safety parameters monitoring system
International Nuclear Information System (INIS)
Iyudina, O.S.; Solov'eva, A.G.; Syrov, A.A.
2015-01-01
Basing on the expertise in upgrading and creation of control systems for NPP process equipment, “Diakont” has developed the automated process safety parameters monitoring system project. The monitoring system is a set of hardware, software and data analysis tools based on a dynamic logical-and-probabilistic model of process safety. The proposed monitoring system can be used for safety monitoring and analysis of the following processes: reactor core reloading; spent nuclear fuel transfer; startup, loading, on-load operation and shutdown of an NPP turbine [ru
International Nuclear Information System (INIS)
Isaev, P.S.; Osipov, A.A.
1984-01-01
The slope parameters of the ππ-system are calculated in the framework of the superconductor-tupe quark model. The analogous calculations are made for πK-system. The amplitudes are obtained by using the box quark diagrams and tree diagrams with the intermediate scalar epsilon(700), Ssup(x)(975), K tilde (1350) mesons and vector rho(770), K* (892) mesons
Simulation Propulsion System and Trajectory Optimization
Hendricks, Eric S.; Falck, Robert D.; Gray, Justin S.
2017-01-01
A number of new aircraft concepts have recently been proposed which tightly couple the propulsion system design and operation with the overall vehicle design and performance characteristics. These concepts include propulsion technology such as boundary layer ingestion, hybrid electric propulsion systems, distributed propulsion systems and variable cycle engines. Initial studies examining these concepts have typically used a traditional decoupled approach to aircraft design where the aerodynamics and propulsion designs are done a-priori and tabular data is used to provide inexpensive look ups to the trajectory ana-ysis. However the cost of generating the tabular data begins to grow exponentially when newer aircraft concepts require consideration of additional operational parameters such as multiple throttle settings, angle-of-attack effects on the propulsion system, or propulsion throttle setting effects on aerodynamics. This paper proposes a new modeling approach that eliminated the need to generate tabular data, instead allowing an expensive propulsion or aerodynamic analysis to be directly integrated into the trajectory analysis model and the entire design problem optimized in a fully coupled manner. The new method is demonstrated by implementing a canonical optimal control problem, the F-4 minimum time-to-climb trajectory optimization using three relatively new analysis tools: Open M-DAO, PyCycle and Pointer. Pycycle and Pointer both provide analytic derivatives and Open MDAO enables the two tools to be combined into a coupled model that can be run in an efficient parallel manner that helps to cost the increased cost of the more expensive propulsion analysis. Results generated with this model serve as a validation of the tightly coupled design method and guide future studies to examine aircraft concepts with more complex operational dependencies for the aerodynamic and propulsion models.
Reutterer, Bernd; Traxler, Lukas; Bayer, Natascha; Drauschke, Andreas
2016-04-01
Selective Laser Sintering (SLS) is considered as one of the most important additive manufacturing processes due to component stability and its broad range of usable materials. However the influence of the different process parameters on mechanical workpiece properties is still poorly studied, leading to the fact that further optimization is necessary to increase workpiece quality. In order to investigate the impact of various process parameters, laboratory experiments are implemented to improve the understanding of the SLS limitations and advantages on an educational level. Experiments are based on two different workstations, used to teach students the fundamentals of SLS. First of all a 50 W CO2 laser workstation is used to investigate the interaction of the laser beam with the used material in accordance with varied process parameters to analyze a single-layered test piece. Second of all the FORMIGA P110 laser sintering system from EOS is used to print different 3D test pieces in dependence on various process parameters. Finally quality attributes are tested including warpage, dimension accuracy or tensile strength. For dimension measurements and evaluation of the surface structure a telecentric lens in combination with a camera is used. A tensile test machine allows testing of the tensile strength and the interpreting of stress-strain curves. The developed laboratory experiments are suitable to teach students the influence of processing parameters. In this context they will be able to optimize the input parameters depending on the component which has to be manufactured and to increase the overall quality of the final workpiece.
Characterization of antioxidant system parameters in four freshwater fish species.
Atli, Gülüzar; Canli, Esin G; Eroglu, Ali; Canli, Mustafa
2016-04-01
The potential use of antioxidant system parameters has gained considerable interest due to their pivotal role of detoxification mechanisms in environmental studies and culture fish point of view. Fish with different ecological needs may have different antioxidant capacity and response to environmental contaminants. Thus, the optimal working conditions and specific enzyme activities (Vmax and Km) of antioxidant system parameters (Superoxide dismutase, SOD; Catalase, CAT; Glutathione peroxidase, GPX; Glutathione reductase, GR and Glutathione S-transferase, GST) and glutathione (GSH) were determined in four commonly cultured freshwater fish species (tilapia; Oreochromis niloticus, carp; Cyprinus carpio, trout; Onchorhynchus mykiss and catfish; Clarias garipienus). Data showed that optimal concentrations of different buffers, pH and specific chemicals for each enzyme and GSH were similar in most cases for all fish species, except a few differences. The highest Vmax and Km values were found in carp for GPX and GST, though these values were the highest in tilapia, catfish and trout for CAT, SOD and GR, respectively. As a conclusion, optimization assays of these parameters in different bioindicator organisms based on their physiological and ecological differences may be useful for the aquatic ecosystem biomonitoring studies and also present fundamental data for utilization in aquaculture. Copyright © 2015 Elsevier Inc. All rights reserved.
Optimal Trajectories Generation in Robotic Fiber Placement Systems
Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane
2017-06-01
The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.
Sensitivity analysis in multi-parameter probabilistic systems
International Nuclear Information System (INIS)
Walker, J.R.
1987-01-01
Probabilistic methods involving the use of multi-parameter Monte Carlo analysis can be applied to a wide range of engineering systems. The output from the Monte Carlo analysis is a probabilistic estimate of the system consequence, which can vary spatially and temporally. Sensitivity analysis aims to examine how the output consequence is influenced by the input parameter values. Sensitivity analysis provides the necessary information so that the engineering properties of the system can be optimized. This report details a package of sensitivity analysis techniques that together form an integrated methodology for the sensitivity analysis of probabilistic systems. The techniques have known confidence limits and can be applied to a wide range of engineering problems. The sensitivity analysis methodology is illustrated by performing the sensitivity analysis of the MCROC rock microcracking model
Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems.
Dresselhaus, Thomas; Yang, Jack; Kumbhar, Sadhana; Waller, Mark P
2013-04-09
Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.
Microbial alkaline proteases: Optimization of production parameters and their properties
Kanupriya Miglani Sharma; Rajesh Kumar; Surbhi Panwar; Ashwani Kumar
2017-01-01
Proteases are hydrolytic enzymes capable of degrading proteins into small peptides and amino acids. They account for nearly 60% of the total industrial enzyme market. Proteases are extensively exploited commercially, in food, pharmaceutical, leather and detergent industry. Given their potential use, there has been renewed interest in the discovery of proteases with novel properties and a constant thrust to optimize the enzyme production. This review summarizes a fraction of the enormous repor...
PC based 8-parameter data acquisition system
International Nuclear Information System (INIS)
Gupta, J.D.; Naik, K.V.; Jain, S.K.; Pathak, R.V.; Suman, B.
1989-01-01
Multiparameter data acquisition (MPA) systems which analyse nuclear events with respect to more than one property of the event are essential tools for the study of some complex nuclear phenomena requiring analysis of time coincident spectra. For better throughput and accuracy each parameter is digitized by its own ADC. A stand alone low cost IBM PC based 8-parameter data acquisition system developed by the authors makes use of Address Recording technique for acquiring data from eight 12 bit ADC's in the PC Memory. Two memory buffers in the PC memory are used in ping-pong fashion so that data acquisition in one bank and dumping of data onto PC disk from the other bank can proceed simultaneously. Data is acquired in the PC memory through DMA mode for realising high throughput and hardware interrupt is used for switching banks for data acquisition. A comprehensive software package developed in Turbo-Pascal offers a set of menu-driven interactive commands to the user for setting-up system parameters and control of the system. The system is to be used with pelletron accelerator. (author). 5 figs
Optimal Control and Optimization of Stochastic Supply Chain Systems
Song, Dong-Ping
2013-01-01
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject in the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of ...
Optimization of turning process parameters by using grey-Taguchi ...
African Journals Online (AJOL)
The main objective of this study is to improve toughness and hardness of engineering material by changing the machining parameters of turning process. By applying Taguchi method the quality of manufactured goods, and engineering designs are developed by studying variations. In this work, an attempt has been made to ...
Investigation and validation of optimal cutting parameters for least ...
African Journals Online (AJOL)
user
machining the hard martensite stainless steel and indicated that the surface roughness is a critical parameter to the functionality of machined components and ... Turning is carried on lathe that provides the power to turn the work piece at a given rotational speed and feed to the cutting tool at specified rate and depth of cut.
Optimization of Storage Parameters of Selected Fruits in Passive ...
African Journals Online (AJOL)
This study was carried out to determine the optimum storage parameters of selected fruit using three sets of four types of passive evaporative cooling structures made of two different materials clay and aluminium. One set consisted of four separate cooling chambers. Two cooling chambers were made with aluminium ...
Optimal Two Parameter Bounds for the Seiffert Mean
Directory of Open Access Journals (Sweden)
Hui Sun
2013-01-01
Full Text Available We obtain sharp bounds for the Seiffert mean in terms of a two parameter family of means. Our results generalize and extend the recent bounds presented in the Journal of Inequalities and Applications (2012 and Abstract and Applied Analysis (2012.
Screening for the optimal induction parameters for periplasmic ...
African Journals Online (AJOL)
ONOS
2010-09-20
Sep 20, 2010 ... 43400 UPM Serdang, Selangor, Malaysia. Accepted 5 August, 2010. Screening for optimum induction parameters to improve the production of periplasmic interferon-α2b. (PrIFN-α2b) by recombinant Escherichia coli was conducted using shake flask culture. Recombinant E. coli Rosetta-gami 2(DE3) ...
Optimization of process parameters for synthesis of silica–Ni ...
Indian Academy of Sciences (India)
ship between the reduction of metal salts in silica powder as a function of experimental parameters, viz. temperature and time of reduction (Roy et al 2007). It was, therefore,. ∗. Author for correspondence (bduari@yahoo.co.in) thought to be worthwhile to find out such a quantitative rela- tionship and this paper deals with the ...
Optimization of burnishing parameters and determination of select ...
Indian Academy of Sciences (India)
The present study is aimed at ﬁlling the gaps in scientiﬁc understanding of the burnishing process, and also to aid and arrive at technological solutions for the surface modiﬁcations based on burnishing of some of the commonly employed engineering materials. The effects of various burnishing parameters on the surface ...
Optimizing pulsed current micro plasma arc welding parameters to ...
African Journals Online (AJOL)
This paper reveals the influences of pulsed current parameters namely peak current, back current, pulse and pulse width on the ultimate tensile strength of Micro Plasma Arc Welded Inconel 625 sheets. Mathematical model is developed to predict ultimate tensile strength of pulsed current micro plasma arc welded Inconel ...
Dependability of self-optimizing mechatronic systems
Rammig, Franz; Schäfer, Wilhelm; Sextro, Walter
2014-01-01
Intelligent technical systems, which combine mechanical, electrical and software engineering with control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. Self-optimizing systems create high value for example in terms of energy and resource efficiency as well as reliability. The Collaborative Research Center 614 "Self-optimizing Concepts and Structures in Mechanical Engineering" pursued the long-term aim to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop self-optimizing systems. This book is directed to researchers and practitioners alike. It provides a design methodology for the development of self-optimizing systems consisting of a reference process, methods, and tools. The reference process is divided into two phase...
Flores, Jorge L.; García-Torales, G.; Ponce Ávila, Cristina
2006-08-01
This paper describes an in situ image recognition system designed to inspect the quality standards of the chocolate pops during their production. The essence of the recognition system is the localization of the events (i.e., defects) in the input images that affect the quality standards of pops. To this end, processing modules, based on correlation filter, and segmentation of images are employed with the objective of measuring the quality standards. Therefore, we designed the correlation filter and defined a set of features from the correlation plane. The desired values for these parameters are obtained by exploiting information about objects to be rejected in order to find the optimal discrimination capability of the system. Regarding this set of features, the pop can be correctly classified. The efficacy of the system has been tested thoroughly under laboratory conditions using at least 50 images, containing 3 different types of possible defects.
Energy Technology Data Exchange (ETDEWEB)
Katheder, H. [Max-Planck-Inst. fuer Plasmaphysik, Garching (Germany). NET Team
1995-12-31
Large superconducting coils such as are used for fusion experiments (Tokamak or Stellarator confiurations) are best equipped with internally cooled superconducting cables. These cables, which are cooled with helium at 4 K
Multicriteria Optimization of Gasification Operational Parameters Using a Pareto Genetic Algorithm
Directory of Open Access Journals (Sweden)
Miguel Caldas
2005-04-01
Full Text Available Gasification is a well-known technology that allows for a combustible gas to be obtained from a carbonaceous fuel by a partial oxidation process (POX. The resulting gas (synthesis gas or syngas can be used either as a fuel or as a feedstock for chemical production. Recently, gasification has also received a great deal of attention concerning power production possibilities through IGCC process (Integrated Gasification Combined Cycle, which is currently the most environmentally friendly and efficient method for the production of electricity. Gasification allows for low grade fuels, or dirty fuels, to be used in an environmental acceptable way. Amongst these fuels are wastes from the petrochemical and other industries, which vary in composition from shipment to shipment, and from lot to lot. If operating conditions are kept constant this could result in lose of efficiency. This paper presents an application of Genetic Algorithms to optimize the operating parameters of a gasifier processing a given fuel, so that the system achieves maximum efficiency for each particular fuel composition. A Pareto multiobjective optimization method, combined with a Genetic Algorithm, is applied to the simultaneous maximization of two different objective functions: Cold Gas Efficiency and Hydrogen Contents of the syngas. Results show that the optimization method developed is fast and simple enough to be used for on-line adjustment of the gasification operating parameters for each fuel composition and aim of gasification, thus improving overall performance of the industrial process.
Optimization of dynamic MOSA model parameters using ATP/EMTP software tool
Directory of Open Access Journals (Sweden)
Jasika Ranko
2017-01-01
Full Text Available This paper demonstrates the procedure for estimating parameters of a dynamic metal-oxide surge arrester model by using a genetic algorithm, implemented in ATP/EMTP graphic preprocessor (ATPDraw optimization module. The advantages of new ATPDraw options that allow optimization of electric circuit elements are shown. The optimization process is applied to two frequency-dependent MOSA models. At the end of the work, a comparison of results obtained before and after optimization is given.
Ju, Jonghyun; Han, Yun-ah; Kim, Seok-min
2013-03-07
The effects of structural design parameters on the performance of nano-replicated photonic crystal (PC) label-free biosensors were examined by the analysis of simulated reflection spectra of PC structures. The grating pitch, duty, scaled grating height and scaled TiO2 layer thickness were selected as the design factors to optimize the PC structure. The peak wavelength value (PWV), full width at half maximum of the peak, figure of merit for the bulk and surface sensitivities, and surface/bulk sensitivity ratio were also selected as the responses to optimize the PC label-free biosensor performance. A parametric study showed that the grating pitch was the dominant factor for PWV, and that it had low interaction effects with other scaled design factors. Therefore, we can isolate the effect of grating pitch using scaled design factors. For the design of PC-label free biosensor, one should consider that: (1) the PWV can be measured by the reflection peak measurement instruments, (2) the grating pitch and duty can be manufactured using conventional lithography systems, and (3) the optimum design is less sensitive to the grating height and TiO2 layer thickness variations in the fabrication process. In this paper, we suggested a design guide for highly sensitive PC biosensor in which one select the grating pitch and duty based on the limitations of the lithography and measurement system, and conduct a multi objective optimization of the grating height and TiO2 layer thickness for maximizing performance and minimizing the influence of parameter variation. Through multi-objective optimization of a PC structure with a fixed grating height of 550 nm and a duty of 50%, we obtained a surface FOM of 66.18 RIU-1 and an S/B ratio of 34.8%, with a grating height of 117 nm and TiO2 height of 210 nm.
Directory of Open Access Journals (Sweden)
Yun-ah Han
2013-03-01
Full Text Available The effects of structural design parameters on the performance of nano-replicated photonic crystal (PC label-free biosensors were examined by the analysis of simulated reflection spectra of PC structures. The grating pitch, duty, scaled grating height and scaled TiO2 layer thickness were selected as the design factors to optimize the PC structure. The peak wavelength value (PWV, full width at half maximum of the peak, figure of merit for the bulk and surface sensitivities, and surface/bulk sensitivity ratio were also selected as the responses to optimize the PC label-free biosensor performance. A parametric study showed that the grating pitch was the dominant factor for PWV, and that it had low interaction effects with other scaled design factors. Therefore, we can isolate the effect of grating pitch using scaled design factors. For the design of PC-label free biosensor, one should consider that: (1 the PWV can be measured by the reflection peak measurement instruments, (2 the grating pitch and duty can be manufactured using conventional lithography systems, and (3 the optimum design is less sensitive to the grating height and TiO2 layer thickness variations in the fabrication process. In this paper, we suggested a design guide for highly sensitive PC biosensor in which one select the grating pitch and duty based on the limitations of the lithography and measurement system, and conduct a multi objective optimization of the grating height and TiO2 layer thickness for maximizing performance and minimizing the influence of parameter variation. Through multi-objective optimization of a PC structure with a fixed grating height of 550 nm and a duty of 50%, we obtained a surface FOM of 66.18 RIU−1 and an S/B ratio of 34.8%, with a grating height of 117 nm and TiO2 height of 210 nm.
Machining parameter optimization in turning process for sustainable manufacturing
S. G. Dambhare; S. J. Deshmukh; A. B. Borade
2015-01-01
There is an increase in awareness about sustainable manufacturing process. Manufacturing industries are backbone of a country’s economy. Although it is important but there is a great concern about consumption of resources and waste creation. The primary aim of this study was to explore sustainability concern in turning process in an Indian machining industry. The effect of cutting parameters, Speed/Feed/Depth of Cut, the machining environment, Dry/MQL/Wet, and the type of cutting tool on sust...
Fuzzy logic control and optimization system
Lou, Xinsheng [West Hartford, CT
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Optimization of process parameters through GRA, TOPSIS and RSA models
Directory of Open Access Journals (Sweden)
Suresh Nipanikar
2018-01-01
Full Text Available This article investigates the effect of cutting parameters on the surface roughness and flank wear during machining of titanium alloy Ti-6Al-4V ELI( Extra Low Interstitial in minimum quantity lubrication environment by using PVD TiAlN insert. Full factorial design of experiment was used for the machining 2 factors 3 levels and 2 factors 2 levels. Turning parameters studied were cutting speed (50, 65, 80 m/min, feed (0.08, 0.15, 0.2 mm/rev and depth of cut 0.5 mm constant. The results show that 44.61 % contribution of feed and 43.57 % contribution of cutting speed on surface roughness also 53.16 % contribution of cutting tool and 26.47 % contribution of cutting speed on tool flank wear. Grey relational analysis and TOPSIS method suggest the optimum combinations of machining parameters as cutting speed: 50 m/min, feed: 0.8 mm/rev., cutting tool: PVD TiAlN, cutting fluid: Palm oi
Directory of Open Access Journals (Sweden)
Hongjin Wang
2015-09-01
Full Text Available To efficiently recover the waste heat from a diesel engine exhaust, a regenerative organic Rankine cycle (RORC system was employed, and butane, R124, R416A, and R134a were used as the working fluids. The resulting diesel engine-RORC combined system was defined and the relevant evaluation indexes were proposed. First, the variation tendency of the exhaust energy rate under various diesel engine operating conditions was analyzed using experimental data. The thermodynamic model of the RORC system was established based on the first and second laws of thermodynamics, and the net power output and exergy destruction rate of the RORC system were selected as the objective functions. A particle swarm optimization (PSO algorithm was used to optimize the operating parameters of the RORC system, including evaporating pressure, intermediate pressure, and degree of superheat. The operating performances of the RORC system and diesel engine-RORC combined system were studied for the four selected working fluids under various operating conditions of the diesel engine. The results show that the operating performances of the RORC system and the combined system using butane are optimal on the basis of optimizing the operating parameters; when the engine speed is 2200 r/min and engine torque is 1215 N·m, the net power output of the RORC system using butane is 36.57 kW, and the power output increasing ratio (POIR of the combined system using butane is 11.56%.
A Novel adaptative Discrete Cuckoo Search Algorithm for parameter optimization in computer vision
Directory of Open Access Journals (Sweden)
loubna benchikhi
2017-10-01
Full Text Available Computer vision applications require choosing operators and their parameters, in order to provide the best outcomes. Often, the users quarry on expert knowledge and must experiment many combinations to find manually the best one. As performance, time and accuracy are important, it is necessary to automate parameter optimization at least for crucial operators. In this paper, a novel approach based on an adaptive discrete cuckoo search algorithm (ADCS is proposed. It automates the process of algorithms’ setting and provides optimal parameters for vision applications. This work reconsiders a discretization problem to adapt the cuckoo search algorithm and presents the procedure of parameter optimization. Some experiments on real examples and comparisons to other metaheuristic-based approaches: particle swarm optimization (PSO, reinforcement learning (RL and ant colony optimization (ACO show the efficiency of this novel method.
A Pseudodifferential Approach to Distributed Parameter Systems and Stabilization
DEFF Research Database (Denmark)
Pedersen, Michael
1993-01-01
in the case of a distributed system with feedback acting on the boundary of a bounded domain in Rn and appearing in the Neumann boundary condition. We establish the pseudodifferental setting for the Neumann feedback control problem previously established for the corresponding Dirichlet problem by Pederson....... Differential Equations47 (1983); Appl. Math. Optim.10 (1983)). So far, this work seems to have simplified or unified many of the previous works cited above. We hope that in the future it will even provide stronger and newer results in the boundary control of distributed parameter systems....
Design of Thermal Systems Using Topology Optimization
DEFF Research Database (Denmark)
Haertel, Jan Hendrik Klaas
The goal of this thesis is to apply topology optimization to the design of di_erent thermal systems such as heat sinks and heat exchangers in order to improve the thermal performance of these systems compared to conventional designs. The design of thermal systems is a complex task that has...... of optimized designs are presented within this thesis. The main contribution of the thesis is the development of several numerical optimization models that are applied to different design challenges within thermal engineering. Topology optimization is applied in an industrial project to design the heat....... The design of 3D printed dry-cooled power plant condensers using a simpliffed thermouid topology optimization model is presented in another study. A benchmarking of the optimized geometries against a conventional heat exchanger design is conducted and the topology optimized designs show a superior...
Multi Objective Optimization of Flux Cored Arc Weld Parameters Using Hybrid Grey - Fuzzy Technique
Directory of Open Access Journals (Sweden)
M Satheesh
2014-06-01
Full Text Available In the present work, an attempt has been made to use the grey-based fuzzy logic method to solve correlated multiple response optimization problems in the field of flux cored arc welding. This approach converts the complex multiple objectives into a single grey-fuzzy reasoning grade. Based on the grey-fuzzy reasoning grade, optimum parameters are identified. The significant contributions of parameters are estimated using analysis of variance (ANOVA. This evaluation procedure can be used in intelligent decision making for a welding operator. The proposed and developed method has good accuracy and competency. The proposed technique provides manufacturers who develop intelligent manufacturing systems a method to facilitate the achievement of the highest level of automation.
Research on Intelligent Control System of DC SQUID Magnetometer Parameters for Multi-channel System
Chen, Hua; Yang, Kang; Lu, Li; Kong, Xiangyan; Wang, Hai; Wu, Jun; Wang, Yongliang
2018-03-01
In a multi-channel SQUID measurement system, adjusting device parameters to optimal condition for all channels is time-consuming. In this paper, an intelligent control system is presented to determine the optimal working point of devices which is automatic and more efficient comparing to the manual one. An optimal working point searching algorithm is introduced as the core component of the control system. In this algorithm, the bias voltage V_bias is step scanned to obtain the maximal value of the peak-to-peak current value I_pp of the SQUID magnetometer modulation curve. We choose this point as the optimal one. Using the above control system, more than 30 weakly damped SQUID magnetometers with area of 5 × 5 mm^2 or 10 × 10 mm^2 are adjusted and a 36-channel magnetocardiography system perfectly worked in a magnetically shielded room. The average white flux noise is 15 μΦ_0/Hz^{1/2}.
Optimized multi area AGC simulation in restructured power systems
International Nuclear Information System (INIS)
Bhatt, Praghnesh; Roy, Ranjit; Ghoshal, S.P.
2010-01-01
In this paper, the traditional automatic generation control loop with modifications is incorporated for simulating automatic generation control (AGC) in restructured power system. Federal energy regulatory commission (FERC) encourages an open market system for price based operation. FERC has issued a notice for proposed rulemaking of various ancillary services. One of these ancillary services is load following with frequency control which comes broadly under Automatic Generation Control in deregulated regime. The concept of DISCO participation matrix is used to simulate the bilateral contracts in the three areas and four area diagrams. Hybrid particle swarm optimization is used to obtain optimal gain parameters for optimal transient performance. (author)
System Topology Optimization - An Approach to System Design of Electro-Hydraulic-Mechanical Systems
DEFF Research Database (Denmark)
Andersen, T. O.; Hansen, M. R.; Conrad, Finn
2003-01-01
The current paper presents an approach to system design of combined electro-hydraulic-mechanical systems. The approach is based on the concurrent handling of the topology as well as the design parameters of the mechanical, hydraulic and controller sub- systems, respectively. Based on an initial...... design the procedure attempts to find the optimal topology and the related parameters. The topology considerations comprise the type of hydraulic pump, the employment of knee linkages or not as well as the type of hydraulic actuators. The design variables also include the signals to the proportional...... valve in a number of predefined load cases as well as the hydraulic and mechanical parameters....
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems
Directory of Open Access Journals (Sweden)
Banga Julio R
2006-11-01
Full Text Available Abstract Background We consider the problem of parameter estimation (model calibration in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector. In order to surmount these difficulties, global optimization (GO methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown structure (i.e. black-box models. In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.
Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R
2006-11-02
We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.
Robust Design Optimization of an Aerospace Vehicle Prolusion System
Directory of Open Access Journals (Sweden)
Muhammad Aamir Raza
2011-01-01
Full Text Available This paper proposes a robust design optimization methodology under design uncertainties of an aerospace vehicle propulsion system. The approach consists of 3D geometric design coupled with complex internal ballistics, hybrid optimization, worst-case deviation, and efficient statistical approach. The uncertainties are propagated through worst-case deviation using first-order orthogonal design matrices. The robustness assessment is measured using the framework of mean-variance and percentile difference approach. A parametric sensitivity analysis is carried out to analyze the effects of design variables variation on performance parameters. A hybrid simulated annealing and pattern search approach is used as an optimizer. The results show the objective function of optimizing the mean performance and minimizing the variation of performance parameters in terms of thrust ratio and total impulse could be achieved while adhering to the system constraints.
Scheduling with Bus Access Optimization for Distributed Embedded Systems
DEFF Research Database (Denmark)
Eles, Petru; Doboli, Alex; Pop, Paul
2000-01-01
In this paper, we concentrate on aspects related to the synthesis of distributed embedded systems consisting of programmable processors and application-specific hardware components. The approach is based on an abstract graph representation that captures, at process level, both dataflow and the flow...... of control. Our goal is to derive a worst case delay by which the system completes execution, such that this delay is as small as possible; to generate a logically and temporally deterministic schedule; and to optimize parameters of the communication protocol such that this delay is guaranteed. We have...... have to be considered during scheduling but also the parameters of the communication protocol should be adapted to fit the particular embedded application. The optimization algorithm, which implies both process scheduling and optimization of the parameters related to the communication protocol...
Optimization of sampling parameters for standardized exhaled breath sampling.
Doran, Sophie; Romano, Andrea; Hanna, George B
2017-09-05
The lack of standardization of breath sampling is a major contributing factor to the poor repeatability of results and hence represents a barrier to the adoption of breath tests in clinical practice. On-line and bag breath sampling have advantages but do not suit multicentre clinical studies whereas storage and robust transport are essential for the conduct of wide-scale studies. Several devices have been developed to control sampling parameters and to concentrate volatile organic compounds (VOCs) onto thermal desorption (TD) tubes and subsequently transport those tubes for laboratory analysis. We conducted three experiments to investigate (i) the fraction of breath sampled (whole vs. lower expiratory exhaled breath); (ii) breath sample volume (125, 250, 500 and 1000ml) and (iii) breath sample flow rate (400, 200, 100 and 50 ml/min). The target VOCs were acetone and potential volatile biomarkers for oesophago-gastric cancer belonging to the aldehyde, fatty acids and phenol chemical classes. We also examined the collection execution time and the impact of environmental contamination. The experiments showed that the use of exhaled breath-sampling devices requires the selection of optimum sampling parameters. The increase in sample volume has improved the levels of VOCs detected. However, the influence of the fraction of exhaled breath and the flow rate depends on the target VOCs measured. The concentration of potential volatile biomarkers for oesophago-gastric cancer was not significantly different between the whole and lower airway exhaled breath. While the recovery of phenols and acetone from TD tubes was lower when breath sampling was performed at a higher flow rate, other VOCs were not affected. A dedicated 'clean air supply' overcomes the contamination from ambient air, but the breath collection device itself can be a source of contaminants. In clinical studies using VOCs to diagnose gastro-oesophageal cancer, the optimum parameters are 500mls sample volume
Kumar, S.; Singh, A.; Dhar, A.
2017-08-01
The accurate estimation of the photovoltaic parameters is fundamental to gain an insight of the physical processes occurring inside a photovoltaic device and thereby to optimize its design, fabrication processes, and quality. A simulative approach of accurately determining the device parameters is crucial for cell array and module simulation when applied in practical on-field applications. In this work, we have developed a global particle swarm optimization (GPSO) approach to estimate the different solar cell parameters viz., ideality factor (η), short circuit current (Isc), open circuit voltage (Voc), shunt resistant (Rsh), and series resistance (Rs) with wide a search range of over ±100 % for each model parameter. After validating the accurateness and global search power of the proposed approach with synthetic and noisy data, we applied the technique to the extract the PV parameters of ZnO/PCDTBT based hybrid solar cells (HSCs) prepared under different annealing conditions. Further, we examine the variation of extracted model parameters to unveil the physical processes occurring when different annealing temperatures are employed during the device fabrication and establish the role of improved charge transport in polymer films from independent FET measurements. The evolution of surface morphology, optical absorption, and chemical compositional behaviour of PCDTBT co-polymer films as a function of processing temperature has also been captured in the study and correlated with the findings from the PV parameters extracted using GPSO approach.
Identification of systems with distributed parameters
International Nuclear Information System (INIS)
Moret, J.M.
1990-10-01
The problem of finding a model for the dynamical response of a system with distributed parameters based on measured data is addressed. First a mathematical formalism is developed in order to obtain the specific properties of such a system. Then a linear iterative identification algorithm is proposed that includes these properties, and that produces better results than usual non linear minimisation techniques. This algorithm is further improved by an original data decimation that allow to artificially increase the sampling period without losing between sample information. These algorithms are tested with real laboratory data
Yu, Tao; Kang, Chao; Zhao, Pan
2018-01-01
The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. PMID:29385048
Parameters and design optimization of the ring piezoelectric ceramic transformer
Directory of Open Access Journals (Sweden)
Jiří Erhart
2015-09-01
Full Text Available Main aim of the presented paper is the theoretical analysis and experimental verification of the transformation parameters for the new type of nonhomogeneously poled ring transformer. The input part is poled in the thickness direction and output part in the radial direction. Two transformer geometries are studied — the input part is at inner ring segment, or it is at the outer ring segment. The optimum electrode size aspect ratios have been found experimentally as d1∕D≈0.60−0.65 for the ring with aspect ratio d∕D=0.2. The fundamental as well as higher overtone resonances were studied for the transformation ratio, the optimum resistive load, efficiency and no-load transformation ratio. Higher overtones have better transformation parameters compared to the fundamental resonance. The new type ring transformer exhibits very high transformation ratios up to 200 under no-load and up to 13.4 under a high efficiency of 97% at the optimum load conditions of 10 kΩ. Strong electric field gradient at the output circuit is applicable for the electrical discharge generation.
Gomez-Cardona, Daniel; Hayes, John; Zhang, Ran; Li, Ke; Cruz-Bastida, Juan Pablo; Chen, Guang-Hong
2018-03-12
region, and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise-spatial resolution trade-offs. The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work. A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Noncooperatively optimized tolerance: decentralized strategic optimization in complex systems.
Vorobeychik, Yevgeniy; Mayo, Jackson R; Armstrong, Robert C; Ruthruff, Joseph R
2011-09-02
We introduce noncooperatively optimized tolerance (NOT), a game theoretic generalization of highly optimized tolerance (HOT), which we illustrate in the forest fire framework. As the number of players increases, NOT retains features of HOT, such as robustness and self-dissimilar landscapes, but also develops features of self-organized criticality. The system retains considerable robustness even as it becomes fractured, due in part to emergent cooperation between players, and at the same time exhibits increasing resilience against changes in the environment, giving rise to intermediate regimes where the system is robust to a particular distribution of adverse events, yet not very fragile to changes.
Vijaya Ramnath, B.; Sharavanan, S.; Jeykrishnan, J.
2017-03-01
Nowadays quality plays a vital role in all the products. Hence, the development in manufacturing process focuses on the fabrication of composite with high dimensional accuracy and also incurring low manufacturing cost. In this work, an investigation on machining parameters has been performed on jute-flax hybrid composite. Here, the two important responses characteristics like surface roughness and material removal rate are optimized by employing 3 machining input parameters. The input variables considered are drill bit diameter, spindle speed and feed rate. Machining is done on CNC vertical drilling machine at different levels of drilling parameters. Taguchi’s L16 orthogonal array is used for optimizing individual tool parameters. Analysis Of Variance is used to find the significance of individual parameters. The simultaneous optimization of the process parameters is done by grey relational analysis. The results of this investigation shows that, spindle speed and drill bit diameter have most effect on material removal rate and surface roughness followed by feed rate.
METHODOLOGY FOR DETERMINING OPTIMAL EXPOSURE PARAMETERS OF A HYPERSPECTRAL SCANNING SENSOR
Directory of Open Access Journals (Sweden)
P. Walczykowski
2016-06-01
Full Text Available The purpose of the presented research was to establish a methodology that would allow the registration of hyperspectral images with a defined spatial resolution on a horizontal plane. The results obtained within this research could then be used to establish the optimum sensor and flight parameters for collecting aerial imagery data using an UAV or other aerial system. The methodology is based on an user-selected optimal camera exposure parameters (i.e. time, gain value and flight parameters (i.e. altitude, velocity. A push-broom hyperspectral imager- the Headwall MicroHyperspec A-series VNIR was used to conduct this research. The measurement station consisted of the following equipment: a hyperspectral camera MicroHyperspec A-series VNIR, a personal computer with HyperSpec III software, a slider system which guaranteed the stable motion of the sensor system, a white reference panel and a Siemens star, which was used to evaluate the spatial resolution. Hyperspectral images were recorded at different distances between the sensor and the target- from 5m to 100m. During the registration process of each acquired image, many exposure parameters were changed, such as: the aperture value, exposure time and speed of the camera’s movement on the slider. Based on all of the registered hyperspectral images, some dependencies between chosen parameters had been developed: - the Ground Sampling Distance – GSD and the distance between the sensor and the target, - the speed of the camera and the distance between the sensor and the target, - the exposure time and the gain value, - the Density Number and the gain value. The developed methodology allowed us to determine the speed and the altitude of an unmanned aerial vehicle on which the sensor would be mounted, ensuring that the registered hyperspectral images have the required spatial resolution.
Metallic Fuel Casting Development and Parameter Optimization Simulations
International Nuclear Information System (INIS)
Fielding, Randall S.; Kennedy, J.R.; Crapps, J.; Unal, C.
2013-01-01
Conclusions: • Gravity casting is a feasible process for casting of metallic fuels: – May not be as robust as CGIC, more parameter dependent to find right “sweet spot” for high quality castings; – Fluid flow is very important and is affected by mold design, vent size, super heat, etc.; – Pressure differential assist was found to be detrimental. • Simulation found that vent location was important to allow adequate filling of mold; • Surface tension plays an important role in determining casting quality; • Casting and simulations high light the need for better characterized fluid physical and thermal properties; • Results from simulations will be incorporated in GACS design such as vent location and physical property characterization
Parameter and cost optimizations for a modular stellarator reactor
Hitchon, W. N. G.; Johnson, P. C.; Watson, C. J. H.
1983-02-01
The physical scaling and cost scaling of a modular stellarator reactor are described. It is shown that configurations based on l=2 are best able to support adequate beta, and physical relationships are derived which enable the geometry and parameters of an l=2 modular stellarator to be defined. A cost scaling for the components of the nuclear island is developed using Starfire (tokamak reactor study) engineering as a basis. It is shown that for minimum cost the stellarator should be of small aspect ratio. For a 4000 MWth plant, as Starfire, the optimum configuration is a 15 coil, 3 field period, l=2 device with a major radius of 16 m and a plasma minor radius of 2 m; and with a conservative wall loading of 2 MW/m2 and an average beta of 3.9%; the estimated cost per kilowatt (electrical) is marginally (7%) greater than Starfire.
Directory of Open Access Journals (Sweden)
Jeevanandham Arumugam
2009-01-01
Full Text Available In this paper a classical lead-lag power system stabilizer is used for demonstration. The stabilizer parameters are selected in such a manner to damp the rotor oscillations. The problem of selecting the stabilizer parameters is converted to a simple optimization problem with an eigen value based objective function and it is proposed to employ simulated annealing and particle swarm optimization for solving the optimization problem. The objective function allows the selection of the stabilizer parameters to optimally place the closed-loop eigen values in the left hand side of the complex s-plane. The single machine connected to infinite bus system and 10-machine 39-bus system are considered for this study. The effectiveness of the stabilizer tuned using the best technique, in enhancing the stability of power system. Stability is confirmed through eigen value analysis and simulation results and suitable heuristic technique will be selected for the best performance of the system.
Shaw, Calvin B; Prakash, Jaya; Pramanik, Manojit; Yalavarthy, Phaneendra K
2013-08-01
A computationally efficient approach that computes the optimal regularization parameter for the Tikhonov-minimization scheme is developed for photoacoustic imaging. This approach is based on the least squares-QR decomposition which is a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution enabled via finding an optimal regularization parameter. The computational efficiency and performance of the proposed method are shown using a test case of numerical blood vessel phantom, where the initial pressure is exactly known for quantitative comparison.
Akasaka, Thai; Fujimoto, Koji; Yamamoto, Takayuki; Okada, Tomohisa; Fushumi, Yasutaka; Yamamoto, Akira; Tanaka, Toshiyuki; Togashi, Kaori
2016-01-01
In Compressed Sensing (CS) of MRI, optimization of the regularization parameters is not a trivial task. We aimed to establish a method that could determine the optimal weights for regularization parameters in CS of time-of-flight MR angiography (TOF-MRA) by comparing various image metrics with radiologists' visual evaluation. TOF-MRA of a healthy volunteer was scanned using a 3T-MR system. Images were reconstructed by CS from retrospectively under-sampled data by varying the weights for the L...
A portable foot-parameter-extracting system
Zhang, MingKai; Liang, Jin; Li, Wenpan; Liu, Shifan
2016-03-01
In order to solve the problem of automatic foot measurement in garment customization, a new automatic footparameter- extracting system based on stereo vision, photogrammetry and heterodyne multiple frequency phase shift technology is proposed and implemented. The key technologies applied in the system are studied, including calibration of projector, alignment of point clouds, and foot measurement. Firstly, a new projector calibration algorithm based on plane model has been put forward to get the initial calibration parameters and a feature point detection scheme of calibration board image is developed. Then, an almost perfect match of two clouds is achieved by performing a first alignment using the Sampled Consensus - Initial Alignment algorithm (SAC-IA) and refining the alignment using the Iterative Closest Point algorithm (ICP). Finally, the approaches used for foot-parameterextracting and the system scheme are presented in detail. Experimental results show that the RMS error of the calibration result is 0.03 pixel and the foot parameter extracting experiment shows the feasibility of the extracting algorithm. Compared with the traditional measurement method, the system can be more portable, accurate and robust.
Electric power system applications of optimization
Momoh, James A
2008-01-01
Introduction Structure of a Generic Electric Power System Power System Models Power System Control Power System Security Assessment Power System Optimization as a Function of Time Review of Optimization Techniques Applicable to Power Systems Electric Power System Models Complex Power Concepts Three-Phase Systems Per Unit Representation Synchronous Machine Modeling Reactive Capability Limits Prime Movers and Governing Systems Automatic Gain Control Transmission Subsystems Y-Bus Incorporating the Transformer Effect Load Models Available Transfer Capability Illustrative Examples Power
Optimization of a particle optical system in a mutilprocessor environment
International Nuclear Information System (INIS)
Wei Lei; Yin Hanchun; Wang Baoping; Tong Linsu
2002-01-01
In the design of a charged particle optical system, many geometrical and electric parameters have to be optimized to improve the performance characteristics. In every optimization cycle, the electromagnetic field and particle trajectories have to be calculated. Therefore, the optimization of a charged particle optical system is limited by the computer resources seriously. Apart from this, numerical errors of calculation may also influence the convergence of merit function. This article studies how to improve the optimization of charged particle optical systems. A new method is used to determine the gradient matrix. With this method, the accuracy of the Jacobian matrix can be improved. In this paper, the charged particle optical system is optimized with a Message Passing Interface (MPI). The electromagnetic field, particle trajectories and gradients of optimization variables are calculated on networks of workstations. Therefore, the speed of optimization has been increased largely. It is possible to design a complicated charged particle optical system with optimum quality on a MPI environment. Finally, an electron gun for a cathode ray tube has been optimized on a MPI environment to verify the method proposed in this paper
Reliability-Based Optimization of Series Systems of Parallel Systems
DEFF Research Database (Denmark)
Enevoldsen, I.; Sørensen, John Dalsgaard
1993-01-01
Reliability-based design of structural systems is considered. In particular, systems where the reliability model is a series system of parallel systems are treated. A sensitivity analysis for this class of problems is presented. Optimization problems with series systems of parallel systems......) a sequential formulation based on optimality criteria; and (4) a sequential formulation including a new so-called bounds iteration method (BIM). Numerical tests indicate that the sequential technique including the BIM is particularly fast and stable. The B1M is not only effective in reliabilitybased...... optimization of series systems of parallel systems, but it is also efficient in reliability-based optimization of series systems in general....
Control and Estimation of Distributed Parameter Systems
Kappel, F; Kunisch, K
1998-01-01
Consisting of 23 refereed contributions, this volume offers a broad and diverse view of current research in control and estimation of partial differential equations. Topics addressed include, but are not limited to - control and stability of hyperbolic systems related to elasticity, linear and nonlinear; - control and identification of nonlinear parabolic systems; - exact and approximate controllability, and observability; - Pontryagin's maximum principle and dynamic programming in PDE; and - numerics pertinent to optimal and suboptimal control problems. This volume is primarily geared toward control theorists seeking information on the latest developments in their area of expertise. It may also serve as a stimulating reader to any researcher who wants to gain an impression of activities at the forefront of a vigorously expanding area in applied mathematics.
Dynamical System Approaches to Combinatorial Optimization
DEFF Research Database (Denmark)
Starke, Jens
2013-01-01
Several dynamical system approaches to combinatorial optimization problems are described and compared. These include dynamical systems derived from penalty methods; the approach of Hopfield and Tank; self-organizing maps, that is, Kohonen networks; coupled selection equations; and hybrid methods...
Directory of Open Access Journals (Sweden)
Xiao Yang
2017-11-01
Full Text Available The dynamic characteristics of power batteries directly affect the performance of electric vehicles, and the mathematical model is the basis for the design of a battery management system (BMS.Based on the electrode-averaged model of a lithium-ion battery, in view of the solid phase lithium-ion diffusion equation, the electrochemical model is simplified through the finite difference method. By analyzing the characteristics of the model and the type of parameters, the solid state diffusion kinetics are separated, and then the cascade parameter identifications are implemented with Particle Swarm Optimization. Eventually, the validity of the electrochemical model and the accuracy of model parameters are verified through 0.2–2 C multi-rates battery discharge tests of cell and road simulation tests of a micro pure electric vehicle under New European Driving Cycle (NEDC conditions. The results show that the estimated parameters can guarantee the output accuracy. In the test of cell, the voltage deviation of discharge is generally less than 0.1 V except the end; in road simulation test, the output is close to the actual value at low speed with the error around ±0.03 V, and at high speed around ±0.08 V.
Reliability Based Optimization of Structural Systems
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard
1987-01-01
The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability...... is estimated using first. order reliability methods ( FORM ). The design problem is formulated as the optimization problem to minimize a given cost function such that the reliability of the single elements satisfies given requirements or such that the systems reliability satisfies a given requirement....... For these optimization problems it is described how a sensitivity analysis can be performed. Next, new optimization procedures to solve the optimization problems are presented. Two of these procedures solve the system reliability based optimization problem sequentially using quasi-analytical derivatives. Finally...
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
Energy Technology Data Exchange (ETDEWEB)
Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-11
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
International Nuclear Information System (INIS)
Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.
2016-01-01
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Methods of Parametric Optimization of Thin-Walled Structures and Parameters which Influence on it
Directory of Open Access Journals (Sweden)
Kibkalo Anton
2016-01-01
Full Text Available The question of efficiency of thin-walled structures contains a number of contradictions. You need to select the best from all the existing structures on the criteria of optimization options. The search is conducted by varying of the parameters at parametric optimization. As a rule the aim of building structure optimization is reducing of material consumption, the labor input and cost. The costs of a particular variant of construction most full describes the given cost. There are two types of optimization parameters - immutable and varying. The result of the optimization of thin-walled beams will be a combination of parameters for each design situation in which provides the required strength and the minimum of the objective function - factory cost of production
Zang, Xizhe; Liu, Xinyu; Zhu, Yanhe; Zhao, Jie
2016-04-29
The study of human walking patterns mainly focuses on how control affects walking because control schemes are considered to be dominant in human walking. This study proposes that not only fine control schemes but also optimized body segment parameters are responsible for humans' low-energy walking. A passive dynamic walker provides the possibility of analyzing the effect of parameters on walking efficiency because of its ability to walk without any control. Thus, a passive dynamic walking model with a relatively human-like structure was built, and a parameter optimization process based on the gait sensitivity norm was implemented to determine the optimal mechanical parameters by numerical simulation. The results were close to human body parameters, thus indicating that humans can walk under a passive pattern based on their body segment parameters. A quasi-passive walking prototype was built on the basis of the optimization results. Experiments showed that a passive robot with optimized parameters could walk on level ground with only a simple hip actuation. This result implies that humans can walk under a passive pattern based on their body segment parameters with only simple control strategy implying that humans can opt to walk instinctively under a passive pattern.
Simulation and parameter optimization of polysilicon gate biaxial strained silicon MOSFETs
CSIR Research Space (South Africa)
Tsague, HD
2015-10-01
Full Text Available and Parameter Optimization of Polysilicon Gate Biaxial Strained Silicon MOSFETs Hippolyte Djonon Tsague Council for Scientific and Industrial Research (CSIR) Modelling and Digital Science (MDS) Pretoria, South Africa hdjonontsague...
NACP VPRM NEE Parameters Optimized to North American Flux Tower Sites, 2000-2006
National Aeronautics and Space Administration — This data set provides Vegetation Photosynthesis Respiration Model (VPRM) net ecosystem exchange (NEE) parameter values optimized to 65 flux tower sites across North...
Characterization and optimized control by means of multi-parameter controllers
Energy Technology Data Exchange (ETDEWEB)
Nielsen, Carsten; Hoeg, S.; Thoegersen, A. (Dan-Ejendomme, Hellerup (Denmark)) (and others)
2009-07-01
Poorly functioning HVAC systems (Heating, Ventilation and Air Conditioning), but also separate heating, ventilation and air conditioning systems are costing the Danish society billions of kroner every year: partly because of increased energy consumption and high operational and maintenance costs, but mainly due to reduced productivity and absence due to illness because of a poor indoor climate. Typically, the operation of buildings and installations takes place today with traditional build-ing automation, which is characterised by 1) being based on static considerations 2) the individual sensor being coupled with one actuator/valve, i.e. the sensor's signal is only used in one place in the system 3) subsystems often being controlled independently of each other 4) the dynamics in building constructions and systems which is very important to the system and comfort regulation is not being considered. This, coupled with the widespread tendency to use large glass areas in the facades without sufficient sun shading, means that it is difficult to optimise comfort and energy consumption. Therefore, the last 10-20 years have seen a steady increase in the complaints of the indoor climate in Danish buildings and, at the same time, new buildings often turn out to be considerably higher energy consuming than expected. The purpose of the present project is to investigate the type of multi parameter sensors which may be generated for buildings and further to carry out a preliminary evaluation on how such multi parameter controllers may be utilized for optimal control of buildings. The aim of the project isn't to develop multi parameter controllers - this requires much more effort than possible in the present project. The aim is to show the potential of using multi parameter sensors when controlling buildings. For this purpose a larger office building has been chosen - an office building with at high energy demand and complaints regarding the indoor climate. In order to
Safety parameter display system for Kalinin NPP
International Nuclear Information System (INIS)
Andreev, V.I.; Videneev, E.N.; Tissot, J.C.; Joonekindt, D.; Davidenko, N.N.; Shaftan, G.I.; Dounaev, V.G.; Neboyan, V.T.
1995-01-01
The paper discusses the safety parameter display system (SPDS), which is being designed for Kalinin NPP. The assessment of the safety status of the plant is done by the continuous monitoring of six critical safety functions and the corresponding status trees. Besides, a number of additional functions are realized within the scope of KlnNPP, aimed at providing the operator and the safety engineer in the main control room with more detailed information in accidental situation as well as during the normal operation. In particular, these functions are: archiving, data logs and alarm handling, safety actions monitoring, mnemonic diagrams indicating the state of main technological equipment and basic plant parameters, reference data, etc. As compared with the traditional scope of functions of this kind of systems, the functionality of KlnNPP SPDS is significantly expanded due to the inclusion in it the operator support function ''computerized procedures''. The basic SPDS implementation platform is ADACS of SEMA GROUP design. The system architecture includes two workstations in the main control room: one is for reactor operator and the other one for safety engineer. Every station has two CRT screens which ensures computerized procedures implementation and provides for extra services for the operator. Also, the information from the SPDS is transmitted to the local crisis center and to the crisis center of the State utility organization concern ''Rosenergoatom''. (author). 3 refs, 6 figs, 1 tab
Optimizing configurable parameters of model structure using genetic algorithms
Ujević, Željka
2011-01-01
Fractionation product properties of crude distillation unit (CDU) need to be monitored and controlled through feedback mechanism. Due to inability of on-line measurement, soft sensors for product quality estimation are developed. Soft sensors for kerosene distillation end point are developed using linear and nonlinear identification methods. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and labor...
Automatic x-ray image contrast enhancement based on parameter auto-optimization.
Qiu, Jianfeng; Harold Li, H; Zhang, Tiezhi; Ma, Fangfang; Yang, Deshan
2017-11-01
Insufficient image contrast associated with radiation therapy daily setup x-ray images could negatively affect accurate patient treatment setup. We developed a method to perform automatic and user-independent contrast enhancement on 2D kilo voltage (kV) and megavoltage (MV) x-ray images. The goal was to provide tissue contrast optimized for each treatment site in order to support accurate patient daily treatment setup and the subsequent offline review. The proposed method processes the 2D x-ray images with an optimized image processing filter chain, which consists of a noise reduction filter and a high-pass filter followed by a contrast limited adaptive histogram equalization (CLAHE) filter. The most important innovation is to optimize the image processing parameters automatically to determine the required image contrast settings per disease site and imaging modality. Three major parameters controlling the image processing chain, i.e., the Gaussian smoothing weighting factor for the high-pass filter, the block size, and the clip limiting parameter for the CLAHE filter, were determined automatically using an interior-point constrained optimization algorithm. Fifty-two kV and MV x-ray images were included in this study. The results were manually evaluated and ranked with scores from 1 (worst, unacceptable) to 5 (significantly better than adequate and visually praise worthy) by physicians and physicists. The average scores for the images processed by the proposed method, the CLAHE, and the best window-level adjustment were 3.92, 2.83, and 2.27, respectively. The percentage of the processed images received a score of 5 were 48, 29, and 18%, respectively. The proposed method is able to outperform the standard image contrast adjustment procedures that are currently used in the commercial clinical systems. When the proposed method is implemented in the clinical systems as an automatic image processing filter, it could be useful for allowing quicker and potentially more
Kumar, Rishi; Mevada, N. Ramesh; Rathore, Santosh; Agarwal, Nitin; Rajput, Vinod; Sinh Barad, AjayPal
2017-08-01
To improve Welding quality of aluminum (Al) plate, the TIG Welding system has been prepared, by which Welding current, Shielding gas flow rate and Current polarity can be controlled during Welding process. In the present work, an attempt has been made to study the effect of Welding current, current polarity, and shielding gas flow rate on the tensile strength of the weld joint. Based on the number of parameters and their levels, the Response Surface Methodology technique has been selected as the Design of Experiment. For understanding the influence of input parameters on Ultimate tensile strength of weldment, ANOVA analysis has been carried out. Also to describe and optimize TIG Welding using a new metaheuristic Nature - inspired algorithm which is called as Firefly algorithm which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of firefly algorithm is presented together with an analytical, mathematical modeling to optimize the TIG Welding process by a single equivalent objective function.
Artificial intelligence in power system optimization
Ongsakul, Weerakorn
2013-01-01
With the considerable increase of AI applications, AI is being increasingly used to solve optimization problems in engineering. In the past two decades, the applications of artificial intelligence in power systems have attracted much research. This book covers the current level of applications of artificial intelligence to the optimization problems in power systems. This book serves as a textbook for graduate students in electric power system management and is also be useful for those who are interested in using artificial intelligence in power system optimization.
Optimization of some eco-energetic systems
International Nuclear Information System (INIS)
Purica, I.; Pavelescu, M.; Stoica, M.
1976-01-01
An optimization problem of two eco-energetic systems is described. The first one is close to the actual eco-energetic system in Romania, while the second is a new one, based on nuclear energy as primary source and hydrogen energy as secondary source. The optimization problem solved is to find the optimal structure of the systems so that the objective functions adopted, namely unitary energy cost C and total pollution P, to be minimum at the same time. The problem can be modelated with a bimatrix cooperative mathematical game without side payments. We demonstrate the superiority of the new eco-energetic system. (author)
Directory of Open Access Journals (Sweden)
Wenz Frederik
2009-09-01
Full Text Available Abstract Background Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI guided system was developed and examined. Methods The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS. Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS, was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints. The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Results Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02% and membership functions (3.9%, thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. Conclusion The
Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang
2009-09-25
Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 +/- 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. The study demonstrated a feasible way to automatically perform
International Nuclear Information System (INIS)
Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang
2009-01-01
Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be 'translated' to a set of 'if-then rules' for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the 'behavior' of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. The study demonstrated a feasible way
DEFF Research Database (Denmark)
Pingen, Georg; Evgrafov, Anton; Maute, Kurt
2009-01-01
We present an adjoint parameter sensitivity analysis formulation and solution strategy for the lattice Boltzmann method (LBM). The focus is on design optimization applications, in particular topology optimization. The lattice Boltzmann method is briefly described with an in-depth discussion of so...
Optimization of parameters for coverage of low molecular weight proteins.
Müller, Stephan A; Kohajda, Tibor; Findeiss, Sven; Stadler, Peter F; Washietl, Stefan; Kellis, Manolis; von Bergen, Martin; Kalkhof, Stefan
2010-12-01
Proteins with molecular weights of cycle control. Despite their importance, the coverage of smaller proteins in standard proteome studies is rather sparse. Here we investigated biochemical and mass spectrometric parameters that influence coverage and validity of identification. The underrepresentation of low molecular weight (LMW) proteins may be attributed to the low numbers of proteolytic peptides formed by tryptic digestion as well as their tendency to be lost in protein separation and concentration/desalting procedures. In a systematic investigation of the LMW proteome of Escherichia coli, a total of 455 LMW proteins (27% of the 1672 listed in the SwissProt protein database) were identified, corresponding to a coverage of 62% of the known cytosolic LMW proteins. Of these proteins, 93 had not yet been functionally classified, and five had not previously been confirmed at the protein level. In this study, the influences of protein extraction (either urea or TFA), proteolytic digestion (solely, and the combined usage of trypsin and AspN as endoproteases) and protein separation (gel- or non-gel-based) were investigated. Compared to the standard procedure based solely on the use of urea lysis buffer, in-gel separation and tryptic digestion, the complementary use of TFA for extraction or endoprotease AspN for proteolysis permits the identification of an extra 72 (32%) and 51 proteins (23%), respectively. Regarding mass spectrometry analysis with an LTQ Orbitrap mass spectrometer, collision-induced fragmentation (CID and HCD) and electron transfer dissociation using the linear ion trap (IT) or the Orbitrap as the analyzer were compared. IT-CID was found to yield the best identification rate, whereas IT-ETD provided almost comparable results in terms of LMW proteome coverage. The high overlap between the proteins identified with IT-CID and IT-ETD allowed the validation of 75% of the identified proteins using this orthogonal fragmentation technique. Furthermore, a new
OPTIMAL CONTROL ALGORITHMS FOR SECOND ORDER SYSTEMS
Danilo Pelusi; Raffaele Mascella
2013-01-01
Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination o...
Maintenance resources optimization applied to a manufacturing system
International Nuclear Information System (INIS)
Fiori de Castro, Helio; Lucchesi Cavalca, Katia
2006-01-01
This paper presents an availability optimization of an engineering system assembled in a series configuration, with redundancy of units and corrective maintenance resources as optimization parameters. The aim is to reach maximum availability, considering as constraints installation and corrective maintenance costs, weight and volume. The optimization method uses a Genetic Algorithm based on biological concepts of species evolution. It is a robust method, as it does not converge to a local optimum. It does not require the use of differential calculus, thus facilitating computational implementation. Results indicate that the methodology is suitable to solve a wide range of engineering design problems involving allocation of redundancies and maintenance resources
Maintenance resources optimization applied to a manufacturing system
Energy Technology Data Exchange (ETDEWEB)
Fiori de Castro, Helio [UNICAMP-FEM, Department of Mechanical Design, P.O. Box 6051, Campinas, SP 13083-970 (Brazil); Lucchesi Cavalca, Katia [UNICAMP-FEM, Department of Mechanical Design, P.O. Box 6051, Campinas, SP 13083-970 (Brazil)]. E-mail: katia@fem.unicamp.br
2006-04-15
This paper presents an availability optimization of an engineering system assembled in a series configuration, with redundancy of units and corrective maintenance resources as optimization parameters. The aim is to reach maximum availability, considering as constraints installation and corrective maintenance costs, weight and volume. The optimization method uses a Genetic Algorithm based on biological concepts of species evolution. It is a robust method, as it does not converge to a local optimum. It does not require the use of differential calculus, thus facilitating computational implementation. Results indicate that the methodology is suitable to solve a wide range of engineering design problems involving allocation of redundancies and maintenance resources.
Accelerator optimization using a network control and acquisition system
International Nuclear Information System (INIS)
Geddes, Cameron G.R.; Catravas, P.E.; Faure, Jerome; Toth, Csaba; Tilborg, J. van; Leemans, Wim P.
2002-01-01
Accelerator optimization requires detailed study of many parameters, indicating the need for remote control and automated data acquisition systems. A control and data acquisition system based on a network of commodity PCs and applications with standards based inter-application communication is being built for the l'OASIS accelerator facility. This system allows synchronous acquisition of data at high (> 1 Hz) rates and remote control of the accelerator at low cost, allowing detailed study of the acceleration process
Volk, J. M.; Turner, M. A.; Huntington, J. L.; Gardner, M.; Tyler, S.; Sheneman, L.
2016-12-01
Many distributed models that simulate watershed hydrologic processes require a collection of multi-dimensional parameters as input, some of which need to be calibrated before the model can be applied. The Precipitation Runoff Modeling System (PRMS) is a physically-based and spatially distributed hydrologic model that contains a considerable number of parameters that often need to be calibrated. Modelers can also benefit from uncertainty analysis of these parameters. To meet these needs, we developed a modular framework in Python to conduct PRMS parameter optimization, uncertainty analysis, interactive visual inspection of parameters and outputs, and other common modeling tasks. Here we present results for multi-step calibration of sensitive parameters controlling solar radiation, potential evapo-transpiration, and streamflow in a PRMS model that we applied to the snow-dominated Dry Creek watershed in Idaho. We also demonstrate how our modular approach enables the user to use a variety of parameter optimization and uncertainty methods or easily define their own, such as Monte Carlo random sampling, uniform sampling, or even optimization methods such as the downhill simplex method or its commonly used, more robust counterpart, shuffled complex evolution.
Optimal policies for identification of stochastic linear systems
Lopez-Toledo, A. A.; Athans, M.
1975-01-01
The problem of designing closed-loop policies for identification of multiinput-multioutput linear discrete-time systems with random time-varying parameters is considered in this paper using a Bayesian approach. A sensitivity index gives a measure of performance for the closed-loop laws. The computation of the optimal laws is shown to be nontrivial, an exercise in stochastic control, but open-loop, affine, and open-loop feedback optimal inputs are shown to yield tractable problems. Numerical examples are given. For time-invariant systems, the criterion considered is shown to be related to the trace of the information matrix associated with the system.
Energetic Optimal Control Of Adjustable Drive Systems
Directory of Open Access Journals (Sweden)
Ion BIVOL
2002-12-01
Full Text Available n the paper is developed a new control strategy for the adjustable speed drives. The strategy consists in the energetic optimal control of the dynamic regimes as starting, stopping and reversing. The main developed problems: formulation of energetic optimal problem, solution, experimental results via simulation and some considerations concerning the use of the control. The optimal developed solution can be applied for the both AC and DC drives, but only for linear systems.
Poscharny, K.
2018-04-07
A methodology for the synthesis of oxetanes from benzophenone and furan derivatives is presented. UV-light irradiation in batch and flow systems allowed the [2 + 2] cycloaddition reaction to proceed and a broad range of oxetanes could be synthesized in manual and automated fashion. The identification of high-yielding reaction parameters was achieved through a new self-optimizing photoreactor system.
Parameter estimation using meta-heuristics in systems biology: a comprehensive review.
Sun, Jianyong; Garibaldi, Jonathan M; Hodgman, Charlie
2012-01-01
This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.
Manjunath, S V; Kumar, S Mathava; Ngo, Huu Hao; Guo, Wenshan
2017-12-06
Metronidazole (MNZ) removal by two adsorbents, i.e., concrete-containing graphene (CG) and powder-activated carbon (PAC), was investigated via batch-mode experiments and the outcomes were used to analyze the kinetics, equilibrium and thermodynamics of MNZ adsorption. MNZ sorption on CG and PAC has followed the pseudo-second-order kinetic model, and the thermodynamic parameters revealed that MNZ adsorption was spontaneous on PAC and non-spontaneous on CG. Subsequently, two-parameter isotherm models, i.e., Langmuir, Freundlich, Temkin, Dubinin-Radushkevich and Elovich models, were applied to evaluate the MNZ adsorption capacity. The maximum MNZ adsorption capacities ([Formula: see text]) of PAC and CG were found to be between 25.5-32.8 mg/g and 0.41-0.002 mg/g, respectively. Subsequently, the effects of pH, temperature and adsorbent dosage on MNZ adsorption were evaluated by a central composite design (CCD) approach. The CCD experiments have pointed out the complete removal of MNZ at a much lower PAC dosage by increasing the system temperature (i.e., from 20°C to 40°C). On the other hand, a desorption experiment has shown 3.5% and 1.7% MNZ removal from the surface of PAC and CG, respectively, which was insignificant compared to the sorbed MNZ on the surface by adsorption. The overall findings indicate that PAC and CG with higher graphene content could be useful in MNZ removal from aqueous systems.
Optimal Design of the Transverse Flux Machine Using a Fitted Genetic Algorithm with Real Parameters
DEFF Research Database (Denmark)
Argeseanu, Alin; Ritchie, Ewen; Leban, Krisztina Monika
2012-01-01
This paper applies a fitted genetic algorithm (GA) to the optimal design of transverse flux machine (TFM). The main goal is to provide a tool for the optimal design of TFM that is an easy to use. The GA optimizes the analytic basic design of two TFM topologies: the C-core and the U-core. First......, the GA was designed with real parameters. A further, objective of the fitted GA is minimization of the computation time, related to the number of individuals, the number of generations and the types of operators and their specific parameters....
Zhang, Xing-Yi; Chen, Da-Wei; Jin, Jie; Lu, Wei
2009-10-01
Artificial neural network (ANN) is a multi-objective optimization method that needs mathematic and statistic knowledge which restricts its application in the pharmaceutical research area. An artificial neural network parameters optimization software (ANNPOS) programmed by the Visual Basic language was developed to overcome this shortcoming. In the design of a sustained release formulation, the suitable parameters of ANN were estimated by the ANNPOS. And then the Matlab 5.0 Neural Network Toolbox was used to determine the optimal formulation. It showed that the ANNPOS reduced the complexity and difficulty in the ANN's application.
The primary ion source for construction and optimization of operation parameters
International Nuclear Information System (INIS)
Synowiecki, A.; Gazda, E.
1986-01-01
The construction of primary ion source for SIMS has been presented. The influence of individual operation parameters on the properties of ion source has been investigated. Optimization of these parameters has allowed to appreciate usefulness of the ion source for SIMS study. 14 refs., 8 figs., 2 tabs. (author)
Sensitivity of the optimal parameter settings for a LTE packet scheduler
Fernandez-Diaz, I.; Litjens, R.; van den Berg, C.A.; Dimitrova, D.C.; Spaey, K.
Advanced packet scheduling schemes in 3G/3G+ mobile networks provide one or more parameters to optimise the trade-off between QoS and resource efficiency. In this paper we study the sensitivity of the optimal parameter setting for packet scheduling in LTE radio networks with respect to various
Optimization of accelerator parameters using normal form methods on high-order transfer maps
Energy Technology Data Exchange (ETDEWEB)
Snopok, Pavel [Michigan State Univ., East Lansing, MI (United States)
2007-05-01
in a way that is easy to understand, such important characteristics as the strengths of the resonances and the tune shifts with amplitude and various parameters of the system are calculated. Each major section is supplied with the results of applying various numerical optimization methods to the problems stated. The emphasis is made on the efficiency comparison of various approaches and methods. The main simulation tool is the arbitrary order code COSY INFINITY written by M. Berz, K. Makino, et al. at Michigan State University. Also, the code MAD is utilized to design the 750 x 750 GeV Muon Collider storage ring baseline lattice.
Toulabi, Mohammadreza; Bahrami, Shahab; Ranjbar, Ali Mohammad
2018-03-01
In most of the existing studies, the frequency response in the variable speed wind turbines (VSWTs) is simply realized by changing the torque set-point via appropriate inputs such as frequency deviations signal. However, effective dynamics and systematic process design have not been comprehensively discussed yet. Accordingly, this paper proposes a proportional-derivative frequency controller and investigates its performance in a wind farm consisting of several VSWTs. A band-pass filter is deployed before the proposed controller to avoid responding to either steady state frequency deviations or high rate of change of frequency. To design the controller, the frequency model of the wind farm is first characterized. The proposed controller is then designed based on the obtained open loop system. The stability region associated with the controller parameters is analytically determined by decomposing the closed-loop system's characteristic polynomial into the odd and even parts. The performance of the proposed controller is evaluated through extensive simulations in MATLAB/Simulink environment in a power system comprising a high penetration of VSWTs equipped with the proposed controller. Finally, based on the obtained feasible area and appropriate objective function, the optimal values associated with the controller parameters are determined using the genetic algorithm (GA). Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta
2016-06-01
With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.
Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
Directory of Open Access Journals (Sweden)
Jie-Sheng Wang
2015-12-01
Full Text Available The gravitational search algorithm (GSA is a kind of swarm intelligence optimization algorithm based on the law of gravitation. The parameter initialization of all swarm intelligence optimization algorithms has an important influence on the global optimization ability. Seen from the basic principle of GSA, the convergence rate of GSA is determined by the gravitational constant and the acceleration of the particles. The optimization performances on six typical test functions are verified by the simulation experiments. The simulation results show that the convergence speed of the GSA algorithm is relatively sensitive to the setting of the algorithm parameters, and the GSA parameter can be used flexibly to improve the algorithm’s convergence velocity and improve the accuracy of the solutions.
Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle
Directory of Open Access Journals (Sweden)
Bambang Wahono
2015-07-01
Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.
Optimization of machining parameters of turning operations based on multi performance criteria
Directory of Open Access Journals (Sweden)
N.K.Mandal
2013-01-01
Full Text Available The selection of optimum machining parameters plays a significant role to ensure quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process. For many years, multi-objective optimization of turning based on inherent complexity of process is a competitive engineering issue. This study investigates multi-response optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA. Confirmation test is conducted for the optimal machining parameters to validate the test result. Various turning parameters, such as spindle speed, feed and depth of cut are considered. Experiments are designed and conducted based on full factorial design of experiment.
Optimal Design of Material and Process Parameters in Powder Injection Molding
Ayad, G.; Barriere, T.; Gelin, J. C.; Song, J.; Liu, B.
2007-04-01
The paper is concerned with optimization and parametric identification for the different stages in Powder Injection Molding process that consists first in injection of powder mixture with polymer binder and then to the sintering of the resulting powders part by solid state diffusion. In the first part, one describes an original methodology to optimize the process and geometry parameters in injection stage based on the combination of design of experiments and an adaptive Response Surface Modeling. Then the second part of the paper describes the identification strategy that one proposes for the sintering stage, using the identification of sintering parameters from dilatometeric curves followed by the optimization of the sintering process. The proposed approaches are applied to the optimization of material and process parameters for manufacturing a ceramic femoral implant. One demonstrates that the proposed approach give satisfactory results.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-01
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO-ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO-ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO-ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO-ACO is a very powerful tool for parameter estimation with high accuracy and low deviations.
Optimizing sonication parameters for dispersion of single-walled carbon nanotubes
Energy Technology Data Exchange (ETDEWEB)
Yu, Haibo [Fraunhofer Institute for Electronic Nano Systems (Fraunhofer ENAS), 09126 Chemnitz (Germany); Graduate University of the Chinese Academy of Sciences, Beijing (China); State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang (China); Hermann, Sascha, E-mail: sascha.hermann@zfm.tu-chemnitz.de [Center for Microtechnologies (ZfM), Chemnitz University of Technology, 09126 Chemnitz (Germany); Schulz, Stefan E.; Gessner, Thomas [Fraunhofer Institute for Electronic Nano Systems (Fraunhofer ENAS), 09126 Chemnitz (Germany); Center for Microtechnologies (ZfM), Chemnitz University of Technology, 09126 Chemnitz (Germany); Dong, Zaili [State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang (China); Li, Wen J., E-mail: wenjungli@gmail.com [State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang (China); Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong SAR (China)
2012-10-26
Graphical abstract: We study the dispersing behavior of SWCNTs based on the surfactant and the optimization of sonication parameters including the sonication power and running time. Highlights: Black-Right-Pointing-Pointer We study the optimization of sonication for the surfactant-based dispersion of SWCNTs. Black-Right-Pointing-Pointer The absorption spectrum of SWCNT solution strongly depend on the sonication conditions. Black-Right-Pointing-Pointer The sonication process has an important influence on the average length and diameters of SWCNTs in solution. Black-Right-Pointing-Pointer Centrifugation mainly contributes to the decrease of nonresonant absorption background. Black-Right-Pointing-Pointer Under the same sonication parameters, the large-diameter tip performs dispersion of SWCNTs better than the small-diameter tip. -- Abstract: Non-covalent functionalization based on surfactants has become one of the most common methods for dispersing of single-walled carbon nanotubes (SWCNTs). Previously, efforts have mainly been focused on experimenting with different surfactant systems, varying their concentrations and solvents. However sonication plays a very important role during the surfactant-based dispersion process for SWCNTs. The sonication treatment enables the surfactant molecules to adsorb onto the surface of SWCNTs by overcoming the interactions induced by the hydrophobic, electrostatic and van der Waals forces. This work describes a systematic study of the influence of the sonication power and time on the dispersion of SWCNTs. UV-vis-NIR absorption spectra is used to analyze and to evaluate the dispersion of SWCNTs in an aqueous solution of 1 w/v% sodium deoxycholate (DOC) showing that the resonant and nonresonant background absorption strongly depends on the sonication conditions. Furthermore, the diameter and length of SWCNTs under different sonication parameters are investigated using atomic force microscopy (AFM).
Directory of Open Access Journals (Sweden)
A. G. Bakhanovich
2006-01-01
Full Text Available Impact of technological process parameters (pressing pressure, duration and vulcanization temperature on drive toothed belt longevity has been investigated. Optimum parameters of the technological process that permit to improve a belt resource have been determined. Methodology for determination of a number of cycles intended for loading of belt teeth according to a test duration and transmission parameters has been developed. The paper presents results of industrial resource tests of drive toothed belts manufactured in accordance with an optimized technology
Directory of Open Access Journals (Sweden)
Huanqing Cui
2017-03-01
Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.
Reliability-Based Optimization of Series Systems of Parallel Systems
DEFF Research Database (Denmark)
Enevoldsen, I.; Sørensen, John Dalsgaard
Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...... problems are described. Numerical tests indicate that a sequential technique called the bounds iteration method (BIM) is particularly fast and stable....
International Nuclear Information System (INIS)
Casarosa, C.; Donatini, F.; Franco, A.
2004-01-01
The optimization of the heat recovery steam generator (HRSG) is particularly interesting for the combined plants design in order to maximise the work obtained in the vapour cycle. A detailed optimization of the HRSG is a very difficult problem, depending on several variables. The first step is represented by the optimization of the operating parameters. These are the number of pressure levels, the pressures, the mass flow ratio, and the inlet temperatures to the HRSG sections. The operating parameters can be determined by means both of a thermodynamic and of a thermoeconomic analysis, minimising a suitable objective function by analytical or numerical mathematical methods. In the paper, thermodynamic optimization is based on the minimization of exergy losses, while the thermoeconomic optimization is based on the minimization of the total HRSG cost, after the reduction to a common monetary base of the costs of exergy losses and of installation
Dynamic Parameter-Control Chaotic System.
Hua, Zhongyun; Zhou, Yicong
2016-12-01
This paper proposes a general framework of 1-D chaotic maps called the dynamic parameter-control chaotic system (DPCCS). It has a simple but effective structure that uses the outputs of a chaotic map (control map) to dynamically control the parameter of another chaotic map (seed map). Using any existing 1-D chaotic map as the control/seed map (or both), DPCCS is able to produce a huge number of new chaotic maps. Evaluations and comparisons show that chaotic maps generated by DPCCS are very sensitive to their initial states, and have wider chaotic ranges, better unpredictability and more complex chaotic behaviors than their seed maps. Using a chaotic map of DPCCS as an example, we provide a field-programmable gate array design of this chaotic map to show the simplicity of DPCCS in hardware implementation, and introduce a new pseudo-random number generator (PRNG) to investigate the applications of DPCCS. Analysis and testing results demonstrate the excellent randomness of the proposed PRNG.
Energy Technology Data Exchange (ETDEWEB)
Eldred, Michael Scott; Vigil, Dena M.; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia (Sandia National Laboratories, Livermore, CA); Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Eddy, John P.
2011-12-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.
Parameter and state estimation in nonlinear dynamical systems
Creveling, Daniel R.
and driven by the synchronization error to increase coupling when needed. This method, along with a suitable cost function, allows for the determination of model parameters without the complexity of calculating Lyapunov exponents. Lastly, a method is developed allowing the coupling to vary in time without the constraint of following a differential equation. This approach shows the equivalence of the parameter and state estimation problem to that of tracking within an optimal control framework. This equivalence allows the application of powerful numerical methods that provide robust practical tools for model development and validation. Examples of each of these methods are presented with both simulated data and data measured from electrical circuit implementations of several dynamical systems.
Periodic orbits of hybrid systems and parameter estimation via AD.
Energy Technology Data Exchange (ETDEWEB)
Guckenheimer, John. (Cornell University); Phipps, Eric Todd; Casey, Richard (INRIA Sophia-Antipolis)
2004-07-01
Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high fidelity as periodic orbits of dynamical systems is challenging because: (1) (most) nonlinear differential equations can only be solved numerically; (2) accurate computation requires solving boundary value problems; (3) many problems and solutions are only piecewise smooth; (4) many problems require solving differential-algebraic equations; (5) sensitivity information for parameter dependence of solutions requires solving variational equations; and (6) truncation errors in numerical integration degrade performance of optimization methods for parameter estimation. In addition, mathematical models of biological processes frequently contain many poorly-known parameters, and the problems associated with this impedes the construction of detailed, high-fidelity models. Modelers are often faced with the difficult problem of using simulations of a nonlinear model, with complex dynamics and many parameters, to match experimental data. Improved computational tools for exploring parameter space and fitting models to data are clearly needed. This paper describes techniques for computing periodic orbits in systems of hybrid differential-algebraic equations and parameter estimation methods for fitting these orbits to data. These techniques make extensive use of automatic differentiation to accurately and efficiently evaluate derivatives for time integration, parameter sensitivities, root finding and optimization. The boundary value problem representing a periodic orbit in a hybrid system of differential algebraic equations is discretized via multiple-shooting using a high-degree Taylor series integration method [GM00, Phi03]. Numerical solutions to the shooting equations are then estimated by a Newton process yielding an approximate periodic orbit. A metric is defined for computing the distance
Parameters Optimization for a Novel Vacuum Laser Acceleration Test at BNL-ATF
Shao, Lei; Zhou, Feng
2005-01-01
This paper presents a new VLA theory model which has revealed that the injection electrons with low energy and small incident angle relative to the laser beam are captured and significantly accelerated in a strong laser field. For the further step for verifying the novel-VLA mechanics, we propose to use the BNL-ATF Terawatt CO2 laser and a high-brightness electron beam to carry out a proof-of-principle beam experiment. Experiment setup including the laser injection optics and electron extraction system and beam diagnostics is presented. Extensive optimized simulation results with ATF practical parameters are also presented, which shows that even when the laser intensity is not very high, the net energy gain still can be seen obviously. This could be prospect for a new revolution of vacuum laser acceleration.
Optimization problems in the Bulgarian electoral system
Konstantinov, Mihail; Yanev, Kostadin; Pelova, Galina; Boneva, Juliana
2013-12-01
In this paper we consider several optimization problems for the Bulgarian bi-proportional electoral systems. Experiments with data from real elections are presented. In this way a series of previous investigations of the authors is further developed.
Optimal beamforming in MIMO systems with HPA nonlinearity
Qi, Jian
2010-09-01
In this paper, multiple-input multiple-output (MIMO) transmit beamforming (TB) systems under the consideration of nonlinear high-power amplifiers (HPAs) are investigated. The optimal beamforming scheme, with the optimal beamforming weight vector and combining vector, is proposed for MIMO systems with HPA nonlinearity. The performance of the proposed MIMO beamforming scheme in the presence of HPA nonlinearity is evaluated in terms of average symbol error probability (SEP), outage probability and system capacity, considering transmission over uncorrelated quasi-static frequency-flat Rayleigh fading channels. Numerical results are provided and show the effects of several system parameters, namely, parameters of nonlinear HPA, numbers of transmit and receive antennas, and modulation order of phase-shift keying (PSK), on performance. ©2010 IEEE.
Analysis of reliability parameters for complicated information measurement systems
Sydor, Andriy
2012-01-01
A method of analysis of reliability parameters for complicated systems by means of generating functions is developed taking account of aging of the systems output elements. Main reliability parameters of complicated information measurement systems are examined in this paper.
Combustion optimization using an expert system and neural networks
Energy Technology Data Exchange (ETDEWEB)
Levy, E.; Pfahler, J.; Miles, J.; Keller, F.; Lee, P.; Woldehanna, S. [Lehigh Univ., Bethlehem, PA (United States). Energy Research Center; Williams, S. [Potomac Electric Power Co., Upper Marlboro, MD (United States)
1996-12-31
Lehigh University`s Energy Research Center and the Potomac Electric Power Company have been developing software for use by plant personnel in turning a pulverized coal-fired boiler to achieve optimized combustion. The software is based on the interactions of an expert system, neural network, and a mathematical optimization algorithm. It uses the expert system to safely guide the plant engineer through a series of parametric boiler tests and gather a data base which characterizes boiler operation over a wide range of conditions. The neural network portion develops non-linear mapping functions between the outputs of NO{sub x}, heat rate, LOI, opacity, and the controllable boiler input parameters. These mapping functions are then analyzed by the mathematical optimization algorithm and the optimal boiler operating conditions are identified. This paper describes the application of the software to corner-fired boilers with either conventional or low NO{sub x} burners and overfire air.
Energy Technology Data Exchange (ETDEWEB)
Turner, D P; Ritts, W D; Wharton, S; Thomas, C; Monson, R; Black, T A
2009-02-26
The combination of satellite remote sensing and carbon cycle models provides an opportunity for regional to global scale monitoring of terrestrial gross primary production, ecosystem respiration, and net ecosystem production. FPAR (the fraction of photosynthetically active radiation absorbed by the plant canopy) is a critical input to diagnostic models, however little is known about the relative effectiveness of FPAR products from different satellite sensors nor about the sensitivity of flux estimates to different parameterization approaches. In this study, we used multiyear observations of carbon flux at four eddy covariance flux tower sites within the conifer biome to evaluate these factors. FPAR products from the MODIS and SeaWiFS sensors, and the effects of single site vs. cross-site parameter optimization were tested with the CFLUX model. The SeaWiFs FPAR product showed greater dynamic range across sites and resulted in slightly reduced flux estimation errors relative to the MODIS product when using cross-site optimization. With site-specific parameter optimization, the flux model was effective in capturing seasonal and interannual variation in the carbon fluxes at these sites. The cross-site prediction errors were lower when using parameters from a cross-site optimization compared to parameter sets from optimization at single sites. These results support the practice of multisite optimization within a biome for parameterization of diagnostic carbon flux models.
Application of Factorial Design for Gas Parameter Optimization in CO2 Laser Welding
DEFF Research Database (Denmark)
Gong, Hui; Dragsted, Birgitte; Olsen, Flemming Ove
1997-01-01
The effect of different gas process parameters involved in CO2 laser welding has been studied by applying two-set of three-level complete factorial designs. In this work 5 gas parameters, gas type, gas flow rate, gas blowing angle, gas nozzle diameter, gas blowing point-offset, are optimized...... to be a very useful tool for parameter optimi-zation in laser welding process. Keywords: CO2 laser welding, gas parameters, factorial design, Analysis of Variance........ The bead-on-plate welding specimens are evaluated by a number of quality char-acteristics, such as the penetration depth and the seam width. The significance of the gas pa-rameters and their interactions are based on the data found by the Analysis of Variance-ANOVA. This statistic methodology is proven...
Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS
Energy Technology Data Exchange (ETDEWEB)
Manivannan, R.; Kumar, M. Pradeep [CEG, Anna University, Chennai (India)
2016-01-15
The Technique for order preference by similarity to ideal solution (TOPSIS) method of optimization is used to analyze the process parameters of the micro-Electrical discharge machining (micro-EDM) of an AISI 304 steel with multi-performance characteristics. The Taguchi method of experimental design L27 is performed to obtain the optimal parameters for inputs, including feed rate, current, pulse on time, and gap voltage. Several output responses, such as the material removal rate, electrode wear rate, overcut, taper angle, and circularity at entry and exit points, are analyzed for the optimal conditions. Among all the investigated parameters, feed rate exerts a greater influence on the hole quality. ANOVA is employed to identify the contribution of each experiment. The optimal level of parameter setting is maintained at a feed rate of 4 μm/s, a current of 10 A, a pulse on time of 10 μs, and a gap voltage of 10 V. Scanning electron microscope analysis is conducted to examine the hole quality. The experimental results indicate that the optimal level of the process parameter setting over the overall performance of the micro-EDM is improved through TOPSIS.
A Taguchi approach on optimal process control parameters for HDPE pipe extrusion process
Sharma, G. V. S. S.; Rao, R. Umamaheswara; Rao, P. Srinivasa
2017-12-01
High-density polyethylene (HDPE) pipes find versatile applicability for transportation of water, sewage and slurry from one place to another. Hence, these pipes undergo tremendous pressure by the fluid carried. The present work entails the optimization of the withstanding pressure of the HDPE pipes using Taguchi technique. The traditional heuristic methodology stresses on a trial and error approach and relies heavily upon the accumulated experience of the process engineers for determining the optimal process control parameters. This results in setting up of less-than-optimal values. Hence, there arouse a necessity to determine optimal process control parameters for the pipe extrusion process, which can ensure robust pipe quality and process reliability. In the proposed optimization strategy, the design of experiments (DoE) are conducted wherein different control parameter combinations are analyzed by considering multiple setting levels of each control parameter. The concept of signal-to-noise ratio ( S/ N ratio) is applied and ultimately optimum values of process control parameters are obtained as: pushing zone temperature of 166 °C, Dimmer speed at 08 rpm, and Die head temperature to be 192 °C. Confirmation experimental run is also conducted to verify the analysis and research result and values proved to be in synchronization with the main experimental findings and the withstanding pressure showed a significant improvement from 0.60 to 1.004 Mpa.
Optimization of long range potential interaction parameters in ion mobility spectrometry
Wu, Tianyang; Derrick, Joseph; Nahin, Minal; Chen, Xi; Larriba-Andaluz, Carlos
2018-02-01
The problem of optimizing Lennard-Jones (L-J) potential parameters to perform collision cross section (CCS) calculations in ion mobility spectrometry has been undertaken. The experimental CCS of 16 small organic molecules containing carbon, hydrogen, oxygen, nitrogen, and fluoride in N2 was compared to numerical calculations using Density Functional Theory (DFT). CCS calculations were performed using the momentum transfer algorithm IMoS and a 4-6-12 potential without incorporating the ion-quadrupole potential. A ceteris paribus optimization method was used to optimize the intercept σ and potential well-depth ɛ for the given atoms. This method yields important information that otherwise would remain concealed. Results show that the optimized L-J parameters are not necessarily unique with intercept and well-depth following an exponential relation at an existing line of minimums. Similarly, the method shows that some molecules containing atoms of interest may be ill-conditioned candidates to perform optimizations of the L-J parameters. The final calculated CCSs for the chosen parameters differ 1% on average from their experimental counterparts. This result conveys the notion that DFT calculations can indeed be used as potential candidates for CCS calculations and that effects, such as the ion-quadrupole potential or diffuse scattering, can be embedded into the L-J parameters without loss of accuracy but with a large increase in computational efficiency.
International Nuclear Information System (INIS)
Carver, M.B.; Austin, C.F.; Ross, N.E.
1980-02-01
This report discusses the mechanics of automated parameter identification in simulation packages, and reviews available integration and optimization algorithms and their interaction within the recently developed optimization options in the FORSIM and MACKSIM simulation packages. In the MACKSIM mass-action chemical kinetics simulation package, the form and structure of the ordinary differential equations involved is known, so the implementation of an optimizing option is relatively straightforward. FORSIM, however, is designed to integrate ordinary and partial differential equations of abritrary definition. As the form of the equations is not known in advance, the design of the optimizing option is more intricate, but the philosophy could be applied to most simulation packages. In either case, however, the invocation of the optimizing interface is simple and user-oriented. Full details for the use of the optimizing mode for each program are given; specific applications are used as examples. (O.T.)
Error propagation of partial least squares for parameters optimization in NIR modeling
Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng
2018-03-01
A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.
Fault detection of feed water treatment process using PCA-WD with parameter optimization.
Zhang, Shirong; Tang, Qian; Lin, Yu; Tang, Yuling
2017-05-01
Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T 2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T 2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
"Body-In-The-Loop": Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost.
Felt, Wyatt; Selinger, Jessica C; Donelan, J Maxwell; Remy, C David
2015-01-01
This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body "in-the-loop") and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (Steady-State Cost Mapping, Instantaneous Cost Mapping, and Instantaneous Cost Gradient Search) with eight healthy human subjects. Steady-State Cost Mapping is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, Instantaneous Cost Mapping identifies a cubic polynomial more quickly. Instantaneous Cost Gradient Search uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects' preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using Instantaneous Cost Mapping, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous
Optimization of Allelic Combinations Controlling Parameters of a Peach Quality Model.
Quilot-Turion, Bénédicte; Génard, Michel; Valsesia, Pierre; Memmah, Mohamed-Mahmoud
2016-01-01
Process-based models are effective tools to predict the phenotype of an individual in different growing conditions. Combined with a quantitative trait locus (QTL) mapping approach, it is then possible to predict the behavior of individuals with any combinations of alleles. However the number of simulations to explore the realm of possibilities may become infinite. Therefore, the use of an efficient optimization algorithm to intelligently explore the search space becomes imperative. The optimization algorithm has to solve a multi-objective problem, since the phenotypes of interest are usually a complex of traits, to identify the individuals with best tradeoffs between those traits. In this study we proposed to unroll such a combined approach in the case of peach fruit quality described through three targeted traits, using a process-based model with seven parameters controlled by QTL. We compared a current approach based on the optimization of the values of the parameters with a more evolved way to proceed which consists in the direct optimization of the alleles controlling the parameters. The optimization algorithm has been adapted to deal with both continuous and combinatorial problems. We compared the spaces of parameters obtained with different tactics and the phenotype of the individuals resulting from random simulations and optimization in these spaces. The use of a genetic model enabled the restriction of the dimension of the parameter space toward more feasible combinations of parameter values, reproducing relationships between parameters as observed in a real progeny. The results of this study demonstrated the potential of such an approach to refine the solutions toward more realistic ideotypes. Perspectives of improvement are discussed.
Grippa, Tais; Georganos, Stefanos; Lennert, Moritz; Vanhuysse, Sabine; Wolff, Eléonore
2017-10-01
Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.
Papagiannis, P.; Azariadis, P.; Papanikos, P.
2017-10-01
Footwear is subject to bending and torsion deformations that affect comfort perception. Following review of Finite Element Analysis studies of sole rigidity and comfort, a three-dimensional, linear multi-material finite element sole model for quasi-static bending and torsion simulation, overcoming boundary and optimisation limitations, is described. Common footwear materials properties and boundary conditions from gait biomechanics are used. The use of normalised strain energy for product benchmarking is demonstrated along with comfort level determination through strain energy density stratification. Sensitivity of strain energy against material thickness is greater for bending than for torsion, with results of both deformations showing positive correlation. Optimization for a targeted performance level and given layer thickness is demonstrated with bending simulations sufficing for overall comfort assessment. An algorithm for comfort optimization w.r.t. bending is presented, based on a discrete approach with thickness values set in line with practical manufacturing accuracy. This work illustrates the potential of the developed finite element analysis applications to offer viable and proven aids to modern footwear sole design assessment and optimization.
Economic optimization in new distribution system construction
International Nuclear Information System (INIS)
Freese, J.
1994-01-01
The substantial capital investment and the long-term nature of extension projects make it necessary, in particular for local utilities, to intensively prepare their construction projects. Resulting from this context, the PC-program MAFIOSY for calculating and optimizing the economics of pipeline extension projects has been developed to facilitate the decision-making process and to ensure an optimum decision. The optimum structure of a distribution network to be designed for a new service area is defined using the four-phase method set out below: Situation Audit; Determination of Potential; Determination of Economic and Technical Parameters; Optimization. (orig.)
Optimal parameters for the Green-Ampt infiltration model under rainfall conditions
Directory of Open Access Journals (Sweden)
Chen Li
2015-06-01
Full Text Available The Green-Ampt (GA model is widely used in hydrologic studies as a simple, physically-based method to estimate infiltration processes. The accuracy of the model for applications under rainfall conditions (as opposed to initially ponded situations has not been studied extensively. We compared calculated rainfall infiltration results for various soils obtained using existing GA parameterizations with those obtained by solving the Richards equation for variably saturated flow. Results provided an overview of GA model performance evaluated by means of a root-meansquare- error-based objective function across a large region in GA parameter space as compared to the Richards equation, which showed a need for seeking optimal GA parameters. Subsequent analysis enabled the identification of optimal GA parameters that provided a close fit with the Richards equation. The optimal parameters were found to substantially outperform the standard theoretical parameters, thus improving the utility and accuracy of the GA model for infiltration simulations under rainfall conditions. A sensitivity analyses indicated that the optimal parameters may change for some rainfall scenarios, but are relatively stable for high-intensity rainfall events.
International Nuclear Information System (INIS)
El-Berry, A.; El-Berry, A.; Al-Bossly, A.
2010-01-01
In machining operation, the quality of surface finish is an important requirement for many work pieces. Thus, that is very important to optimize cutting parameters for controlling the required manufacturing quality. Surface roughness parameter (Ra) in mechanical parts depends on turning parameters during the turning process. In the development of predictive models, cutting parameters of feed, cutting speed, depth of cut, are considered as model variables. For this purpose, this study focuses on comparing various machining experiments which using CNC vertical machining center, work pieces was aluminum 6061. Multiple regression models are used to predict the surface roughness at different experiments.
GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling
International Nuclear Information System (INIS)
Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas
2015-01-01
Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and
International Nuclear Information System (INIS)
Rao, R. Venkata; Rai, Dhiraj P.
2017-01-01
Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO).
Directory of Open Access Journals (Sweden)
Mohammd Mohammed S.
2015-01-01
Full Text Available The aim of this work is to develop a method for optimization of operating parameters of a triple pressure heat recovery steam generator. Two types of optimization: (a thermodynamic and (b thermoeconomic were preformed. The purpose of the thermodynamic optimization is to maximize the efficiency of the plant. The selected objective for this purpose is minimization of the exergy destruction in the heat recovery steam generator (HRSG. The purpose of the thermoeconomic optimization is to decrease the production cost of electricity. Here, the total annual cost of HRSG, defined as a sum of annual values of the capital costs and the cost of the exergy destruction, is selected as the objective function. The optimal values of the most influencing variables are obtained by minimizing the objective function while satisfying a group of constraints. The optimization algorithm is developed and tested on a case of CCGT plant with complex configuration. Six operating parameters were subject of optimization: pressures and pinch point temperatures of every three (high, intermediate and low pressure steam stream in the HRSG. The influence of these variables on the objective function and production cost are investigated in detail. The differences between results of thermodynamic and the thermoeconomic optimization are discussed.
Energy Technology Data Exchange (ETDEWEB)
Rao, R. Venkata; Rai, Dhiraj P. [Sardar Vallabhbhai National Institute of Technology, Gujarat (India)
2017-05-15
Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO).
INTERVALS OPTIMIZATION OF SYSTEMS INFORMATION SECURITY INSPECTION
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V. A. Bogatyrev
2014-09-01
Full Text Available A Markov model is suggested for secure information systems, functioning under conditions of destructive impacts, which aftereffects are found by on-line and test control. It is assumed that on-line control, in contrast to the test one, is char- acterized by the limited control completeness, but does not require the stopping of computational process. The aim of re- search is to create models that optimize intervals of test control initialization by the criterion of probability maximization for system stay in the ready state to secure fulfillment of the functional requests and minimization of the dangerous system states in view of the uncertainty and intensity variance of the destructive impacts. Variants of testing intervals optimization are con- sidered depending on the intensity of destructive impacts by the criterion of the maximum system availability for the safe execution of queries. Optimization is carried out with and without adaptation to the actual intensity change of destructive impacts. The efficiency of adaptive change for testing periods is shown depending on the observed activity of destructive impacts. The solution of optimization problem is obtained by built-in tools of computer mathematics Mathcad 15, including symbolic mathematics for solution of systems of algebraic equations. The proposed models and methods of determining the optimal testing intervals can find their application in the system design of computer systems and networks of critical applications, working under conditions of destabilizing actions with the increased requirements for their safety.
Directory of Open Access Journals (Sweden)
Oladipupo Olaosebikan Ogunleye
2015-02-01
Full Text Available Preparation of Polypropylene ternary nanocomposites (PPTN was accomplished by blending multiwall carbon nanotube (MWCNT in polypropylene/clay binary system using a melt intercalation method. The effects of MWCNT loadings (A, melting temperature (B and mixing speed (C were investigated and optimized using central composite design. The analysis of the fitted cubic model clearly indicated that A and B were the main factors influencing the tensile properties at a fixed value of C. However, the analysis of variance showed that the interactions between the process parameters, such as; AB, AC, AB2, A2B and ABC, were highly significant on both tensile strength and Young’s modulus enhancement, while no interaction is significant in all models considered for elongation. The established optimal conditions gave 0.17%, 165 °C, and 120 rpm for A, B and C, respectively. These conditions yielded a percentage increase of 57 and 63% for tensile strength and Young’s modulus respectively compared to the virgin Polypropylene used.
Optimal control applications in electric power systems
Christensen, G S; Soliman, S A
1987-01-01
Significant advances in the field of optimal control have been made over the past few decades. These advances have been well documented in numerous fine publications, and have motivated a number of innovations in electric power system engineering, but they have not yet been collected in book form. Our purpose in writing this book is to provide a description of some of the applications of optimal control techniques to practical power system problems. The book is designed for advanced undergraduate courses in electric power systems, as well as graduate courses in electrical engineering, applied mathematics, and industrial engineering. It is also intended as a self-study aid for practicing personnel involved in the planning and operation of electric power systems for utilities, manufacturers, and consulting and government regulatory agencies. The book consists of seven chapters. It begins with an introductory chapter that briefly reviews the history of optimal control and its power system applications and also p...
Force optimized recoil control system
Townsend, P. E.; Radkiewicz, R. J.; Gartner, R. F.
1982-05-01
Reduction of the recoil force of high rate of fire automatic guns was proven effective. This system will allow consideration of more powerful guns for use in both helicopter and armored personnel carrier applications. By substituting the large shock loads of firing guns with a nearly constant force, both vibration and fatigue problems that prevent mounting of powerful automatic guns is eliminated.
Simulation Based Optimization for World Line Card Production System
Directory of Open Access Journals (Sweden)
Sinan APAK
2012-07-01
Full Text Available Simulation based decision support system is one of the commonly used tool to examine complex production systems. The simulation approach provides process modules which can be adjusted with certain parameters by using data relatively easily obtainable in production process. World Line Card production system simulation is developed to evaluate the optimality of existing production line via using discrete event simulation model with variaty of alternative proposals. The current production system is analysed by a simulation model emphasizing the bottlenecks and the poorly utilized production line. Our analysis identified some improvements and efficient solutions for the existing system.
Thermo-mechanical simulation and parameters optimization for beam blank continuous casting
International Nuclear Information System (INIS)
Chen, W.; Zhang, Y.Z.; Zhang, C.J.; Zhu, L.G.; Lu, W.G.; Wang, B.X.; Ma, J.H.
2009-01-01
The objective of this work is to optimize the process parameters of beam blank continuous casting in order to ensure high quality and productivity. A transient thermo-mechanical finite element model is developed to compute the temperature and stress profile in beam blank continuous casting. By comparing the calculated data with the metallurgical constraints, the key factors causing defects of beam blank can be found out. Then based on the subproblem approximation method, an optimization program is developed to search out the optimum cooling parameters. Those optimum parameters can make it possible to run the caster at its maximum productivity, minimum cost and to reduce the defects. Now, online verifying of this optimization project has been put in practice, which can prove that it is very useful to control the actual production
Gambino, James; Tarver, Craig; Springer, H. Keo; White, Bradley; Fried, Laurence
2017-06-01
We present a novel method for optimizing parameters of the Ignition and Growth reactive flow (I&G) model for high explosives. The I&G model can yield accurate predictions of experimental observations. However, calibrating the model is a time-consuming task especially with multiple experiments. In this study, we couple the differential evolution global optimization algorithm to simulations of shock initiation experiments in the multi-physics code ALE3D. We develop parameter sets for HMX based explosives LX-07 and LX-10. The optimization finds the I&G model parameters that globally minimize the difference between calculated and experimental shock time of arrival at embedded pressure gauges. This work was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344. LLNS, LLC LLNL-ABS- 724898.
Optimization of TRPO process parameters for americium extraction from high level waste
International Nuclear Information System (INIS)
Chen Jing; Wang Jianchen; Song Chongli
2001-01-01
The numerical calculations for Am multistage fractional extraction by trialkyl phosphine oxide (TRPO) were verified by a hot test. 1750L/t-U high level waste (HLW) was used as the feed to the TRPO process. The analysis used the simple objective function to minimize the total waste content in the TRPO process streams. Some process parameters were optimized after other parameters were selected. The optimal process parameters for Am extraction by TRPO are: 10 stages for extraction and 2 stages for scrubbing; a flow rate ratio of 0.931 for extraction and 4.42 for scrubbing; nitric acid concentration of 1.35 mol/L for the feed and 0.5 mol/L for the scrubbing solution. Finally, the nitric acid and Am concentration profiles in the optimal TRPO extraction process are given
Receiver System Analysis and Optimization
2013-01-01
for several devices from the IBM SiGe 8HP process design kit (the manufacturing process used for the MDREX project): bipolar transistor ( BJT ), spiral...of the project. Most significantly, a transistor -level simulation algorithm compatible with the system level simulation algorithm was developed. This... transistor -level simulation program simultaneously and synchronizing them at time intervals. Since the new capability allows the simulation of the entire
International Nuclear Information System (INIS)
Zarepisheh, Masoud; Uribe-Sanchez, Andres F.; Li, Nan; Jia, Xun; Jiang, Steve B.
2014-01-01
Purpose: To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. Methods: In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. Results: The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly
Zarepisheh, Masoud; Uribe-Sanchez, Andres F; Li, Nan; Jia, Xun; Jiang, Steve B
2014-04-01
To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly allowing for the
Holm, R; Jensen, I H M; Sonnergaard, J
2006-10-01
D-optimal design and the desirability function were applied to optimize a self-microemulsifying drug delivery system (SMEDDS). The optimized key parameters were the following: 1) particle size of the dispersed emulsion, 2) solubility of the drug in the vehicle, and 3) the vehicle compatibility with the hard gelatin capsule. Three formulation variables, PEG200, a surfactant mixture, and an oil mixture, were included in the experimental design. The results of the mathematical analysis of the data demonstrated significant interactions among the formulation variables, and the desirability function was demonstrated to be a powerful tool to predict the optimal formulation for the explored system.
Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization.
Energy Technology Data Exchange (ETDEWEB)
Buchheit, Thomas E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wilcox, Ian Zachary [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandoval, Andrew J [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Reza, Shahed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-12-01
This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction and portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.
Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei
2018-03-01
Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF
Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm
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Yourim Yoon
2015-01-01
Full Text Available This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient technique. From this memetic approach, we could further improve the accuracy of the estimated solar cell parameters when compared with single algorithm approaches.
Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.
2017-09-01
In injection moulding process, it is important to keep the productivity increase constantly with least of waste produced such as warpage defect. Thus, this study is concerning on minimizing warpage defect on wheel caster part. Apart from eliminating product wastes, this project also giving out best optimization techniques using response surface methodology. This research studied on five parameters A-packing pressure, B-packing time, C-mold temperature, D-melting temperature and E-cooling time. The optimization showed that packing pressure is the most significant parameter. Warpage have been improved 42.64% from 0.6524 mm to 0.3742mm.
Distributed-Computer System Optimizes SRB Joints
Rogers, James L., Jr.; Young, Katherine C.; Barthelemy, Jean-Francois M.
1991-01-01
Initial calculations of redesign of joint on solid rocket booster (SRB) that failed during Space Shuttle tragedy showed redesign increased weight. Optimization techniques applied to determine whether weight could be reduced while keeping joint closed and limiting stresses. Analysis system developed by use of existing software coupling structural analysis with optimization computations. Software designed executable on network of computer workstations. Took advantage of parallelism offered by finite-difference technique of computing gradients to enable several workstations to contribute simultaneously to solution of problem. Key features, effective use of redundancies in hardware and flexible software, enabling optimization to proceed with minimal delay and decreased overall time to completion.
Optimization of photovoltaic power systems
Rekioua, Djamila
2012-01-01
Photovoltaic generation is one of the cleanest forms of energy conversion available. One of the advantages offered by solar energy is its potential to provide sustainable electricity in areas not served by the conventional power grid. Optimisation of Photovoltaic Power Systems details explicit modelling, control and optimisation of the most popular stand-alone applications such as pumping, power supply, and desalination. Each section is concluded by an example using the MATLAB(R) and Simulink(R) packages to help the reader understand and evaluate the performance of different photovoltaic syste
Directory of Open Access Journals (Sweden)
Abdelhafid HASNI
2009-03-01
Full Text Available Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO algorithm which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results.
Multi-agent for manufacturing systems optimization
Ciortea, E. M.; Tulbure, A.; Huţanu, C.-tin
2016-08-01
The paper is meant to be a dynamic approach to optimize manufacturing systems based on multi-agent systems. Multi-agent systems are semiautonomous decision makers and cooperate to optimize the manufacturing process. Increasing production the capacity is achieved by developing, implementing efficient and effective systems from control based on current manufacturing process. The model multi-agent proposed in this paper is based on communication between agents who, based on their mechanisms drive to autonomous decision making. Methods based on multi-agent programming are applied between flexible manufacturing processes and cooperation with agents. Based on multi-agent technology and architecture of intelligent manufacturing can lead to development of strategies for control and optimization of scheduled production resulting from the simulation.
Collaborative Systems Driven Aircraft Configuration Design Optimization
Shiva Prakasha, Prajwal; Ciampa, Pier Davide; Nagel, Björn
2016-01-01
A Collaborative, Inside-Out Aircraft Design approach is presented in this paper. An approach using physics based analysis to evaluate the correlations between the airframe design, as well as sub-systems integration from the early design process, and to exploit the synergies within a simultaneous optimization process. Further, the disciplinary analysis modules involved in the optimization task are located in different organization. Hence, the Airframe and Subsystem design tools are integrated ...
Adaptive stimulus optimization for sensory systems neuroscience
DiMattina, Christopher; Zhang, Kechen
2013-01-01
In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system...
A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
Directory of Open Access Journals (Sweden)
Jilin Zhang
2017-09-01
Full Text Available In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT. Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP, which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS. This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.
A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.
Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei
2017-09-21
In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.
A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei
2017-01-01
In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163
Directory of Open Access Journals (Sweden)
Wei Gao
2016-01-01
Full Text Available According to the regularization method in the inverse problem of load identification, a new method for determining the optimal regularization parameter is proposed. Firstly, quotient function (QF is defined by utilizing the regularization parameter as a variable based on the least squares solution of the minimization problem. Secondly, the quotient function method (QFM is proposed to select the optimal regularization parameter based on the quadratic programming theory. For employing the QFM, the characteristics of the values of QF with respect to the different regularization parameters are taken into consideration. Finally, numerical and experimental examples are utilized to validate the performance of the QFM. Furthermore, the Generalized Cross-Validation (GCV method and the L-curve method are taken as the comparison methods. The results indicate that the proposed QFM is adaptive to different measuring points, noise levels, and types of dynamic load.
Zarepisheh, M; Uribe-Sanchez, A; Li, N; Jia, X; Jiang, S
2012-06-01
To establish a new mathematical framework for IMRT treatment optimization with voxel-dependent optimization parameters. In IMRT inverse treatment planning, a physician seeks for a plan to deliver a prescribed dose to the target while sparing the nearby healthy tissues. The conflict between these objectives makes the multi-criteria optimization an appropriate tool. Traditionally, a clinically acceptable plan can be generated by fine-tuning organ-based parameters. We establish a new mathematical framework by using voxel-based parameters for optimization. We introduce three different Pareto surfaces, prove the relationship between those surfaces, and compare voxel-based and organ-based methods. We prove some new theorems providing conditions under which the Pareto optimality is guaranteed. The new mathematical framework has shown that: 1) Using an increasing voxel penalty function with an increasing derivative, in particular the popular power function, it is possible to explore the entire Pareto surface by changing voxel-based weighting factors, which increases the chances of getting more desirable plan. 2) The Pareto optimality is always guaranteed by adjusting voxel-based weighting factors. 3) If the plan is initially produced by adjusting organ-based weighting factors, it is impossible to improve all the DVH curves at the same time by adjusting voxel-based weighting factors. 4) A larger Pareto surface is explored by changing voxel-based weighting factors than by changing organ-based weighting factors, possibly leading to a plan with better trade-offs. 5) The Pareto optimality is not necessarily guaranteed while we are adjusting the voxel reference doses, and hence, adjusting voxel-based weighting factors is preferred in terms of preserving the Pareto optimality. We have developed a mathematical framework for IMRT optimization using voxel-based parameters. We can improve the plan quality by adjusting voxel-based weighting factors after organ-based parameter
Parameter estimation of a pulp digester model with derivative-free optimization strategies
Seiça, João C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C. P.
2017-07-01
The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.
Wang, Xinghu; Hong, Yiguang; Yi, Peng; Ji, Haibo; Kang, Yu
2017-05-24
In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.
Writing systems: not optimal, but good enough.
Seidenberg, Mark S
2012-10-01
Languages and writing systems result from satisfying multiple constraints related to learning, comprehension, production, and their biological bases. Orthographies are not optimal because these constraints often conflict, with further deviations due to accidents of history and geography. Things tend to even out because writing systems and the languages they represent exhibit systematic trade-offs between orthographic depth and morphological complexity.
OPTIMIZATION OF COMBINED SEWER OVERFLOW CONTROL SYSTEMS
The highly variable and intermittent pollutant concentrations and flowrates associated with wet-weather events in combined sewersheds necessitates the use of storage-treatment systems to control pollution.An optimized combined-sewer-overflow (CSO) control system requires a manage...
Parameters Optimization for a Kind of Dynamic Vibration Absorber with Negative Stiffness
Directory of Open Access Journals (Sweden)
Yongjun Shen
2016-01-01
Full Text Available A new type of dynamic vibration absorber (DVA with negative stiffness is studied in detail. At first, the analytical solution of the system is obtained based on the established differential motion equation. Three fixed points are found in the amplitude-frequency curves of the primary system. The design formulae for the optimum tuning ratio and optimum stiffness ratio of DVA are obtained by adjusting the three fixed points to the same height according to the fixed-point theory. Then, the optimum damping ratio is formulated by minimizing the maximum value of the amplitude-frequency curves according to H∞ optimization principle. According to the characteristics of negative stiffness element, the optimum negative stiffness ratio is also established and it could still keep the system stable. In the end, the comparison between the analytical and the numerical solutions verifies the correctness of the analytical solution. The comparisons with three other traditional DVAs under the harmonic and random excitations show that the presented DVA performs better in vibration absorption. This result could provide theoretical basis for optimum parameters design of similar DVAs.
Optimization of the protective energy removal parameters for tokamak HT7-U superconducting magnets
Energy Technology Data Exchange (ETDEWEB)
Khvostenko, P.P.; Chudnovsky, A.N.; Posadsky, I.A. [RRC ' Kurchatov Inst.' , Nuclear Fusion Inst., Moscow (Russian Federation); Bi, Y.F.; Cheng, S.M.; He, Y.X. [Academia Sinica, Hefei, Anhui (China). Inst. of Plasma Physics
1998-07-01
The design of the HT-7U superconducting tokamak is in progress now. The design incorporates superconducting magnets of the toroidal field and poloidal field systems. Toroidal field system consists of 16 D-shape coils and poloidal field system consists of 12 coils. All coils will be use NbTi/Cu cable-in-conduit conductor cooled with forced-flow supercritical helium at 4.5 K, 4 Bar. Quench in the superconducting magnets is accompanied byconversion of the stored magnetic field energy into a thermal one which is spent on heating of both the coil part which made transition into a normal state and dump resistors. A non-uniform heating of the coil part results in the emergence of thermomechanical stresses which can cause its destruction. The protective removal of a current is realized to prevent the coil destruction at the emergence of the quench. In that case, the faster the current removal occurs, the less the coil heating is. On the other hand, the current removal rate should not be too high in order to avoid an electric breakdown by the excited inductive voltage. Optimization of the protective energy removal parameters both for TF and PF superconducting magnets is presented. (author)
Optimization of the protective energy removal parameters for tokamak HT7-U superconducting magnets
International Nuclear Information System (INIS)
Khvostenko, P.P.; Chudnovsky, A.N.; Posadsky, I.A.; Bi, Y.F.; Cheng, S.M.; He, Y.X.
1998-01-01
The design of the HT-7U superconducting tokamak is in progress now. The design incorporates superconducting magnets of the toroidal field and poloidal field systems. Toroidal field system consists of 16 D-shape coils and poloidal field system consists of 12 coils. All coils will be use NbTi/Cu cable-in-conduit conductor cooled with forced-flow supercritical helium at 4.5 K, 4 Bar. Quench in the superconducting magnets is accompanied by conversion of the stored magnetic field energy into a thermal one which is spent on heating of both the coil part which made transition into a normal state and dump resistors. A non-uniform heating of the coil part results in the emergence of thermomechanical stresses which can cause its destruction. The protective removal of a current is realized to prevent the coil destruction at the emergence of the quench. In that case, the faster the current removal occurs, the less the coil heating is. On the other hand, the current removal rate should not be too high in order to avoid an electric breakdown by the excited inductive voltage. Optimization of the protective energy removal parameters both for TF and PF superconducting magnets is presented. (author)
Panorama parking assistant system with improved particle swarm optimization method
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.
REopt: A Platform for Energy System Integration and Optimization
Energy Technology Data Exchange (ETDEWEB)
Anderson, Katherine H. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cutler, Dylan S. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Olis, Daniel R. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Elgqvist, Emma M. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Li, Xiangkun [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Laws, Nicholas D. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); DiOrio, Nicholas A. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Walker, H. A [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-09-22
REopt is a techno-economic decision support model used to optimize energy systems for buildings, campuses, communities, and microgrids. The primary application of the model is for optimizing the integration and operation of behind-the-meter energy assets. This report provides an overview of the model, including its capabilities and typical applications; inputs and outputs; economic calculations; technology descriptions; and model parameters, variables, and equations. The model is highly flexible, and is continually evolving to meet the needs of each analysis. Therefore, this report is not an exhaustive description of all capabilities, but rather a summary of the core components of the model.
Genetic optimization of steam multi-turbines system
International Nuclear Information System (INIS)
Olszewski, Pawel
2014-01-01
Optimization analysis of partially loaded cogeneration, multiple-stages steam turbines system was numerically investigated by using own-developed code (C++). The system can be controlled by following variables: fresh steam temperature, pressure, and flow rates through all stages in steam turbines. Five various strategies, four thermodynamics and one economical, which quantify system operation, were defined and discussed as an optimization functions. Mathematical model of steam turbines calculates steam properties according to the formulation proposed by the International Association for the Properties of Water and Steam. Genetic algorithm GENOCOP was implemented as a solving engine for non–linear problem with handling constrains. Using formulated methodology, example solution for partially loaded system, composed of five steam turbines (30 input variables) with different characteristics, was obtained for five strategies. The genetic algorithm found multiple solutions (various input parameters sets) giving similar overall results. In real application it allows for appropriate scheduling of machine operation that would affect equable time load of every system compounds. Also based on these results three strategies where chosen as the most complex: the first thermodynamic law energy and exergy efficiency maximization and total equivalent energy minimization. These strategies can be successfully used in optimization of real cogeneration applications. - Highlights: • Genetic optimization model for a set of five various steam turbines was presented. • Four various thermodynamic optimization strategies were proposed and discussed. • Operational parameters (steam pressure, temperature, flow) influence was examined. • Genetic algorithm generated optimal solutions giving the best estimators values. • It has been found that similar energy effect can be obtained for various inputs