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.
Source term identification in atmospheric modelling via sparse optimization
Adam, Lukas; Branda, Martin; Hamburger, Thomas
2015-04-01
Inverse modelling plays an important role in identifying the amount of harmful substances released into atmosphere during major incidents such as power plant accidents or volcano eruptions. Another possible application of inverse modelling lies in the monitoring the CO2 emission limits where only observations at certain places are available and the task is to estimate the total releases at given locations. This gives rise to minimizing the discrepancy between the observations and the model predictions. There are two standard ways of solving such problems. In the first one, this discrepancy is regularized by adding additional terms. Such terms may include Tikhonov regularization, distance from a priori information or a smoothing term. The resulting, usually quadratic, problem is then solved via standard optimization solvers. The second approach assumes that the error term has a (normal) distribution and makes use of Bayesian modelling to identify the source term. Instead of following the above-mentioned approaches, we utilize techniques from the field of compressive sensing. Such techniques look for a sparsest solution (solution with the smallest number of nonzeros) of a linear system, where a maximal allowed error term may be added to this system. Even though this field is a developed one with many possible solution techniques, most of them do not consider even the simplest constraints which are naturally present in atmospheric modelling. One of such examples is the nonnegativity of release amounts. We believe that the concept of a sparse solution is natural in both problems of identification of the source location and of the time process of the source release. In the first case, it is usually assumed that there are only few release points and the task is to find them. In the second case, the time window is usually much longer than the duration of the actual release. In both cases, the optimal solution should contain a large amount of zeros, giving rise to the
Optimal experiment design for identification of grey-box models
DEFF Research Database (Denmark)
Sadegh, Payman; Melgaard, Henrik; Madsen, Henrik
1994-01-01
Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure...... estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest....... of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established. A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP...
Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization
International Nuclear Information System (INIS)
Yong, Li; Ying-Gan, Tang
2010-01-01
A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method
Ayvaz, M Tamer
2010-09-20
This study proposes a linked simulation-optimization model for solving the unknown groundwater pollution source identification problems. In the proposed model, MODFLOW and MT3DMS packages are used to simulate the flow and transport processes in the groundwater system. These models are then integrated with an optimization model which is based on the heuristic harmony search (HS) algorithm. In the proposed simulation-optimization model, the locations and release histories of the pollution sources are treated as the explicit decision variables and determined through the optimization model. Also, an implicit solution procedure is proposed to determine the optimum number of pollution sources which is an advantage of this model. The performance of the proposed model is evaluated on two hypothetical examples for simple and complex aquifer geometries, measurement error conditions, and different HS solution parameter sets. Identified results indicated that the proposed simulation-optimization model is an effective way and may be used to solve the inverse pollution source identification problems. Copyright (c) 2010 Elsevier B.V. All rights reserved.
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.
Optimization of inverse model identification for multi-axial test rig control
Directory of Open Access Journals (Sweden)
Müller Tino
2016-01-01
Full Text Available Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.
Optimization of an individual re-identification modeling process using biometric features
Energy Technology Data Exchange (ETDEWEB)
Heredia-Langner, Alejandro; Amidan, Brett G.; Matzner, Shari; Jarman, Kristin H.
2014-09-24
We present results from the optimization of a re-identification process using two sets of biometric data obtained from the Civilian American and European Surface Anthropometry Resource Project (CAESAR) database. The datasets contain real measurements of features for 2378 individuals in a standing (43 features) and seated (16 features) position. A genetic algorithm (GA) was used to search a large combinatorial space where different features are available between the probe (seated) and gallery (standing) datasets. Results show that optimized model predictions obtained using less than half of the 43 gallery features and data from roughly 16% of the individuals available produce better re-identification rates than two other approaches that use all the information available.
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.
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
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.
Mansor, Zakwan; Zakaria, Mohd Zakimi; Nor, Azuwir Mohd; Saad, Mohd Sazli; Ahmad, Robiah; Jamaluddin, Hishamuddin
2017-09-01
This paper presents the black-box modelling of palm oil biodiesel engine (POB) using multi-objective optimization differential evolution (MOODE) algorithm. Two objective functions are considered in the algorithm for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. The mathematical model used in this study to represent the POB system is nonlinear auto-regressive moving average with exogenous input (NARMAX) model. Finally, model validity tests are applied in order to validate the possible models that was obtained from MOODE algorithm and lead to select an optimal model.
DEFF Research Database (Denmark)
Ursem, Rasmus Kjær
of handling problems with non-linear constraints, multiple objectives, and dynamic components – properties that frequently appear in real-world problems. This thesis presents research in three fundamental areas of EC; fitness function design, methods for parameter control, and techniques for multimodal...... optimization. In addition to general investigations in these areas, I introduce a number of algorithms and demonstrate their potential on real-world problems in system identification and control. Furthermore, I investigate dynamic optimization problems in the context of the three fundamental areas as well...... as control, which is a field where real-world dynamic problems appear. Regarding fitness function design, smoothness of the fitness landscape is of primary concern, because a too rugged landscape may disrupt the search and lead to premature convergence at local optima. Rugged fitness landscapes typically...
Motion model identification of rescue robot based on optimized Jordan neural network
Zhang, Guangbin; Zhang, Runmei; Wang, Guangyin; Wu, Yulu
2017-06-01
Considering the influence of various factors, such as speed, angle, depth of water, weight, and water flow, on the underwater rescue robot, a method based on neural network is proposed. According to the characteristics of Elman and Jordan neural network, a new dynamic neural network is constructed. The network can be used to remember the state of the hidden layer and increase the feedback of the output node. The improved Jordan network is optimized by chaos particle swarm optimization algorithm. The optimized neural network is applied to identify the dynamic model of the underwater rescue robot. The simulation results show that the neural network has good convergence speed and accuracy.
Chen, C.; Long, H. L.; Wan, J.; Jia, JL; Li, X.; Chu, CJ
2016-08-01
An economic-energy-industrial-environmental optimization (EEIEO) model is proposed for identification of optimal economic, industry, energy and environment strategies. The EEIEO model is applied to a real case of Beijing-Tianjin-Hebei (BTH) region, which is the important economic growth pole of northern China. The EEIEO model could fully consider the interaction between industrial, energy, urbanization and environment sector, and generate the optimized economic development, industrial restructuring, energy consumption and environment management schemes. This is first attempt to introduce economic, energy, industrial, urbanization and environmental sectors into an optimization framework, while sustainable energy and environment development pathways are explored through EEIEO model. The results suggest that: (i) the GDP of BTH region would increase about 73.80% over the planning horizon; (ii) the contribution of tertiary industry for BTH region's economic development would gradually increase from 54.00% in 2015 to 65.00% in 2030; (iii) the consumption of coal would decrease by 36%, and the natural gas would obviously increase by 97.70% over the planning horizon; and (iv) the SO2, smoke and dust emissions and CO2 would reduce by 30.20%, 35.30% and 4.50% from 2015 to 2030, respectively.
Identification of physical models
DEFF Research Database (Denmark)
Melgaard, Henrik
1994-01-01
The problem of identification of physical models is considered within the frame of stochastic differential equations. Methods for estimation of parameters of these continuous time models based on descrete time measurements are discussed. The important algorithms of a computer program for ML or MAP...... design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...... of the model with the available prior knowledge. The methods for identification of physical models have been applied in two different case studies. One case is the identification of thermal dynamics of building components. The work is related to a CEC research project called PASSYS (Passive Solar Components...
Directory of Open Access Journals (Sweden)
S. N. Naikwad
2009-01-01
Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.
Jiming, K.; Peifeng, H.; Yun, C.
2012-12-01
Potential landslide judgment is one of the key issues for the landslide forecast. Firstly, this work takes advantage of earth multiple time-space and multi-sensor networks to obtain the slope critical information such as lithology, slope structure, topography, activities signs and so on. Secondly, select different control judgment indicators, and establish the multi-factor judgment model of the potential landslide based on the multi-source information. Thirdly, especially according to the landslide disaster law, analyze the changes of the topography, the changes of the disaster conditions and the induced conditions of landslide occurred in the gestation process of potential landslide. Based on the above conclusions, build a landslide judgment model based on the controlling factors from different sources of information. Finally, study the typical case on the landslide in Wenchuan earthquake of China, and optimize the potential landslide judgment model. This research results can provide a good reference to landslide prediction and prevention.
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.
Optimization modeling with spreadsheets
Baker, Kenneth R
2015-01-01
An accessible introduction to optimization analysis using spreadsheets Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software. The Third Edition includes many practical applications of optimization models as well as a systematic framework that il
Identification and optimization problems in plasma physics
International Nuclear Information System (INIS)
Gilbert, J.C.
1986-06-01
Parameter identification of the current in a tokamak plasma is studied. Plasma equilibrium in a vacuum container with a diaphragm is analyzed. A variable metric method with reduced optimization with nonlinear equality constraints; and a quasi-Newton reduced optimization method with constraints giving priority to restoration are presented [fr
Optimized Experiment Design for Marine Systems Identification
DEFF Research Database (Denmark)
Blanke, M.; Knudsen, Morten
1999-01-01
Simulation of maneuvring and design of motion controls for marine systems require non-linear mathematical models, which often have more than one-hundred parameters. Model identification is hence an extremely difficult task. This paper discusses experiment design for marine systems identification...... and proposes a sensitivity approach to solve the practical experiment design problem. The applicability of the sensitivity approach is demonstrated on a large non-linear model of surge, sway, roll and yaw of a ship. The use of the method is illustrated for a container-ship where both model and full-scale tests...
Cost Optimal System Identification Experiment Design
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
A structural system identification experiment design method is formulated in the light of decision theory, structural reliability theory and optimization theory. The experiment design is based on a preposterior analysis, well-known from the classical decision theory. I.e. the decisions concerning...... the experiment design are not based on obtained experimental data. Instead the decisions are based on the expected experimental data assumed to be obtained from the measurements, estimated based on prior information and engineering judgement. The design method provides a system identification experiment design...... reflecting the cost of the experiment and the value of obtained additional information. An example concerning design of an experiment for parametric identification of a single degree of freedom structural system shows the applicability of the experiment design method....
Directory of Open Access Journals (Sweden)
Bańka Stanisław
2015-12-01
Full Text Available The paper presents algorithms for parameter identification of linear vessel models being in force for the current operating point of a ship. Advantages and disadvantages of gradient and genetic algorithms in identifying the model parameters are discussed. The study is supported by presentation of identification results for a nonlinear model of a drilling vessel.
Optimization Modeling with Spreadsheets
Baker, Kenneth R
2011-01-01
This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver. The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the sp
Directory of Open Access Journals (Sweden)
Yongkai An
2015-07-01
Full Text Available This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately.
Directory of Open Access Journals (Sweden)
Nguyen Ngoc Son
2016-12-01
Full Text Available This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.
System identification using Nuclear Norm & Tabu Search optimization
Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.
2018-01-01
In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.
Identification of optimal feedback control rules from micro-quadrotor and insect flight trajectories
Faruque, Imraan A.; Muijres, Florian T.; Macfarlane, Kenneth M.; Kehlenbeck, Andrew; Humbert, J.S.
2018-01-01
This paper presents “optimal identification,” a framework for using experimental data to identify the optimality conditions associated with the feedback control law implemented in the measurements. The technique compares closed loop trajectory measurements against a reduced order model of the open
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...
Modeling, simulation and optimization of bipedal walking
Berns, Karsten
2013-01-01
The model-based investigation of motions of anthropomorphic systems is an important interdisciplinary research topic involving specialists from many fields such as Robotics, Biomechanics, Physiology, Orthopedics, Psychology, Neurosciences, Sports, Computer Graphics and Applied Mathematics. This book presents a study of basic locomotion forms such as walking and running is of particular interest due to the high demand on dynamic coordination, actuator efficiency and balance control. Mathematical models and numerical simulation and optimization techniques are explained, in combination with experimental data, which can help to better understand the basic underlying mechanisms of these motions and to improve them. Example topics treated in this book are Modeling techniques for anthropomorphic bipedal walking systems Optimized walking motions for different objective functions Identification of objective functions from measurements Simulation and optimization approaches for humanoid robots Biologically inspired con...
Risk modelling in portfolio optimization
Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi
2013-09-01
Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.
Model Risk in Portfolio Optimization
Directory of Open Access Journals (Sweden)
David Stefanovits
2014-08-01
Full Text Available We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk.
Optimization and Non-Linear Identification of Reservoir Water Flooding Process
Directory of Open Access Journals (Sweden)
A. S. Grema
2017-10-01
Full Text Available In this study, dynamic optimization and identification of petroleum reservoir waterflooding using receding horizon (RH principles was examined. Two forms of the strategy were compared on a realistic reservoir model. Sequential quadratic programming (SQP was applied to optimize net present value (NPV using water injection rates as the variables. MRST from SINTEF was used for the reservoir modeling. The identification of the reservoir was performed using nonlinear autoregressive with exogenous input (NARX neural network from MATLAB. Data for the network training and validation was obtained by carrying out a numerical experiment on a high fidelity model of the reservoir. This model was developed with Eclipse Reservoir Simulator from Schlumberger. From the results obtained, moving-end RH gave a higher NPV than fixed-end RH with a margin of $0.5 billion. The identification algorithm was very much effective and near perfect for the studied reservoir.
On Optimal Input Design and Model Selection for Communication Channels
Energy Technology Data Exchange (ETDEWEB)
Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
identification with model reduction issues
Directory of Open Access Journals (Sweden)
A. Bilbao-Guillerna
2005-01-01
with the multiestimation scheme instead of a high-order one. Depending on the frequency spectrum characteristics of the input and on the estimates evolution, the multiestimation scheme selects on-line the most appropriate model and its related estimation scheme in order to improve the identification and control performances. Robust closed-loop stability is proved even in the presence of unmodeled dynamics of sufficiently small sizes as it has been confirmed by simulation results. The scheme chooses in real time the estimator/controller associated with a particular reduced model possessing the best performance according to an identification performance index by implementing a switching rule between estimators. The switching rule is subject to a minimum residence time at each identifier/adaptive controller parameterization for closed-loop stabilization purposes. A conceptually simple higher-level supervisor, based on heuristic updating rules which estimate on-line the weights of the switching rule between estimation schemes, is discussed.
Optimization-based topology identification of complex networks
International Nuclear Information System (INIS)
Tang Sheng-Xue; Chen Li; He Yi-Gang
2011-01-01
In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)
On the Optimal Location of Sensors for Parametric Identification of Linear Structural Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Brincker, Rune
A survey of the field of optimal location of sensors for parametric identification of linear structural systems is presented. The survey shows that few papers are devoted to the case of optimal location sensors in which the measurements are modelled by a random field with non-trivial covariance...... function. Most often it is assumed that the results of the measurements are statistically independent variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The example is concerned with optimal location of sensors for parametric...... identification of modal parameters for a vibrating beam under random loading. The covariance of the modal parameters expected to be obtained is investigated to variations of number and location of sensors. Further, the influence of the noise on the optimal location of the sensors is investigated....
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.
Pyomo optimization modeling in Python
Hart, William E; Watson, Jean-Paul; Woodruff, David L; Hackebeil, Gabriel A; Nicholson, Bethany L; Siirola, John D
2017-01-01
This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package fo...
Optimal Strategy and Business Models
DEFF Research Database (Denmark)
Johnson, Peter; Foss, Nicolai Juul
2016-01-01
, it is possible to formalize useful notions of a business model, resources, and competitive advantage. The business model that underpins strategy may be seen as a set of constraints on resources that can be interpreted as controls in optimal control theory. Strategy then might be considered to be the control...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....
Giller, C A
2011-12-01
The use of conformity indices to optimize Gamma Knife planning is common, but does not address important tradeoffs between dose to tumor and normal tissue. Pareto analysis has been used for this purpose in other applications, but not for Gamma Knife (GK) planning. The goal of this work is to use computer models to show that Pareto analysis may be feasible for GK planning to identify dosimetric tradeoffs. We define a GK plan A to be Pareto dominant to B if the prescription isodose volume of A covers more tumor but not more normal tissue than B, or if A covers less normal tissue but not less tumor than B. A plan is Pareto optimal if it is not dominated by any other plan. Two different Pareto optimal plans represent different tradeoffs between dose to tumor and normal tissue, because neither plan dominates the other. 'GK simulator' software calculated dose distributions for GK plans, and was called repetitively by a genetic algorithm to calculate Pareto dominant plans. Three irregular tumor shapes were tested in 17 trials using various combinations of shots. The mean number of Pareto dominant plans/trial was 59 ± 17 (sd). Different planning strategies were identified by large differences in shot positions, and 70 of the 153 coordinate plots (46%) showed differences of 5mm or more. The Pareto dominant plans dominated other nearby plans. Pareto dominant plans represent different dosimetric tradeoffs and can be systematically calculated using genetic algorithms. Automatic identification of non-intuitive planning strategies may be feasible with these methods.
Topology optimization of continuum structure with dynamic constraints using mode identification
Energy Technology Data Exchange (ETDEWEB)
Li, Jianhongyu; Chen, Shenyan; Huang, Hai [Beihang University, Beijing (China)
2015-04-15
For the problems such as mode exchange and localized modes in topology optimization of continuum structure with dynamic constraints, it is difficult to apply the traditional optimization model which considers fixed order mode frequencies as constraints in optimization calculation. A new optimization model is established, in which the dynamical constraints are changed as frequencies of structural principal vibrations. The order of the principal vibrations is recognized through modal identification in the optimization process, and the constraints are updated to make the optimization calculation execute smoothly. Localized mode elimination techniques are introduced to reduce the localized modes induced by the low density elements, which could improve the optimization efficiency. A new optimization process is designed, which achieves the purpose of overcoming mode exchange problem and localized mode problem at the cost of increasing several structural analyses. Optimization system is developed by using Nastran to perform structural analysis and sensitivity analysis and two-level multipoint approximation algorithm as optimizer. Numerical results verified that the presented method is effective and reasonable.
THz identification and Bayes modeling
Sokolnikov, Andre
2017-05-01
THz Identification is a developing technology. Sensing in the THz range potentially gives opportunity for short range radar sensing because THz waves can better penetrate through obscured atmosphere, such as fog, than visible light. The lower scattering of THz as opposed to the visible light results also in significantly better imaging than in IR spectrum. A much higher contrast can be achieved in medical trans-illumination applications than with X-rays or visible light. The same THz radiation qualities produce better tomographical images from hard surfaces, e.g. ceramics. This effect comes from the delay in time of reflected THz pulses detection. For special or commercial applications alike, the industrial quality control of defects is facilitated with a lower cost. The effectiveness of THz wave measurements is increased with computational methods. One of them is Bayes modeling. Examples of this kind of mathematical modeling are considered.
On the Optimal Location of Sensors for Parametric Identification of Linear Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Brincker, Rune
1994-01-01
. It is assumed most often that the results of the measurements are statistically independent random variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The covariance of the model parameters expected to be obtained is investigated......An outline of the field of optimal location of sensors for parametric identification of linear structural systems is presented. There are few papers devoted to the case of optimal location of sensors in which the measurements are modeled by a random field with non-trivial covariance function...
Faruque, Imraan A; Muijres, Florian T; Macfarlane, Kenneth M; Kehlenbeck, Andrew; Humbert, J Sean
2018-01-03
This paper presents "optimal identification," a framework for using experimental data to identify the optimality conditions associated with the feedback control law implemented in the measurements. The technique compares closed loop trajectory measurements against a reduced order model of the open loop dynamics, and uses linear matrix inequalities to solve an inverse optimal control problem as a convex optimization that estimates the controller optimality conditions. In this study, the optimal identification technique is applied to two examples, that of a millimeter-scale micro-quadrotor with an engineered controller on board, and the example of a population of freely flying Drosophila hydei maneuvering about forward flight. The micro-quadrotor results show that the performance indices used to design an optimal flight control law for a micro-quadrotor may be recovered from the closed loop simulated flight trajectories, and the Drosophila results indicate that the combined effect of the insect longitudinal flight control sensing and feedback acts principally to regulate pitch rate.
Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...
Iterative Selection of Unknown Weights in Direct Weight Optimization Identification
Directory of Open Access Journals (Sweden)
Xiao Xuan
2014-01-01
Full Text Available To the direct weight optimization identification of the nonlinear system, we add some linear terms about input sequences in the former linear affine function so as to approximate the nonlinear property. To choose the two classes of unknown weights in the more linear terms, this paper derives the detailed process on how to choose these unknown weights from theoretical analysis and engineering practice, respectively, and makes sure of their key roles between the unknown weights. From the theoretical analysis, the added unknown weights’ auxiliary role can be known in the whole process of approximating the nonlinear system. From the practical analysis, we learn how to transform one complex optimization problem to its corresponding common quadratic program problem. Then, the common quadratic program problem can be solved by the basic interior point method. Finally, the efficiency and possibility of the proposed strategies can be confirmed by the simulation results.
The Brookhaven Process Optimization Models
Energy Technology Data Exchange (ETDEWEB)
Pilati, D. A.; Sparrow, F. T.
1979-01-01
The Brookhaven National Laboratory Industry Model Program (IMP) has undertaken the development of a set of industry-specific process-optimization models. These models are to be used for energy-use projections, energy-policy analyses, and process technology assessments. Applications of the models currently under development show that system-wide energy impacts may be very different from engineering estimates, selected investment tax credits for cogeneration (or other conservation strategies) may have the perverse effect of increasing industrial energy use, and that a proper combination of energy taxes and investment tax credits is more socially desirable than either policy alone. A section is included describing possible extensions of these models to answer questions or address other systems (e.g., a single plant instead of an entire industry).
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
This paper describes the modelling, simulating and optimizing including experimental verification as being carried out as part of a Ph.D. project being written resp. supervised by the authors. The work covers dynamic performance of both water-tube boilers and fire tube boilers. A detailed dynamic...... model of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic-Equation system. Being able...... to operate a boiler plant dynamically means that the boiler designs must be able to absorb any fluctuations in water level and temperature gradients resulting from the pressure change in the boiler. On the one hand a large water-/steam space may be required, i.e. to build the boiler as big as possible. Due...
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
This paper describes the modelling, simulating and optimizing including experimental verification as being carried out as part of a Ph.D. project being written resp. supervised by the authors. The work covers dynamic performance of both water-tube boilers and fire tube boilers. A detailed dynamic...... to the internal pressure the consequence of the increased volume (i.e. water-/steam space) is an increased wall thickness in the pressure part of the boiler. The stresses introduced in the boiler pressure part as a result of the temperature gradients are proportional to the square of the wall thickness...... model of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic-Equation system. Being able...
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2004-01-01
In the present work a framework for optimizing the design of boilers for dynamic operation has been developed. A cost function to be minimized during the optimization has been formulated and for the present design variables related to the Boiler Volume and the Boiler load Gradient (i.e. ring rate...... on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... performance has been developed. Outputs from the simulations are shrinking and swelling of water level in the drum during for example a start-up of the boiler, these gures combined with the requirements with respect to allowable water level uctuations in the drum denes the requirements with respect to drum...
Overhead longwave infrared hyperspectral material identification using radiometric models
Energy Technology Data Exchange (ETDEWEB)
Zelinski, M. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2018-01-09
Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.
Modelling, Optimization and Optimal Control of Small Scale Stirred Tank Bioreactors
Directory of Open Access Journals (Sweden)
Mitko Petrov
2004-10-01
Full Text Available Models of the mass-transfer in a stirred tank bioreactor depending on general indexes of the processes of aeration and mixing in concrete simplifications of the hydrodynamic structure of the flows are developed. The offered combined model after parameters identification is used for optimization of the parameters of the apparatus construction. The optimization problem is solved by using of the fuzzy sets theory and in this way the unspecified as a result of the model simplification are read. In conclusion an optimal control of a fed-batch fermentation process of E. coli is completed by using Neuro-Dynamic programming. The received results after optimization show a considerable improvement of the mass-transfer indexes and the quantity indexes at the end of the process.
Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
Directory of Open Access Journals (Sweden)
Hanxin Chen
2013-01-01
Full Text Available A novel intelligent method based on wavelet neural network (WNN was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.
Identification of Optimal Policies in Markov Decision Processes
Czech Academy of Sciences Publication Activity Database
Sladký, Karel
46 2010, č. 3 (2010), s. 558-570 ISSN 0023-5954. [ International Conference on Mathematical Methods in Economy and Industry. České Budějovice, 15.06.2009-18.06.2009] R&D Projects: GA ČR(CZ) GA402/08/0107; GA ČR GA402/07/1113 Institutional research plan: CEZ:AV0Z10750506 Keywords : finite state Markov decision processes * discounted and average costs * elimination of suboptimal policies Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.461, year: 2010 http://library.utia.cas.cz/separaty/2010/E/sladky-identification of optimal policies in markov decision processes.pdf
Identification and Optimization of New Leads for Malaria Vector Control.
Hueter, Ottmar F; Hoppé, Mark; Wege, Philip; Maienfisch, Peter
2016-10-01
A significant proportion of the world's population remains at risk from malaria, and whilst great progress has been made in reducing the number of malaria cases globally through the use of vector control insecticides, these gains are under threat from the emergence of insecticide resistance. The spread of resistance in the vector populations, principally to pyrethroids, is driving the need for the development of new tools for malaria vector control. In order to identify new leads 30,000 compounds from the Syngenta corporate chemical collection were tested in a newly developed screening platform. More than 3000 compounds (10%) showed activity at ≤200 mg active ingredient (AI) litre -1 against Anopheles stephensi. Further evaluation resulted in the identification of 12 viable leads for the control of adult mosquitoes, most originating from current or former insecticide projects. Surprisingly, one of these leads emerged from a former PPO herbicide project and one from a former complex III fungicide project. This indicates that representatives of certain herbicide and fungicide projects and modes of action can also represent a valuable source of leads for malaria vector control. Optimization of the diphenyl ether lead 1 resulted in the identification of the cyano-pyridyl compound 31. This compound 31 exhibits good activity against mosquito species including rdl resistant Anopheles. It is only slightly weaker than permethrin and does not show relevant levels of cross-resistance to the organochlorine insecticide dieldrin.
Simulation–optimization model for groundwater contamination ...
Indian Academy of Sciences (India)
the current optimum particles (Kennedy & Eberhart 1995). Parsopoulas & Vrahatis (2002) also concluded that PSO is a very useful and simple technique for solving complex problems. If simulation model is embedded in an optimization model using advanced optimization tools like particle swarm optimization (PSO) or ...
Optimal Appearance Model for Visual Tracking.
Directory of Open Access Journals (Sweden)
Yuru Wang
Full Text Available Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models.
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.
Model Updating Nonlinear System Identification Toolbox Project
National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...
Identification of a Hammerstein Model of the Stretch Reflex EMG using Cubic Splines
National Research Council Canada - National Science Library
Dempsey, Erika
2001-01-01
.... The identification algorithm based on a separable least squares Levenberg-Marquardt optimization is used to identify a Hammerstein model of the stretch reflex EMG recorded from a spinal cord injured patient...
Koyuncu, A.; Cigeroglu, E.; Özgüven, H. N.
2017-10-01
In this study, a new approach is proposed for identification of structural nonlinearities by employing cascaded optimization and neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the types of all nonlinearities associated with the nonlinear degrees of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, initially, a feed forward regression neural network is trained, which parametrically identifies the classified nonlinearities. Then, the results obtained are further improved by carrying out an optimization which uses network identified values as starting points. Unlike identification methods available in literature, the proposed approach does not require data collection from the degrees of freedoms where nonlinear elements are attached, and furthermore, it is sufficiently accurate even in the presence of measurement noise. The application of the proposed approach is demonstrated on an example system with nonlinear elements and on a real life experimental setup with a local nonlinearity.
Bioprocess optimization under uncertainty using ensemble modeling
Liu, Yang; Gunawan, Rudiyanto
2017-01-01
The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single “best fit” model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ens...
Faster identification of optimal contraction sequences for tensor networks
Pfeifer, Robert N. C.; Haegeman, Jutho; Verstraete, Frank
2014-09-01
The efficient evaluation of tensor expressions involving sums over multiple indices is of significant importance to many fields of research, including quantum many-body physics, loop quantum gravity, and quantum chemistry. The computational cost of evaluating an expression may depend strongly on the order in which the index sums are evaluated, and determination of the operation-minimizing contraction sequence for a single tensor network (single term, in quantum chemistry) is known to be NP-hard. The current preferred solution is an exhaustive search, using either an iterative depth-first approach with pruning or dynamic programming and memoization, but these approaches are impractical for many of the larger tensor network ansätze encountered in quantum many-body physics. We present a modified search algorithm with enhanced pruning which exhibits a performance increase of several orders of magnitude while still guaranteeing identification of an optimal operation-minimizing contraction sequence for a single tensor network. A reference implementation for matlab, compatible with the ncon() and multienv() network contractors of arXiv:1402.0939 and Evenbly and Pfeifer, Phys. Rev. B 89, 245118 (2014),10.1103/PhysRevB.89.245118, respectively, is supplied.
Surrogate Modeling for Geometry Optimization
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas; Holzwarth, Natalie
2009-01-01
A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used.......A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used....
Intelligent structural optimization: Concept, Model and Methods
International Nuclear Information System (INIS)
Lu, Dagang; Wang, Guangyuan; Peng, Zhang
2002-01-01
Structural optimization has many characteristics of Soft Design, and so, it is necessary to apply the experience of human experts to solving the uncertain and multidisciplinary optimization problems in large-scale and complex engineering systems. With the development of artificial intelligence (AI) and computational intelligence (CI), the theory of structural optimization is now developing into the direction of intelligent optimization. In this paper, a concept of Intelligent Structural Optimization (ISO) is proposed. And then, a design process model of ISO is put forward in which each design sub-process model are discussed. Finally, the design methods of ISO are presented
Towards model evaluation and identification using Self-Organizing Maps
Directory of Open Access Journals (Sweden)
M. Herbst
2008-04-01
Full Text Available The reduction of information contained in model time series through the use of aggregating statistical performance measures is very high compared to the amount of information that one would like to draw from it for model identification and calibration purposes. It has been readily shown that this loss imposes important limitations on model identification and -diagnostics and thus constitutes an element of the overall model uncertainty. In this contribution we present an approach using a Self-Organizing Map (SOM to circumvent the identifiability problem induced by the low discriminatory power of aggregating performance measures. Instead, a Self-Organizing Map is used to differentiate the spectrum of model realizations, obtained from Monte-Carlo simulations with a distributed conceptual watershed model, based on the recognition of different patterns in time series. Further, the SOM is used instead of a classical optimization algorithm to identify those model realizations among the Monte-Carlo simulation results that most closely approximate the pattern of the measured discharge time series. The results are analyzed and compared with the manually calibrated model as well as with the results of the Shuffled Complex Evolution algorithm (SCE-UA. In our study the latter slightly outperformed the SOM results. The SOM method, however, yields a set of equivalent model parameterizations and therefore also allows for confining the parameter space to a region that closely represents a measured data set. This particular feature renders the SOM potentially useful for future model identification applications.
Optimization in engineering models and algorithms
Sioshansi, Ramteen
2017-01-01
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...
Using Pareto points for model identification in predictive toxicology
2013-01-01
Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649
A Model of Optimal Development
Prabir C. Bhattacharya
2015-01-01
The paper addresses the question of optimal development of a developing economy. The framework presented, it is believed, can be of help in thinking about policies relating, inter alia, to population growth, inter-sectoral migration, agriculture-industry relationship, wages in different sectors, and income distribution in an inter-connected way in the context of optimal development of an economy with an informal sector.
Optimal design for nonlinear response models
Fedorov, Valerii V
2013-01-01
Optimal Design for Nonlinear Response Models discusses the theory and applications of model-based experimental design with a strong emphasis on biopharmaceutical studies. The book draws on the authors' many years of experience in academia and the pharmaceutical industry. While the focus is on nonlinear models, the book begins with an explanation of the key ideas, using linear models as examples. Applying the linearization in the parameter space, it then covers nonlinear models and locally optimal designs as well as minimax, optimal on average, and Bayesian designs. The authors also discuss ada
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
Thermoelectric heat sink modeling and optimization
International Nuclear Information System (INIS)
Buist, R.J.; Nagy, M.J.
1994-01-01
Proper design and optimization of a thermoelectric heat sinks has been neglected somewhat in the design of the thermoelectric cooling systems. TE Technology, Inc. has developed a model over a period of 30 hears. The use and application of this model through optimizing heat sink performance is presented
The linear utility model for optimal selection
Mellenbergh, Gideon J.; van der Linden, Willem J.
A linear utility model is introduced for optimal selection when several subpopulations of applicants are to be distinguished. Using this model, procedures are described for obtaining optimal cutting scores in subpopulations in quota-free as well as quota-restricted selection situations. The cutting
Rethinking exchange market models as optimization algorithms
Luquini, Evandro; Omar, Nizam
2018-02-01
The exchange market model has mainly been used to study the inequality problem. Although the human society inequality problem is very important, the exchange market models dynamics until stationary state and its capability of ranking individuals is interesting in itself. This study considers the hypothesis that the exchange market model could be understood as an optimization procedure. We present herein the implications for algorithmic optimization and also the possibility of a new family of exchange market models
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...... authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron data set, while 89.5% accuracy has been achieved on authors' constructed real email data set. The results on Enron data set have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1...
Structural system identification: Structural dynamics model validation
Energy Technology Data Exchange (ETDEWEB)
Red-Horse, J.R.
1997-04-01
Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.
Li, Muqun; Carrell, David; Aberdeen, John; Hirschman, Lynette; Kirby, Jacqueline; Li, Bo; Vorobeychik, Yevgeniy; Malin, Bradley A
2016-06-01
Electronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational research and public policy analysis. To mitigate privacy risks, healthcare organizations (HCOs) aim to remove potentially identifying patient information. A substantial quantity of EMR data is in natural language form and there are concerns that automated tools for detecting identifiers are imperfect and leak information that can be exploited by ill-intentioned data recipients. Thus, HCOs have been encouraged to invest as much effort as possible to find and detect potential identifiers, but such a strategy assumes the recipients are sufficiently incentivized and capable of exploiting leaked identifiers. In practice, such an assumption may not hold true and HCOs may overinvest in de-identification technology. The goal of this study is to design a natural language de-identification framework, rooted in game theory, which enables an HCO to optimize their investments given the expected capabilities of an adversarial recipient. We introduce a Stackelberg game to balance risk and utility in natural language de-identification. This game represents a cost-benefit model that enables an HCO with a fixed budget to minimize their investment in the de-identification process. We evaluate this model by assessing the overall payoff to the HCO and the adversary using 2100 clinical notes from Vanderbilt University Medical Center. We simulate several policy alternatives using a range of parameters, including the cost of training a de-identification model and the loss in data utility due to the removal of terms that are not identifiers. In addition, we compare policy options where, when an attacker is fined for misuse, a monetary penalty is paid to the publishing HCO as opposed to a third party (e.g., a federal regulator). Our results show that when an HCO is forced to exhaust a limited budget (set to $2000 in the study), the precision and recall of the
Portfolio optimization with mean-variance model
Hoe, Lam Weng; Siew, Lam Weng
2016-06-01
Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.
Handbook on modelling for discrete optimization
Pitsoulis, Leonidas; Williams, H
2006-01-01
The primary objective underlying the Handbook on Modelling for Discrete Optimization is to demonstrate and detail the pervasive nature of Discrete Optimization. While its applications cut across an incredibly wide range of activities, many of the applications are only known to specialists. It is the aim of this handbook to correct this. It has long been recognized that "modelling" is a critically important mathematical activity in designing algorithms for solving these discrete optimization problems. Nevertheless solving the resultant models is also often far from straightforward. In recent years it has become possible to solve many large-scale discrete optimization problems. However, some problems remain a challenge, even though advances in mathematical methods, hardware, and software technology have pushed the frontiers forward. This handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It will be done in an academic handbook treatment...
Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm
International Nuclear Information System (INIS)
Canedo Medeiros, Jose Antonio Carlos; Schirru, Roberto
2008-01-01
In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results
Optimization Model for Web Based Multimodal Interactive Simulations.
Halic, Tansel; Ahn, Woojin; De, Suvranu
2015-07-15
This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update . In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.
Identification models of the nervous system.
Zipser, D
1992-01-01
It has been widely observed that when artificial neural networks are trained by supervised learning to do computations that also occur in the nervous system, the behavior of the model neurons often closely resembles that of the real neurons involved in the task. It is not immediately clear why this should be the case or what use can be made of models generated by supervised learning. Here, recent developments are reviewed and analysed in an attempt to clarify these issues. This analysis is facilitated by treating supervised learning models of the brain as a special case of system identification, a general and well-studied modeling paradigm. The neural systems identification paradigm provides a systematic way to generate realistic models starting with a high-level description of a hypothesized computation and some architectural and physiological constraints about the area being modeled. There is no inherent limitation to the realism that can be incorporated into identification models. This approach eliminates the need to find neural implementation algorithms by ad hoc means and provides neuroscientists with a convenient way to build models that account for observed data.
Joint Dynamics Modeling and Parameter Identification for Space Robot Applications
Directory of Open Access Journals (Sweden)
Adenilson R. da Silva
2007-01-01
Full Text Available Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS. The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.
Model Identification of Integrated ARMA Processes
Stadnytska, Tetiana; Braun, Simone; Werner, Joachim
2008-01-01
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
System identification application using Hammerstein model
Indian Academy of Sciences (India)
Saban Ozer
. 20(1): 1175–1188. [4] Hizir N B, Phan M Q, Betti R and Longman R W 2012. Identification of discrete-time bilinear systems through equivalent linear models. Nonlinear Dyn. 69(4): 2065–2078. [5] Hong X, Mitchell R J, Chen S, Harris C J, Li K ...
Optimal Decision Making in Neural Inhibition Models
van Ravenzwaaij, Don; van der Maas, Han L. J.; Wagenmakers, Eric-Jan
2012-01-01
In their influential "Psychological Review" article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the…
Optimizations for the EcoPod field identification tool
Directory of Open Access Journals (Sweden)
Yu YuanYuan
2008-03-01
Full Text Available Abstract Background We sketch our species identification tool for palm sized computers that helps knowledgeable observers with census activities. An algorithm turns an identification matrix into a minimal length series of questions that guide the operator towards identification. Historic observation data from the census geographic area helps minimize question volume. We explore how much historic data is required to boost performance, and whether the use of history negatively impacts identification of rare species. We also explore how characteristics of the matrix interact with the algorithm, and how best to predict the probability of observing a previously unseen species. Results Point counts of birds taken at Stanford University's Jasper Ridge Biological Preserve between 2000 and 2005 were used to examine the algorithm. A computer identified species by correctly answering, and counting the algorithm's questions. We also explored how the character density of the key matrix and the theoretical minimum number of questions for each bird in the matrix influenced the algorithm. Our investigation of the required probability smoothing determined whether Laplace smoothing of observation probabilities was sufficient, or whether the more complex Good-Turing technique is required. Conclusion Historic data improved identification speed, but only impacted the top 25% most frequently observed birds. For rare birds the history based algorithms did not impose a noticeable penalty in the number of questions required for identification. For our dataset neither age of the historic data, nor the number of observation years impacted the algorithm. Density of characters for different taxa in the identification matrix did not impact the algorithms. Intrinsic differences in identifying different birds did affect the algorithm, but the differences affected the baseline method of not using historic data to exactly the same degree. We found that Laplace smoothing
Identification of Hammerstein models with cubic spline nonlinearities.
Dempsey, Erika J; Westwick, David T
2004-02-01
This paper considers the use of cubic splines, instead of polynomials, to represent the static nonlinearities in block structured models. It introduces a system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, where cubic splines represent the static nonlinearity and the linear dynamics are modeled using a finite impulse response filter. The algorithm uses a separable least squares Levenberg-Marquardt optimization to identify Hammerstein cascades whose nonlinearities are modeled by either cubic splines or polynomials. These algorithms are compared in simulation, where the effects of variations in the input spectrum and distribution, and those of the measurement noise are examined. The two algorithms are used to fit Hammerstein models to stretch reflex electromyogram (EMG) data recorded from a spinal cord injured patient. The model with the cubic spline nonlinearity provides more accurate predictions of the reflex EMG than the polynomial based model, even in novel data.
Mathematical modeling and optimization of complex structures
Repin, Sergey; Tuovinen, Tero
2016-01-01
This volume contains selected papers in three closely related areas: mathematical modeling in mechanics, numerical analysis, and optimization methods. The papers are based upon talks presented on the International Conference for Mathematical Modeling and Optimization in Mechanics, held in Jyväskylä, Finland, March 6-7, 2014 dedicated to Prof. N. Banichuk on the occasion of his 70th birthday. The articles are written by well-known scientists working in computational mechanics and in optimization of complicated technical models. Also, the volume contains papers discussing the historical development, the state of the art, new ideas, and open problems arising in modern continuum mechanics and applied optimization problems. Several papers are concerned with mathematical problems in numerical analysis, which are also closely related to important mechanical models. The main topics treated include: * Computer simulation methods in mechanics, physics, and biology; * Variational problems and methods; minimiz...
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
A useful framework for optimal replacement models
International Nuclear Information System (INIS)
Aven, Terje; Dekker, Rommert
1997-01-01
In this note we present a general framework for optimization of replacement times. It covers a number of models, including various age and block replacement models, and allows a uniform analysis for all these models. A relation to the marginal cost concept is described
Optimal decision making in neural inhibition models
van Ravenzwaaij, D.; van der Maas, H.L.J.; Wagenmakers, E.-J.
2012-01-01
In their influential Psychological Review article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the
Multiobjective optimization of an extremal evolution model
International Nuclear Information System (INIS)
Elettreby, M.F.
2004-09-01
We propose a two-dimensional model for a co-evolving ecosystem that generalizes the extremal coupled map lattice model. The model takes into account the concept of multiobjective optimization. We find that the system self-organizes into a critical state. The distributions of the distances between subsequent mutations as well as the distribution of avalanches sizes follow power law. (author)
Optimization Models for Petroleum Field Exploitation
Energy Technology Data Exchange (ETDEWEB)
Jonsbraaten, Tore Wiig
1998-12-31
This thesis presents and discusses various models for optimal development of a petroleum field. The objective of these optimization models is to maximize, under many uncertain parameters, the project`s expected net present value. First, an overview of petroleum field optimization is given from the point of view of operations research. Reservoir equations for a simple reservoir system are derived and discretized and included in optimization models. Linear programming models for optimizing production decisions are discussed and extended to mixed integer programming models where decisions concerning platform, wells and production strategy are optimized. Then, optimal development decisions under uncertain oil prices are discussed. The uncertain oil price is estimated by a finite set of price scenarios with associated probabilities. The problem is one of stochastic mixed integer programming, and the solution approach is to use a scenario and policy aggregation technique developed by Rockafellar and Wets although this technique was developed for continuous variables. Stochastic optimization problems with focus on problems with decision dependent information discoveries are also discussed. A class of ``manageable`` problems is identified and an implicit enumeration algorithm for finding optimal decision policy is proposed. Problems involving uncertain reservoir properties but with a known initial probability distribution over possible reservoir realizations are discussed. Finally, a section on Nash-equilibrium and bargaining in an oil reservoir management game discusses the pool problem arising when two lease owners have access to the same underlying oil reservoir. Because the oil tends to migrate, both lease owners have incentive to drain oil from the competitors part of the reservoir. The discussion is based on a numerical example. 107 refs., 31 figs., 14 tabs.
Enhanced index tracking modelling in portfolio optimization
Lam, W. S.; Hj. Jaaman, Saiful Hafizah; Ismail, Hamizun bin
2013-09-01
Enhanced index tracking is a popular form of passive fund management in stock market. It is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the risk. Enhanced index tracking aims to generate excess return over the return achieved by the index without purchasing all of the stocks that make up the index by establishing an optimal portfolio. The objective of this study is to determine the optimal portfolio composition and performance by using weighted model in enhanced index tracking. Weighted model focuses on the trade-off between the excess return and the risk. The results of this study show that the optimal portfolio for the weighted model is able to outperform the Malaysia market index which is Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.
Contribution to the modeling and the identification of haptic interfaces
International Nuclear Information System (INIS)
Janot, A.
2007-12-01
This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)
Bioprocess optimization under uncertainty using ensemble modeling.
Liu, Yang; Gunawan, Rudiyanto
2017-02-20
The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single "best fit" model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ensemble modeling to account for model uncertainty in bioprocess optimization. More specifically, we adopted a Bayesian approach to define the posterior distribution of the model parameters, based on which we generated an ensemble of model parameters using a uniformly distributed sampling of the parameter confidence region. The ensemble-based process optimization involved maximizing the lower confidence bound of the desired bioprocess objective (e.g. yield or product titer), using a mean-standard deviation utility function. We demonstrated the performance and robustness of the proposed strategy in an application to a monoclonal antibody batch production by mammalian hybridoma cell culture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
Hazard identification based on plant functional modelling
International Nuclear Information System (INIS)
Rasmussen, B.; Whetton, C.
1993-10-01
A major objective of the present work is to provide means for representing a process plant as a socio-technical system, so as to allow hazard identification at a high level. The method includes technical, human and organisational aspects and is intended to be used for plant level hazard identification so as to identify critical areas and the need for further analysis using existing methods. The first part of the method is the preparation of a plant functional model where a set of plant functions link together hardware, software, operations, work organisation and other safety related aspects of the plant. The basic principle of the functional modelling is that any aspect of the plant can be represented by an object (in the sense that this term is used in computer science) based upon an Intent (or goal); associated with each Intent are Methods, by which the Intent is realized, and Constraints, which limit the Intent. The Methods and Constraints can themselves be treated as objects and decomposed into lower-level Intents (hence the procedure is known as functional decomposition) so giving rise to a hierarchical, object-oriented structure. The plant level hazard identification is carried out on the plant functional model using the Concept Hazard Analysis method. In this, the user will be supported by checklists and keywords and the analysis is structured by pre-defined worksheets. The preparation of the plant functional model and the performance of the hazard identification can be carried out manually or with computer support. (au) (4 tabs., 10 ills., 7 refs.)
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.
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Dynamic optimization deterministic and stochastic models
Hinderer, Karl; Stieglitz, Michael
2016-01-01
This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Modeling and optimization of LCD optical performance
Yakovlev, Dmitry A; Kwok, Hoi-Sing
2015-01-01
The aim of this book is to present the theoretical foundations of modeling the optical characteristics of liquid crystal displays, critically reviewing modern modeling methods and examining areas of applicability. The modern matrix formalisms of optics of anisotropic stratified media, most convenient for solving problems of numerical modeling and optimization of LCD, will be considered in detail. The benefits of combined use of the matrix methods will be shown, which generally provides the best compromise between physical adequacy and accuracy with computational efficiency and optimization fac
Parameter Identification of Static Friction Based on An Optimal Exciting Trajectory
Tu, X.; Zhao, P.; Zhou, Y. F.
2017-12-01
In this paper, we focus on how to improve the identification efficiency of friction parameters in a robot joint. First, the static friction model that has only linear dependencies with respect to their parameters is adopted so that the servomotor dynamics can be linearized. In this case, the traditional exciting trajectory based on Fourier series is modified by replacing the constant term with quintic polynomial to ensure the boundary continuity of speed and acceleration. Then, the Fourier-related parameters are optimized by genetic algorithm(GA) in which the condition number of regression matrix is set as the fitness function. At last, compared with the constant-velocity tracking experiment, the friction parameters from the exciting trajectory experiment has the similar result with the advantage of time reduction.
Identification of Optimal Preventive Maintenance Decisions for Composite Components
Laks, P.; Verhagen, W.J.C.; Gherman, B.; Porumbel, I.
2018-01-01
This research proposes a decision support tool which identifies cost-optimal maintenance decisions for a given planning period. Simultaneously, the reliability state of the component is kept at or below a given reliability threshold: a failure limit policy applies. The tool is developed to support
Regularizing and Optimizing LSTM Language Models
Merity, Stephen; Keskar, Nitish Shirish; Socher, Richard
2017-01-01
Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on hidden-to-hidden weights as a form of recurrent r...
Optimal Design of Experiments for Parametric Identification of Civil Engineering Structures
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
Optimal Systems of experiments for parametric identification of civil engineering structures is investigated. Design of experiments for parametric identification of dynamic systems is usually done by minimizing a scalar measure, e.g the determinant, the trace ect., of an estimated parameter covar...... covariance matrix, based on prior knowledge. The experimental conditions available for adjustment, considering in this thesis, are input signal, sampling rate, the location of sensors and number of sensors....
MODELING AND OPTIMIZATION OF THE AEROCONCRETE TECHNOLOGY
Directory of Open Access Journals (Sweden)
Zhukov Aleksey Dmitrievich
2012-07-01
Selection of the appropriate composition and optimal technological parameters is performed with the help of G-BAT-2011 software programme developed at MSUCE. The software is based on the methodology that is based on complete factorial experiments, experiments based on fractional replicates and testing of all essential statistical hypotheses. Linear, incomplete quadratic and quadratic equations generated as a result of experiments make it possible to design a model that represents natural processes in the adequate manner. The model is analytically optimized and interpreted thereafter.
Modeling and Optimization : Theory and Applications Conference
Terlaky, Tamás
2015-01-01
This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 13-15, 2014. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, healthcare, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.
Modeling and Optimization : Theory and Applications Conference
Terlaky, Tamás
2017-01-01
This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 17-19, 2016. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, health, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.
Identification of the noise using mathematical modelling
Directory of Open Access Journals (Sweden)
Dobeš Josef
2016-01-01
Full Text Available In engineering applications the noisiness of a component or the whole device is a common problem. Currently, a lot of effort is put to eliminate noise of the already produced devices, to prevent generation of acoustic waves during the design of new components, or to specify the operating problems based on noisiness change. The experimental method and the mathematical modelling method belong to these identification methods. With the power of today’s computers the ability to identify the sources of the noise on the mathematical modelling level is a very appreciated tool for engineers. For example, the noise itself may be generated by the vibration of the solid object, combustion, shock, fluid flow around an object or cavitation at the fluid flow in an object. For the given task generating the noise using fluid flow on the selected geometry and propagation of the acoustic waves and their subsequent identification are solved and evaluated. In this paper the principle of measurement of variables describing the fluid flow field and acoustic field are described. For the solution of fluid flow a mathematical model implemented into the CFD code is used. The mathematical modelling evaluation of the flow field is compared to the experimental data.
Identification of neutral biochemical network models from time series data
Directory of Open Access Journals (Sweden)
Maia Marco
2009-05-01
Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Identification of neutral biochemical network models from time series data.
Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S
2009-05-05
The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
PEM fuel cell model suitable for energy optimization purposes
International Nuclear Information System (INIS)
Caux, S.; Hankache, W.; Fadel, M.; Hissel, D.
2010-01-01
Many fuel cell stack models or fuel cell system models exist. A model must be built with a main objective, sometimes for accurate electro-chemical behavior description, sometimes for optimization procedure at a system level. In this paper, based on the fundamental reactions present in a fuel cell stack, an accurate model and identification procedure is presented for future energy management in a Hybrid Electrical Vehicle (HEV). The proposed approach extracts all important state variables in such a system and based on the control of the fuel cell's gas flows and temperature, simplification arises to a simple electrical model. Assumptions verified due to the control of the stack allow simplifying the relationships within keeping accuracy in the description of a global fuel cell stack behavior from current demand to voltage. Modeled voltage and current dynamic behaviors are compared with actual measurements. The obtained accuracy is sufficient and less time-consuming (versus other previously published system-oriented models) leading to a suitable model for optimization iterative off-line algorithms.
A Review of the Haspert Model for Target Identification
National Research Council Canada - National Science Library
Mortiss, Genevieve
2001-01-01
The Haspert model for target identification using multiple sensors is examined. Haspert takes a total-cost approach in constructing identification rules for engagements in which friendly, hostile and neutral parties are involved...
Optimal information diffusion in stochastic block models.
Curato, Gianbiagio; Lillo, Fabrizio
2016-09-01
We use the linear threshold model to study the diffusion of information on a network generated by the stochastic block model. We focus our analysis on a two-community structure where the initial set of informed nodes lies only in one of the two communities and we look for optimal network structures, i.e., those maximizing the asymptotic extent of the diffusion. We find that, constraining the mean degree and the fraction of initially informed nodes, the optimal structure can be assortative (modular), core-periphery, or even disassortative. We then look for minimal cost structures, i.e., those for which a minimal fraction of initially informed nodes is needed to trigger a global cascade. We find that the optimal networks are assortative but with a structure very close to a core-periphery graph, i.e., a very dense community linked to a much more sparsely connected periphery.
Optimal inventory management and order book modeling
Baradel, Nicolas
2018-02-16
We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [14, 20, 21], the MM and the HFT define their trading strategy by optimizing the expected utility of terminal wealth, while the IB has a prescheduled task to sell or buy many shares of the considered asset. We derive the variational partial differential equations that characterize the value functions of the MM and HFT and explain how almost optimal control can be deduced from them. We then provide a first illustration of the interactions that can take place between these different market participants by simulating the dynamic of an order book in which each of them plays his own (optimal) strategy.
Fuzzy Stochastic Optimization Theory, Models and Applications
Wang, Shuming
2012-01-01
Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies. The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins...
Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang
2011-05-01
A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.
Applied probability models with optimization applications
Ross, Sheldon M
1992-01-01
Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.
Computer models for optimizing radiation therapy
International Nuclear Information System (INIS)
Duechting, W.
1998-01-01
The aim of this contribution is to outline how methods of system analysis, control therapy and modelling can be applied to simulate normal and malignant cell growth and to optimize cancer treatment as for instance radiation therapy. Based on biological observations and cell kinetic data, several types of models have been developed describing the growth of tumor spheroids and the cell renewal of normal tissue. The irradiation model is represented by the so-called linear-quadratic model describing the survival fraction as a function of the dose. Based thereon, numerous simulation runs for different treatment schemes can be performed. Thus, it is possible to study the radiation effect on tumor and normal tissue separately. Finally, this method enables a computer-assisted recommendation for an optimal patient-specific treatment schedule prior to clinical therapy. (orig.) [de
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model...
DEFF Research Database (Denmark)
Suárez, Carlos Gómez; Reigosa, Paula Diaz; Iannuzzo, Francesco
2016-01-01
An original tool for parameter extraction of PSpice models has been released, enabling a simple parameter identification. A physics-based IGBT model is used to demonstrate that the optimization tool is capable of generating a set of parameters which predicts the steady-state and switching behavior...
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.)
Yang, Guoxiang; Best, Elly P H
2015-09-15
Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions. Copyright © 2015 Elsevier Ltd. All rights reserved.
Optimization and mathematical modeling in computer architecture
Sankaralingam, Karu; Nowatzki, Tony
2013-01-01
In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms t
Modelling and Identification of Induction Machines
Energy Technology Data Exchange (ETDEWEB)
Nestli, T.F.
1995-12-01
To obtain high quality control of the induction machine, field orientation is probably the most frequently used control strategy. Using this strategy requires that one of the flux space vectors be known. Since this cannot be measured, many predictor models for calculation of the rotor flux space vector in real time have been developed. This doctoral thesis presents an analysis method for evaluating and comparing predictor models for flux calculation with respect to sensitivity to parameter deviations and measurement errors and with respect to dynamics. It is concluded that the best predictor models in the minimum sensitivity sense should have properties similar to the current and voltage models at lower and higher frequencies, respectively. To further reduce flux estimation errors, a new saturation model for the Inverse {Gamma}-formulation of the induction machine is developed. It is shown that the leakage reactance varies mainly with stator current, and the magnetizing reactance depends both on stator flux and rotor current magnitudes, i.e., both on magnetization and load. The reactance models are verified by experiments. An off-line identification algorithm is developed to identify the parameters of the reactance model and initial values for the stator and rotor resistances. The algorithm is verified in laboratory experiments, which also demonstrate the temperature dependence of the resistances. 36 refs., 49 figs., 6 tabs.
Model-Based Optimizing Control and Estimation Using Modelica Model
Directory of Open Access Journals (Sweden)
L. Imsland
2010-07-01
Full Text Available This paper reports on experiences from case studies in using Modelica/Dymola models interfaced to control and optimization software, as process models in real time process control applications. Possible applications of the integrated models are in state- and parameter estimation and nonlinear model predictive control. It was found that this approach is clearly possible, providing many advantages over modeling in low-level programming languages. However, some effort is required in making the Modelica models accessible to NMPC software.
Systematic approach for the identification of process reference models
CSIR Research Space (South Africa)
Van Der Merwe, A
2009-02-01
Full Text Available Process models are used in different application domains to capture knowledge on the process flow. Process reference models (PRM) are used to capture reusable process models, which should simplify the identification process of process models...
Response Surface Model Building and Multidisciplinary Optimization Using D-Optimal Designs
Unal, Resit; Lepsch, Roger A.; McMillin, Mark L.
1998-01-01
This paper discusses response surface methods for approximation model building and multidisciplinary design optimization. The response surface methods discussed are central composite designs, Bayesian methods and D-optimal designs. An over-determined D-optimal design is applied to a configuration design and optimization study of a wing-body, launch vehicle. Results suggest that over determined D-optimal designs may provide an efficient approach for approximation model building and for multidisciplinary design optimization.
MARKOV CHAIN PORTFOLIO LIQUIDITY OPTIMIZATION MODEL
Directory of Open Access Journals (Sweden)
Eder Oliveira Abensur
2014-05-01
Full Text Available The international financial crisis of September 2008 and May 2010 showed the importance of liquidity as an attribute to be considered in portfolio decisions. This study proposes an optimization model based on available public data, using Markov chain and Genetic Algorithms concepts as it considers the classic duality of risk versus return and incorporating liquidity costs. The work intends to propose a multi-criterion non-linear optimization model using liquidity based on a Markov chain. The non-linear model was tested using Genetic Algorithms with twenty five Brazilian stocks from 2007 to 2009. The results suggest that this is an innovative development methodology and useful for developing an efficient and realistic financial portfolio, as it considers many attributes such as risk, return and liquidity.
Global Optimization Ensemble Model for Classification Methods
Directory of Open Access Journals (Sweden)
Hina Anwar
2014-01-01
Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.
Efficient Iris Localization via Optimization Model
Directory of Open Access Journals (Sweden)
Qi Wang
2017-01-01
Full Text Available Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.
Method of transient identification based on a possibilistic approach, optimized by genetic algorithm
International Nuclear Information System (INIS)
Almeida, Jose Carlos Soares de
2001-02-01
This work develops a method for transient identification based on a possible approach, optimized by Genetic Algorithm to optimize the number of the centroids of the classes that represent the transients. The basic idea of the proposed method is to optimize the partition of the search space, generating subsets in the classes within a partition, defined as subclasses, whose centroids are able to distinguish the classes with the maximum correct classifications. The interpretation of the subclasses as fuzzy sets and the possible approach provided a heuristic to establish influence zones of the centroids, allowing to achieve the 'don't know' answer for unknown transients, that is, outside the training set. (author)
Computer modeling for optimal placement of gloveboxes
Energy Technology Data Exchange (ETDEWEB)
Hench, K.W.; Olivas, J.D. [Los Alamos National Lab., NM (United States); Finch, P.R. [New Mexico State Univ., Las Cruces, NM (United States)
1997-08-01
Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components (pits) in an environment of intense regulation and shrinking budgets. Historically, the location of gloveboxes in a processing area has been determined without benefit of industrial engineering studies to ascertain the optimal arrangement. The opportunity exists for substantial cost savings and increased process efficiency through careful study and optimization of the proposed layout by constructing a computer model of the fabrication process. This paper presents an integrative two- stage approach to modeling the casting operation for pit fabrication. The first stage uses a mathematical technique for the formulation of the facility layout problem; the solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a computer simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units.
Computer modeling for optimal placement of gloveboxes
International Nuclear Information System (INIS)
Hench, K.W.; Olivas, J.D.; Finch, P.R.
1997-08-01
Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components (pits) in an environment of intense regulation and shrinking budgets. Historically, the location of gloveboxes in a processing area has been determined without benefit of industrial engineering studies to ascertain the optimal arrangement. The opportunity exists for substantial cost savings and increased process efficiency through careful study and optimization of the proposed layout by constructing a computer model of the fabrication process. This paper presents an integrative two- stage approach to modeling the casting operation for pit fabrication. The first stage uses a mathematical technique for the formulation of the facility layout problem; the solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a computer simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units
Developments in model-based optimization and control distributed control and industrial applications
Grancharova, Alexandra; Pereira, Fernando
2015-01-01
This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...
Practical Modeling and Comprehensive System Identification of a BLDC Motor
Directory of Open Access Journals (Sweden)
Changle Xiang
2015-01-01
Full Text Available The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.
Behavioral optimization models for multicriteria portfolio selection
Directory of Open Access Journals (Sweden)
Mehlawat Mukesh Kumar
2013-01-01
Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.
On the optimal identification of tag sets in time-constrained RFID configurations.
Vales-Alonso, Javier; Bueno-Delgado, María Victoria; Egea-López, Esteban; Alcaraz, Juan José; Pérez-Mañogil, Juan Manuel
2011-01-01
In Radio Frequency Identification facilities the identification delay of a set of tags is mainly caused by the random access nature of the reading protocol, yielding a random identification time of the set of tags. In this paper, the cumulative distribution function of the identification time is evaluated using a discrete time Markov chain for single-set time-constrained passive RFID systems, namely those ones where a single group of tags is assumed to be in the reading area and only for a bounded time (sojourn time) before leaving. In these scenarios some tags in a set may leave the reader coverage area unidentified. The probability of this event is obtained from the cumulative distribution function of the identification time as a function of the sojourn time. This result provides a suitable criterion to minimize the probability of losing tags. Besides, an identification strategy based on splitting the set of tags in smaller subsets is also considered. Results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags.
On the Uncertainty of Identification of Civil Engineering Structures Using ARMA Models
DEFF Research Database (Denmark)
Andersen, Palle; Brincker, Rune; Kirkegaard, Poul Henning
1995-01-01
In this paper the uncertainties of modal parameters estimated using ARMA models for identification of civil engineering structures are investigated. How to initialize the predictor part of a Gauss-Newton optimization algorithm is put in focus. A backward-forecasting procedure for initialization...
On the Uncertainty of Identification of Civil Engineering Structures using ARMA Models
DEFF Research Database (Denmark)
Andersen, P.; Brincker, Rune; Kirkegaard, Poul Henning
In this paper the uncertainties of modal parameters estimated using ARMA models for identification of civil engineering structures are investigated. How to initialize the predictor part of a Gauss-Newton optimization algorithm is put in focus. A backward-forecasting procedure for initialization...
Combinatorial Optimization in Forest Ecosystem Management Modeling
BAŞKENT, Emin Zeki
2001-01-01
Modeling forest management activities has been tackled by scientists over the last two decades. Both simulation and optimization techniques have been used in solving forest management planning problems. With the introduction of ecosystems management that focuses on the sustainable production and maintenance of ecological, social and economical values, neither approach provided a credible solution technique to help design the complex structure of forest management activities. Alternative to th...
Zhang, Shou-ping; Xin, Xiao-kang
2017-07-01
Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.
Computational modeling, optimization and manufacturing simulation of advanced engineering materials
2016-01-01
This volume presents recent research work focused in the development of adequate theoretical and numerical formulations to describe the behavior of advanced engineering materials. Particular emphasis is devoted to applications in the fields of biological tissues, phase changing and porous materials, polymers and to micro/nano scale modeling. Sensitivity analysis, gradient and non-gradient based optimization procedures are involved in many of the chapters, aiming at the solution of constitutive inverse problems and parameter identification. All these relevant topics are exposed by experienced international and inter institutional research teams resulting in a high level compilation. The book is a valuable research reference for scientists, senior undergraduate and graduate students, as well as for engineers acting in the area of computational material modeling.
Model Updating Nonlinear System Identification Toolbox, Phase II
National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...
Bionic models for identification of biological systems
Gerget, O. M.
2017-01-01
This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
Chemical identification using Bayesian model selection
Energy Technology Data Exchange (ETDEWEB)
Burr, Tom; Fry, H. A. (Herbert A.); McVey, B. D. (Brian D.); Sander, E. (Eric)
2002-01-01
Remote detection and identification of chemicals in a scene is a challenging problem. We introduce an approach that uses some of the image's pixels to establish the background characteristics while other pixels represent the target for which we seek to identify all chemical species present. This leads to a generalized least squares problem in which we focus on 'subset selection' to identify the chemicals thought to be present. Bayesian model selection allows us to approximate the posterior probability that each chemical in the library is present by adding the posterior probabilities of all the subsets which include the chemical. We present results using realistic simulated data for the case with 1 to 5 chemicals present in each target and compare performance to a hybrid of forward and backward stepwise selection procedure using the F statistic.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox
International Nuclear Information System (INIS)
Rabbani, Muhammad Junaid; Hussain, Kashan; Khan, Asim-ur-Rehman; Ali, Abdullah
2013-01-01
This paper proposed a systematic approach to select a mathematical model for an industrial heating system by adopting system identification techniques with the aim of fulfilling the design requirement for the controller. The model identification process will begin by collecting real measurement data samples with the aid of MATLAB system identification toolbox. The criteria for selecting the model that could validate model output with actual data will based upon: parametric identification technique, picking the best model structure with low order among ARX, ARMAX and BJ, and then applying model estimation and validation tests. Simulated results have shown that the BJ model has been best in providing good estimation and validation based upon performance criteria such as: final prediction error, loss function, best percentage of model fit, and co-relation analysis of residual for output
Directory of Open Access Journals (Sweden)
Hakan Gökdağ
2013-01-01
Full Text Available In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO, is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT. It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.
Modeling and optimization of a chiller plant
International Nuclear Information System (INIS)
Wei, Xiupeng; Xu, Guanglin; Kusiak, Andrew
2014-01-01
A data-driven approach is utilized to model a chiller plant that has four chillers, four cooling towers, and two chilled water storage tanks. The chillers have varying energy efficiency. Since the chiller plant model derived from data-driven approach is nonlinear and non-convex, it is not practical to solve it by using the traditional gradient-based optimization algorithm. A two-level intelligent algorithm is developed to solve the model aiming at minimizing the total cost of the chilled water plant. The proposed algorithm can effectively search the optimum under the non-convex and nonlinear situation. A simulation case is conducted and the corresponding results are discussed. - Highlights: • Development of a data-driven based model of a complete chiller plant. • A two-level intelligent algorithm is proposed to optimize the chiller plant which is non-convex and nonlinear problem. • A simulation is conducted to verify the performance of the model and algorithm. • 14 percent of energy saving can be achieved with proposed method
Benchmarking model-free and model-based optimal control
Koryakovskiy, I.; Kudruss, M.; Babuska, R.; Caarls, W.; Kirches, Christian; Mombaur, Katja; Schlöder, Johannes P.; Vallery, H.
2017-01-01
Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while
DEFF Research Database (Denmark)
Guedes, J.M.; Rodrigues, H.C.; Bendsøe, Martin P.
2003-01-01
This paper describes a computational model, based on inverse homogenization and topology design, for approximating energy bounds for two-phase composites under multiple load cases. The approach allows for the identification of possible single-scale cellular materials that give rise to the optimal...
Optimization of hybrid model on hajj travel
Cahyandari, R.; Ariany, R. L.; Sukono
2018-03-01
Hajj travel insurance is an insurance product offered by the insurance company in preparing funds to perform the pilgrimage. This insurance product helps would-be pilgrims to set aside a fund of saving hajj with regularly, but also provides funds of profit sharing (mudharabah) and insurance protection. Scheme of insurance product fund management is largely using the hybrid model, which is the fund from would-be pilgrims will be divided into three account management, that is personal account, tabarru’, and ujrah. Scheme of hybrid model on hajj travel insurance was already discussed at the earlier paper with titled “The Hybrid Model Algorithm on Sharia Insurance”, taking the example case of Mitra Mabrur Plus product from Bumiputera company. On these advanced paper, will be made the previous optimization model design, with partition of benefit the tabarru’ account. Benefits such as compensation for 40 critical illness which initially only for participants of insurance only, on optimization is intended for participants of the insurance and his heir, also to benefit the hospital bills. Meanwhile, the benefits of death benefit is given if the participant is fixed die.
Combined optimization model for sustainable energization strategy
Abtew, Mohammed Seid
Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.
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.
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
Abrahart, R. J.
2004-05-01
This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and
Hou, Zeyu; Lu, Wenxi
2018-05-01
Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation-optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation-optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and also maintained high computation accuracy.
Numerical modeling and optimization of machining duplex stainless steels
Directory of Open Access Journals (Sweden)
Rastee D. Koyee
2015-01-01
Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model...... is a convex combination of a mapped coarse model and a linearized fine model. It exploits, in a novel way, a linear frequency-sensitive mapping. During the optimization iterates, the coarse and fine models are simulated at different sets of frequencies. This approach is shown to be especially powerful...
Application of Scheffe's Model in Optimization of Compressive cube ...
African Journals Online (AJOL)
experimental method using Scheffe's simplex model in optimizing the compressive strength of river stone aggregate concrete. The model was used to optimize the compressive strength of concrete made from river stone aggregate, river sand, cement, ...
Adaptive stimulus optimization and model-based experiments for sensory systems neuroscience
Directory of Open Access Journals (Sweden)
Christopher eDiMattina
2013-06-01
Full Text Available 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 textit{optimal stimulus} paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the textit{iso-response} paradigm which finds sets of stimuli giving rise to constant responses, and the textit{system identification} paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of nonlinear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify nonlinear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and towards a new paradigm of real-time model estimation and comparison.
A system identification model for adaptive nonlinear control
Linse, Dennis J.; Stengel, Robert F.
1991-01-01
A system identification model that combines generalized-spline function approximation with a nonlinear control system is described. The complete control system contains three main elements: a nonlinear-inverse-dynamic control law that depends on a comprehensive model of the plant, a state estimator whose outputs drive the control law, and a function approximation scheme that models the system dynamics. The system-identification task, which combines an extended Kalman filter with a function approximator modeled as an artificial neural network, is considered. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.
A globally optimal k-anonymity method for the de-identification of health data.
El Emam, Khaled; Dankar, Fida Kamal; Issa, Romeo; Jonker, Elizabeth; Amyot, Daniel; Cogo, Elise; Corriveau, Jean-Pierre; Walker, Mark; Chowdhury, Sadrul; Vaillancourt, Regis; Roffey, Tyson; Bottomley, Jim
2009-01-01
Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified. The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated. The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution. For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.
Optimal evolution models for quantum tomography
International Nuclear Information System (INIS)
Czerwiński, Artur
2016-01-01
The research presented in this article concerns the stroboscopic approach to quantum tomography, which is an area of science where quantum physics and linear algebra overlap. In this article we introduce the algebraic structure of the parametric-dependent quantum channels for 2-level and 3-level systems such that the generator of evolution corresponding with the Kraus operators has no degenerate eigenvalues. In such cases the index of cyclicity of the generator is equal to 1, which physically means that there exists one observable the measurement of which performed a sufficient number of times at distinct instants provides enough data to reconstruct the initial density matrix and, consequently, the trajectory of the state. The necessary conditions for the parameters and relations between them are introduced. The results presented in this paper seem to have considerable potential applications in experiments due to the fact that one can perform quantum tomography by conducting only one kind of measurement. Therefore, the analyzed evolution models can be considered optimal in the context of quantum tomography. Finally, we introduce some remarks concerning optimal evolution models in the case of n-dimensional Hilbert space. (paper)
Aerodynamic Modelling and Optimization of Axial Fans
DEFF Research Database (Denmark)
Sørensen, Dan Nørtoft
A numerically efficient mathematical model for the aerodynamics oflow speed axial fans of the arbitrary vortex flow type has been developed.The model is based on a blade-element principle, whereby therotor is divided into a number of annular streamtubes.For each of these streamtubes relations...... and integrated propertiesshow that the computed results agree well with the measurements.Integrating a rotor-only version of the aerodynamic modelwith an algorithm for numerical designoptimization, enables the finding of an optimum fan rotor.The angular velocity of the rotor, the hub radius and the spanwise...... of fan efficiency in a design interval of flow rates,thus designinga fan which operates well over a range of different flow conditions.The optimization scheme was used to investigate the dependence ofmaximum efficiency on1: the number of blades,2: the width of the design interval and3: the hub radius...
System Identification Based Proxy Model of a Reservoir under Water Injection
Directory of Open Access Journals (Sweden)
Berihun M. Negash
2017-01-01
Full Text Available Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.
GPCR Homology Model Generation for Lead Optimization.
Tautermann, Christofer S
2018-01-01
The vast increase of recently solved GPCR X-ray structures forms the basis for GPCR homology modeling to atomistic accuracy. Nowadays, homology models can be employed for GPCR-ligand optimization and have been reported as invaluable tools for drug design in the last few years. Elucidation of the complex GPCR pharmacology and the associated GPCR conformations made clear that different homology models have to be constructed for different activation states of the GPCRs. Therefore, templates have to be chosen accordingly to their sequence homology as well as to their activation state. The subsequent ligand placement is nontrivial, as some recent X-ray structures show very unusual ligand binding sites and solvent involvement, expanding the space of the putative ligand binding site from the generic retinal binding pocket to the whole receptor. In the present study, a workflow is presented starting from the selection of the target sequence, guiding through the GPCR modeling process, and finishing with ligand placement and pose validation.
Modeling and optimization of planar microcoils
International Nuclear Information System (INIS)
Beyzavi, Ali; Nguyen, Nam-Trung
2008-01-01
Magnetic actuation has emerged as a useful tool for manipulating particles, droplets and biological samples in microfluidics. A planar coil is one of the suitable candidates for magnetic actuation and has the potential to be integrated in digital microfluidic devices. A simple model of microcoils is needed to optimize their use in actuation applications. This paper first develops an analytical model for calculating the magnetic field of a planar microcoil. The model was validated by experimental data from microcoils fabricated on printed circuit boards (PCB). The model was used for calculating the field strength and the force acting on a magnetic object. Finally, the effect of different coil parameters such as the magnitude of the electric current, the gap between the wires and the number of wire segments is discussed. Both analytical and experimental results show that a smaller gap size between wire segments, more wire segments and a higher electric current can increase both the magnitude and the gradient of the magnetic field, and consequently cause a higher actuating force. The planar coil analyzed in the paper is suitable for applications in magnetic droplet-based microfluidics
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models
Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237
Multiobjective Optimization Model for Wind Power Allocation
Directory of Open Access Journals (Sweden)
Juan Alemany
2017-01-01
Full Text Available There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented ε-constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.
Modelling and identification of a six axes industrial robot
Waiboer, R.R.; Aarts, Ronald G.K.M.; Jonker, Jan B.; ASME,
2005-01-01
This paper deals with the modelling and identification of a six axes industrial St ¨aubli RX90 robot. A non-linear finite element method is used to generate the dynamic equations of motion in a form suitable for both simulation and identification. The latter requires that the equations of motion are
Pole-zero form fractional model identification in frequency domain
International Nuclear Information System (INIS)
Mansouri, R.; Djamah, T.; Djennoune, S.; Bettayeb, M.
2009-01-01
This paper deals with system identification in the frequency domain using non integer order models given in the pole-zero form. The usual identification techniques cannot be used in this case because of the non integer orders of differentiation which makes the problem strongly nonlinear. A general identification method based on Levenberg-Marquardt algorithm is developed and allows to estimate the (2n+2m+1) parameters of the model. Its application to identify the ''skin effect'' of a squirrel cage induction machine modeling is then presented.
Kanban simulation model for production process optimization
Directory of Open Access Journals (Sweden)
Golchev Riste
2015-01-01
Full Text Available A long time has passed since the KANBAN system has been established as an efficient method for coping with the excessive inventory. Still, the possibilities for its improvement through its integration with other different approaches should be investigated further. The basic research challenge of this paper is to present benefits of KANBAN implementation supported with Discrete Event Simulation (DES. In that direction, at the beginning, the basics of KANBAN system are presented with emphasis on the information and material flow, together with a methodology for implementation of KANBAN system. Certain analysis on combining the simulation with this methodology is presented. The paper is concluded with a practical example which shows that through understanding the philosophy of the implementation methodology of KANBAN system and the simulation methodology, a simulation model can be created which can serve as a basis for a variety of experiments that can be conducted within a short period of time, resulting with production process optimization.
Modeling and Optimizing Antennas for Rotational Spectroscopy Applications
Directory of Open Access Journals (Sweden)
Z. Raida
2006-12-01
Full Text Available In the paper, dielectric and metallic lenses are modeled and optimized in order to enhance the gain of a horn antenna in the frequency range from 60 GHz to 100 GHz. Properties of designed lenses are compared and discussed. The structures are modeled in CST Microwave Studio and optimized by Particle Swarm Optimization (PSO in order to get required antenna parameters.
Directory of Open Access Journals (Sweden)
Hernán Darío Vargas Cardona
2015-07-01
Full Text Available Identification of brain signals from microelectrode recordings (MER is a key procedure during deep brain stimulation (DBS applied in Parkinson’s disease patients. The main purpose of this research work is to identify with high accuracy a brain structure called subthalamic nucleus (STN, since it is the target structure where the DBS achieves the best therapeutic results. To do this, we present an approach for optimal representation of MER signals through method of frames. We obtain coefficients that minimize the Euclidean norm of order two. From optimal coefficients, we extract some features from signals combining the wavelet packet and cosine dictionaries. For a comparison frame with the state of the art, we also process the signals using the discrete wavelet transform (DWT with several mother functions. We validate the proposed methodology in a real data base. We employ simple supervised machine learning algorithms, as the K-Nearest Neighbors classifier (K-NN, a linear Bayesian classifier (LDC and a quadratic Bayesian classifier (QDC. Classification results obtained with the proposed method improves significantly the performance of the DWT. We achieve a positive identification of the STN superior to 97,6%. Identification outcomes achieved by the MOF are highly accurate, as we can potentially get a false positive rate of less than 2% during the DBS.
Robust model identification applied to type 1diabetes
DEFF Research Database (Denmark)
Finan, Daniel Aaron; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad
2010-01-01
In many realistic applications, process noise is known to be neither white nor normally distributed. When identifying models in these cases, it may be more effective to minimize a different penalty function than the standard sum of squared errors (as in a least-squares identification method......). This paper investigates model identification based on two different penalty functions: the 1-norm of the prediction errors and a Huber-type penalty function. For data characteristic of some realistic applications, model identification based on these latter two penalty functions is shown to result in more...... accurate estimates of parameters than the standard least-squares solution, and more accurate model predictions for test data. The identification techniques are demonstrated on a simple toy problem as well as a physiological model of type 1 diabetes....
Modeling and optimization of wet sizing process
International Nuclear Information System (INIS)
Thai Ba Cau; Vu Thanh Quang and Nguyen Ba Tien
2004-01-01
Mathematical simulation on basis of Stock law has been done for wet sizing process on cylinder equipment of laboratory and semi-industrial scale. The model consists of mathematical equations describing relations between variables, such as: - Resident time distribution function of emulsion particles in the separating zone of the equipment depending on flow-rate, height, diameter and structure of the equipment. - Size-distribution function in the fine and coarse parts depending on resident time distribution function of emulsion particles, characteristics of the material being processed, such as specific density, shapes, and characteristics of the environment of classification, such as specific density, viscosity. - Experimental model was developed on data collected from an experimental cylindrical equipment with diameter x height of sedimentation chamber equal to 50 x 40 cm for an emulsion of zirconium silicate in water. - Using this experimental model allows to determine optimal flow-rate in order to obtain product with desired grain size in term of average size or size distribution function. (author)
Aerial Search Optimization Model (ASOM) for UAVs in Special Operations
National Research Council Canada - National Science Library
Kress, Moshe; Royset, Johannes O
2007-01-01
.... The goal is to detect the largest possible number of targets with the given resources. The model prescribes optimal deployment locations for the ground units and optimal time-phased search areas for the UAVs...
Ground Vehicle System Integration (GVSI) and Design Optimization Model
National Research Council Canada - National Science Library
Horton, William
1996-01-01
This report documents the Ground Vehicle System Integration (GVSI) and Design Optimization Model GVSI is a top-level analysis tool designed to support engineering tradeoff studies and vehicle design optimization efforts...
Systematic identification of crystallization kinetics within a generic modelling framework
DEFF Research Database (Denmark)
Abdul Samad, Noor Asma Fazli Bin; Meisler, Kresten Troelstrup; Gernaey, Krist
2012-01-01
A systematic development of constitutive models within a generic modelling framework has been developed for use in design, analysis and simulation of crystallization operations. The framework contains a tool for model identification connected with a generic crystallizer modelling tool-box, a tool...
Analysis of Offshore Knuckle Boom Crane - Part One: Modeling and Parameter Identification
Directory of Open Access Journals (Sweden)
Morten K. Bak
2013-10-01
Full Text Available This paper presents an extensive model of a knuckle boom crane used for pipe handling on offshore drilling rigs. The mechanical system is modeled as a multi-body system and includes the structural flexibility and damping. The motion control system model includes the main components of the crane's electro-hydraulic actuation system. For this a novel black-box model for counterbalance valves is presented, which uses two different pressure ratios to compute the flow through the valve. Experimental data and parameter identification, based on both numerical optimization and manual tuning, are used to verify the crane model. The demonstrated modeling and parameter identification techniques target the system engineer and takes into account the limited access to component data normally encountered by engineers working with design of hydraulic systems.
Modeling of Biometric Identification System Using the Colored Petri Nets
Petrosyan, G. R.; Ter-Vardanyan, L. A.; Gaboutchian, A. V.
2015-05-01
In this paper we present a model of biometric identification system transformed into Petri Nets. Petri Nets, as a graphical and mathematical tool, provide a uniform environment for modelling, formal analysis, and design of discrete event systems. The main objective of this paper is to introduce the fundamental concepts of Petri Nets to the researchers and practitioners, both from identification systems, who are involved in the work in the areas of modelling and analysis of biometric identification types of systems, as well as those who may potentially be involved in these areas. In addition, the paper introduces high-level Petri Nets, as Colored Petri Nets (CPN). In this paper the model of Colored Petri Net describes the identification process much simpler.
Model Updating Nonlinear System Identification Toolbox, Phase I
National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...
International Nuclear Information System (INIS)
Gong, Wenyin; Cai, Zhihua
2013-01-01
Parameter identification of PEM (proton exchange membrane) fuel cell model is a very active area of research. Generally, it can be treated as a numerical optimization problem with complex nonlinear and multi-variable features. DE (differential evolution), which has been successfully used in various fields, is a simple yet efficient evolutionary algorithm for global numerical optimization. In this paper, with the objective of accelerating the process of parameter identification of PEM fuel cell models and reducing the necessary computational efforts, we firstly present a generic and simple ranking-based mutation operator for the DE algorithm. Then, the ranking-based mutation operator is incorporated into five highly-competitive DE variants to solve the PEM fuel cell model parameter identification problems. The main contributions of this work are the proposed ranking-based DE variants and their application to the parameter identification problems of PEM fuel cell models. Experiments have been conducted by using both the simulated voltage–current data and the data obtained from the literature to validate the performance of our approach. The results indicate that the ranking-based DE methods provide better results with respect to the solution quality, the convergence rate, and the success rate compared with their corresponding original DE methods. In addition, the voltage–current characteristics obtained by our approach are in good agreement with the original voltage–current curves in all cases. - Highlights: • A simple and generic ranking-based mutation operator is presented in this paper. • Several DE (differential evolution) variants are used to solve the parameter identification of PEMFC (proton exchange membrane fuel cells) model. • Results show that our method accelerates the process of parameter identification. • The V–I characteristics are in very good agreement with experimental data
An optimization model for improving highway safety
Directory of Open Access Journals (Sweden)
Promothes Saha
2016-12-01
Full Text Available This paper developed a traffic safety management system (TSMS for improving safety on county paved roads in Wyoming. TSMS is a strategic and systematic process to improve safety of roadway network. When funding is limited, it is important to identify the best combination of safety improvement projects to provide the most benefits to society in terms of crash reduction. The factors included in the proposed optimization model are annual safety budget, roadway inventory, roadway functional classification, historical crashes, safety improvement countermeasures, cost and crash reduction factors (CRFs associated with safety improvement countermeasures, and average daily traffics (ADTs. This paper demonstrated how the proposed model can identify the best combination of safety improvement projects to maximize the safety benefits in terms of reducing overall crash frequency. Although the proposed methodology was implemented on the county paved road network of Wyoming, it could be easily modified for potential implementation on the Wyoming state highway system. Other states can also benefit by implementing a similar program within their jurisdictions.
Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai
2015-01-01
, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC......This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead...... of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm...
Incorporating model uncertainty into optimal insurance contract design
Pflug, G.; Timonina-Farkas, A.; Hochrainer-Stigler, S.
2017-01-01
In stochastic optimization models, the optimal solution heavily depends on the selected probability model for the scenarios. However, the scenario models are typically chosen on the basis of statistical estimates and are therefore subject to model error. We demonstrate here how the model uncertainty can be incorporated into the decision making process. We use a nonparametric approach for quantifying the model uncertainty and a minimax setup to find model-robust solutions. The method is illust...
Directory of Open Access Journals (Sweden)
Khanagha Ali
2010-01-01
Full Text Available Blind identification of MIMO FIR systems has widely received attentions in various fields of wireless data communications. Here, we use Particle Swarm Optimization (PSO as the update mechanism of the well-known inverse filtering approach and we show its good performance compared to original method. Specially, the proposed method is shown to be more robust against lower SNR scenarios or in cases with smaller lengths of available data records. Also, a modified version of PSO is presented which further improves the robustness and preciseness of PSO algorithm. However the most important promise of the modified version is its drastically faster convergence compared to standard implementation of PSO.
Modeling emotional content of music using system identification.
Korhonen, Mark D; Clausi, David A; Jernigan, M Ed
2006-06-01
Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.
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.
International Nuclear Information System (INIS)
Nicolau, Andressa; Schirru, Roberto; Medeiros, Jose A.C.C.
2009-01-01
This work presents the results of a performance evaluation study of the quantum based algorithms, QEA (Quantum Inspired Evolutionary Algorithm) and QSE (Quantum Swarm Evolutionary), when applied to the transient identification optimization problem of a nuclear power station operating at 100% of full power. For the sake of evaluation of the algorithms 3 benchmark functions were used. When compared to other similar optimization methods QEA showed that it can be an efficient optimization tool, not only for combinatorial problems but also for numerical problems, particularly for complex problems as the identification of transients in a nuclear power station. (author)
The effect of using a robust optimality criterion in model based adaptive optimization.
Strömberg, Eric A; Hooker, Andrew C
2017-08-01
Optimizing designs using robust (global) optimality criteria has been shown to be a more flexible approach compared to using local optimality criteria. Additionally, model based adaptive optimal design (MBAOD) may be less sensitive to misspecification in the prior information available at the design stage. In this work, we investigate the influence of using a local (lnD) or a robust (ELD) optimality criterion for a MBAOD of a simulated dose optimization study, for rich and sparse sampling schedules. A stopping criterion for accurate effect prediction is constructed to determine the endpoint of the MBAOD by minimizing the expected uncertainty in the effect response of the typical individual. 50 iterations of the MBAODs were run using the MBAOD R-package, with the concentration from a one-compartment first-order absorption pharmacokinetic model driving the population effect response in a sigmoidal EMAX pharmacodynamics model. The initial cohort consisted of eight individuals in two groups and each additional cohort added two individuals receiving a dose optimized as a discrete covariate. The MBAOD designs using lnD and ELD optimality with misspecified initial model parameters were compared by evaluating the efficiency relative to an lnD-optimal design based on the true parameter values. For the explored example model, the MBAOD using ELD-optimal designs converged quicker to the theoretically optimal lnD-optimal design based on the true parameters for both sampling schedules. Thus, using a robust optimality criterion in MBAODs could reduce the number of adaptations required and improve the practicality of adaptive trials using optimal design.
Process optimization of friction stir welding based on thermal models
DEFF Research Database (Denmark)
Larsen, Anders Astrup
2010-01-01
This thesis investigates how to apply optimization methods to numerical models of a friction stir welding process. The work is intended as a proof-of-concept using different methods that are applicable to models of high complexity, possibly with high computational cost, and without the possibility...... information of the high-fidelity model. The optimization schemes are applied to stationary thermal models of differing complexity of the friction stir welding process. The optimization problems considered are based on optimizing the temperature field in the workpiece by finding optimal translational speed...... and the backingplate by solving an inverse modelling problem in which experimental data and a numerical model are used for determining the contact heat transfer coefficient. Different parametrizations of the spatial distribution of the heat transfer coefficient are studied and discussed, and the optimization problem...
Stochastic Models in the Identification Process
Czech Academy of Sciences Publication Activity Database
Slovák, Dalibor; Zvárová, Jana
2011-01-01
Roč. 7, č. 1 (2011), s. 44-50 ISSN 1801-5603 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : identification process * weight-of evidence formula * coancestry coefficient * beta- binomial sampling formula * DNA mixtures Subject RIV: IN - Informatics, Computer Science http://www.ejbi.eu/images/2011-1/Slovak_en.pdf
Optimal experimental designs for inverse quadratic regression models
Dette, Holger; Kiss, Christine
2007-01-01
In this paper optimal experimental designs for inverse quadratic regression models are determined. We consider two different parameterizations of the model and investigate local optimal designs with respect to the $c$-, $D$- and $E$-criteria, which reflect various aspects of the precision of the maximum likelihood estimator for the parameters in inverse quadratic regression models. In particular it is demonstrated that for a sufficiently large design space geometric allocation rules are optim...
Ion source mathematical modeling and optimization
International Nuclear Information System (INIS)
Egorov, N.V.; Vinogradova, E.M.
2004-01-01
Full text: The system of beam formation and control in the ion gun is under investigation. The calculation of the ion gun must take into account the field ion cathode influence on the beam focusing and transport conditions and the other electrodes influence both on the field cathode emission ability and on the characteristics of the formation and control systems. It's considered a mathematical model of the gun as a axially symmetrical ion-optical system which consists of a cathode, i.e. axially symmetrical thin tip on a flat substrate and a system of round apertures as the focusing electrodes. The tip shape may be various. The number of the apertures may be various too. The potential of the tip is equal to the substrate potential and is assumed to be zero without the loss of general character of the problem. A method is proposed for the determination the potential distribution. lt is calculated the distribution of potentials for whole region of the ion-optical system. All geometrical dimensions of the system and the electrodes' potentials are the parameters of this method. The problem of the optimal geometrical parameters and electrodes potentials is solved to have the required emission current. (author)
Directory of Open Access Journals (Sweden)
Rujia Wang
2017-01-01
Full Text Available Acoustical holography has been widely applied for noise sources location and sound field measurement. Performance of the microphones array directly determines the sound source recognition method. Therefore, research is very important to the performance of the microphone array, its array of applications, selection, and how to design instructive. In this paper, based on acoustic holography moving sound source identification theory, the optimization method is applied in design of the microphone array, we select the main side lobe ratio and the main lobe area as the optimization objective function and then put the optimization method use in the sound source identification based on holography, and finally we designed this paper to optimize microphone array and compare the original array of equally spaced array with optimization results; by analyzing the optimization results and objectives, we get that the array can be achieved which is optimized not only to reduce the microphone but also to change objective function results, while improving the far-field acoustic holography resolving effect. Validation experiments have showed that the optimization method is suitable for high speed trains sound source identification microphone array optimization.
Model based optimization of MSWC process control
Kessel, L.B.M. van; Leskens, M.
2002-01-01
Optimization of municipal solid waste combustion (MSWC), processes is an im portant issue doe to the ever-lasting need for emission reduction. more optimal use of raw materials and overall cost reduction. The key of the approach of TNO (Netherlands Orgaru sation for Applied Scientific Research) to
The Optimal Economic Order: the simplest model
J. Tinbergen (Jan)
1992-01-01
textabstractIn the last five years humanity has become faced with the problem of the optimal socioeconomic order more clearly than ever. After the confrontation of capitalism and socialism, which was the core of the Marxist thesis, the fact transpired that capitalism was not the optimal order. It
Mathematics of tsunami: modelling and identification
Krivorotko, Olga; Kabanikhin, Sergey
2015-04-01
Tsunami (long waves in the deep water) motion caused by underwater earthquakes is described by shallow water equations ( { ηtt = div (gH (x,y)-gradη), (x,y) ∈ Ω, t ∈ (0,T ); η|t=0 = q(x,y), ηt|t=0 = 0, (x,y) ∈ Ω. ( (1) Bottom relief H(x,y) characteristics and the initial perturbation data (a tsunami source q(x,y)) are required for the direct simulation of tsunamis. The main difficulty problem of tsunami modelling is a very big size of the computational domain (Ω = 500 × 1000 kilometres in space and about one hour computational time T for one meter of initial perturbation amplitude max|q|). The calculation of the function η(x,y,t) of three variables in Ω × (0,T) requires large computing resources. We construct a new algorithm to solve numerically the problem of determining the moving tsunami wave height S(x,y) which is based on kinematic-type approach and analytical representation of fundamental solution. Proposed algorithm of determining the function of two variables S(x,y) reduces the number of operations in 1.5 times than solving problem (1). If all functions does not depend on the variable y (one dimensional case), then the moving tsunami wave height satisfies of the well-known Airy-Green formula: S(x) = S(0)° --- 4H (0)/H (x). The problem of identification parameters of a tsunami source using additional measurements of a passing wave is called inverse tsunami problem. We investigate two different inverse problems of determining a tsunami source q(x,y) using two different additional data: Deep-ocean Assessment and Reporting of Tsunamis (DART) measurements and satellite altimeters wave-form images. These problems are severely ill-posed. The main idea consists of combination of two measured data to reconstruct the source parameters. We apply regularization techniques to control the degree of ill-posedness such as Fourier expansion, truncated singular value decomposition, numerical regularization. The algorithm of selecting the truncated number of
An Asymmetric Hysteresis Model and Parameter Identification Method for Piezoelectric Actuator
Directory of Open Access Journals (Sweden)
Haichen Qin
2014-01-01
Full Text Available Hysteresis behaviour degrades the positioning accuracy of PZT actuator for ultrahigh-precision positioning applications. In this paper, a corrected hysteresis model based on Bouc-Wen model for modelling the asymmetric hysteresis behaviour of PZT actuator is established by introducing an input bias φ and an asymmetric factor ΔΦ into the standard Bouc-Wen hysteresis model. A modified particle swarm optimization (MPSO algorithm is established and realized to identify and optimize the model parameters. Feasibility and effectiveness of MPSO are proved by experiment and numerical simulation. The research results show that the corrected hysteresis model can represent the asymmetric hysteresis behaviour of the PZT actuator more accurately than the noncorrected hysteresis model based on the Bouc-Wen model. The MPSO parameter identification method can effectively identify the parameters of the corrected and noncorrected hysteresis models. Some cases demonstrate the corrected hysteresis model and the MPSO parameter identification method can be used to model smart materials and structure systems with the asymmetric hysteresis behaviour.
Directory of Open Access Journals (Sweden)
Jun Chen
2014-01-01
Full Text Available In vibration-based structural health monitoring of existing large civil structures, it is difficult, sometimes even impossible, to measure the actual excitation applied to structures. Therefore, an identification method using output-only measurements is crucial for the practical application of structural health monitoring. This paper integrates the ant colony optimization (ACO algorithm into the framework of the complete inverse method to simultaneously identify unknown structural parameters and input time history using output-only measurements. The complete inverse method, which was previously suggested by the authors, converts physical or spatial information of the unknown input into the objective function of an optimization problem that can be solved by the ACO algorithm. ACO is a newly developed swarm computation method that has a very good performance in solving complex global continuous optimization problems. The principles and implementation procedure of the ACO algorithm are first introduced followed by an introduction of the framework of the complete inverse method. Construction of the objective function is then described in detail with an emphasis on the common situation wherein a limited number of actuators are installed on some key locations of the structure. Applicability and feasibility of the proposed method were validated by numerical examples and experimental results from a three-story building model.
CEAI: CCM-based email authorship identification model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2013-11-01
Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].
Optimal control of a delayed SLBS computer virus model
Chen, Lijuan; Hattaf, Khalid; Sun, Jitao
2015-06-01
In this paper, a delayed SLBS computer virus model is firstly proposed. To the best of our knowledge, this is the first time to discuss the optimal control of the SLBS model. By using the optimal control strategy, we present an optimal strategy to minimize the total number of the breakingout computers and the cost associated with toxication or detoxication. We show that an optimal control solution exists for the control problem. Some examples are presented to show the efficiency of this optimal control.
Visual prosthesis wireless energy transfer system optimal modeling.
Li, Xueping; Yang, Yuan; Gao, Yong
2014-01-16
Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling's more accuracy for the actual application. The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system's further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application.
Optimal velocity difference model for a car-following theory
International Nuclear Information System (INIS)
Peng, G.H.; Cai, X.H.; Liu, C.Q.; Cao, B.F.; Tuo, M.X.
2011-01-01
In this Letter, we present a new optimal velocity difference model for a car-following theory based on the full velocity difference model. The linear stability condition of the new model is obtained by using the linear stability theory. The unrealistically high deceleration does not appear in OVDM. Numerical simulation of traffic dynamics shows that the new model can avoid the disadvantage of negative velocity occurred at small sensitivity coefficient λ in full velocity difference model by adjusting the coefficient of the optimal velocity difference, which shows that collision can disappear in the improved model. -- Highlights: → A new optimal velocity difference car-following model is proposed. → The effects of the optimal velocity difference on the stability of traffic flow have been explored. → The starting and braking process were carried out through simulation. → The effects of the optimal velocity difference can avoid the disadvantage of negative velocity.
System Identification Theory Approach to Cohesive Sediment Transport Modelling
CHEN, HUIXIN
1997-01-01
Two aspects of the modelling sediment transport are investigated. One is the univariate time series modelling the current velocity dynamics. The other is the multivariate time series modelling the suspended sediment concentration dynamics. Cohesive sediment dynamics and numerical sediment transport model are reviewed and investigated. The system identification theory and time series analysis method are developed and applied to set up the time series model for current velocity a...
Integration and Optimization of Projectile Design Models
National Research Council Canada - National Science Library
Farina, Anthony P; Chassapis, Constantin; Chen, Yin M
2006-01-01
... an existing projectile, will be optimized with respect to performance requirement(s). Additionally, the design process will be simplified by the integration between predictive codes in this environment...
Optimization model for the design of distributed wastewater treatment networks
Directory of Open Access Journals (Sweden)
Ibrić Nidret
2012-01-01
Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.
Algorithms for Optimal Model Distributions in Adaptive Switching Control Schemes
Directory of Open Access Journals (Sweden)
Debarghya Ghosh
2016-03-01
Full Text Available Several multiple model adaptive control architectures have been proposed in the literature. Despite many advances in theory, the crucial question of how to synthesize the pairs model/controller in a structurally optimal way is to a large extent not addressed. In particular, it is not clear how to place the pairs model/controller is such a way that the properties of the switching algorithm (e.g., number of switches, learning transient, final performance are optimal with respect to some criteria. In this work, we focus on the so-called multi-model unfalsified adaptive supervisory switching control (MUASSC scheme; we define a suitable structural optimality criterion and develop algorithms for synthesizing the pairs model/controller in such a way that they are optimal with respect to the structural optimality criterion we defined. The peculiarity of the proposed optimality criterion and algorithms is that the optimization is carried out so as to optimize the entire behavior of the adaptive algorithm, i.e., both the learning transient and the steady-state response. A comparison is made with respect to the model distribution of the robust multiple model adaptive control (RMMAC, where the optimization considers only the steady-state ideal response and neglects any learning transient.
A model for optimizing the production of pharmaceutical products
Directory of Open Access Journals (Sweden)
Nevena Gospodinova
2017-05-01
Full Text Available The problem associated with the optimal production planning is especially relevant in modern industrial enterprises. The most commonly used optimality criteria in this context are: maximizing the total profit; minimizing the cost per unit of production; maximizing the capacity utilization; minimizing the total production costs. This article aims to explore the possibility for optimizing the production of pharmaceutical products through the construction of a mathematical model that can be viewed in two ways – as a single-product model and a multi-product model. As an optimality criterion it is set the minimization of the cost per unit of production for a given planning period. The author proposes an analytical method for solving the nonlinear optimization problem. An optimal production plan of Tylosin tartrate is found using the single-product model.
A merchant ship size optimization model
Choi, Ki-Chul
1983-01-01
Approved for public release; distribution in unlimited. This paper analyzes how a shipowner or charterer may determine the specification of optimal ship size for a given route with respect to certain market requirements . The theory of optimal ship size, a methodology for estimating scale economics, and the various factors affecting ship size are examined using a typical conventional cargo ship and bulk cargo carriers based on shipowners ' cost data. http://archi...
DEFF Research Database (Denmark)
Guedes, J.M.; Rodrigues, H.C.; Bendsøe, Martin P.
2003-01-01
This paper describes a computational model, based on inverse homogenization and topology design, for approximating energy bounds for two-phase composites under multiple load cases. The approach allows for the identification of possible single-scale cellular materials that give rise to the optimal...... bounds within this class of composites. A comparison of the computational results with the globally optimal bounds given via rank-N layered composites illustrates the behaviour for tension and shear load situations, as well as the importance of considering the shape of the basic unit cell as part...
Vortex Tube Modeling Using the System Identification Method
Energy Technology Data Exchange (ETDEWEB)
Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)
2017-05-15
In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.
Directory of Open Access Journals (Sweden)
Guang-zhou Chen
2015-01-01
Full Text Available Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.
Constrained optimization via simulation models for new product innovation
Pujowidianto, Nugroho A.
2017-11-01
We consider the problem of constrained optimization where the decision makers aim to optimize the primary performance measure while constraining the secondary performance measures. This paper provides a brief overview of stochastically constrained optimization via discrete event simulation. Most review papers tend to be methodology-based. This review attempts to be problem-based as decision makers may have already decided on the problem formulation. We consider constrained optimization models as there are usually constraints on secondary performance measures as trade-off in new product development. It starts by laying out different possible methods and the reasons using constrained optimization via simulation models. It is then followed by the review of different simulation optimization approach to address constrained optimization depending on the number of decision variables, the type of constraints, and the risk preferences of the decision makers in handling uncertainties.
Empty tracks optimization based on Z-Map model
Liu, Le; Yan, Guangrong; Wang, Zaijun; Zang, Genao
2017-12-01
For parts with many features, there are more empty tracks during machining. If these tracks are not optimized, the machining efficiency will be seriously affected. In this paper, the characteristics of the empty tracks are studied in detail. Combining with the existing optimization algorithm, a new tracks optimization method based on Z-Map model is proposed. In this method, the tool tracks are divided into the unit processing section, and then the Z-Map model simulation technique is used to analyze the order constraint between the unit segments. The empty stroke optimization problem is transformed into the TSP with sequential constraints, and then through the genetic algorithm solves the established TSP problem. This kind of optimization method can not only optimize the simple structural parts, but also optimize the complex structural parts, so as to effectively plan the empty tracks and greatly improve the processing efficiency.
Modeling and Optimization of Cement Raw Materials Blending Process
Directory of Open Access Journals (Sweden)
Xianhong Li
2012-01-01
Full Text Available This paper focuses on modelling and solving the ingredient ratio optimization problem in cement raw material blending process. A general nonlinear time-varying (G-NLTV model is established for cement raw material blending process via considering chemical composition, feed flow fluctuation, and various craft and production constraints. Different objective functions are presented to acquire optimal ingredient ratios under various production requirements. The ingredient ratio optimization problem is transformed into discrete-time single objective or multiple objectives rolling nonlinear constraint optimization problem. A framework of grid interior point method is presented to solve the rolling nonlinear constraint optimization problem. Based on MATLAB-GUI platform, the corresponding ingredient ratio software is devised to obtain optimal ingredient ratio. Finally, several numerical examples are presented to study and solve ingredient ratio optimization problems.
IDENTIFICATION OF SYSTEMS IN TERMS OF THE WIENER MODEL
The report presents briefly a nonlinear model originally proposed by the late Norbert Wiener for the characterization of general systems. Three...procedures are then offered for the identification of any given system in terms of the Wiener model. Finally, this report presents the results of a digital
A review on modeling, identification and servo control of robotic ...
African Journals Online (AJOL)
Robotic excavator is a hydraulic actuated 4 DOF manipulator mounted on a mobile chassis which implements automatic excavations. This article reviews modeling, identification, and low level control of the robotic excavator. First, modeling of the nonlinear hydraulic dynamics, coupling manipulator dynamics, and soil-tool ...
A Systematic Identification Method for Thermodynamic Property Modelling
DEFF Research Database (Denmark)
Ana Perederic, Olivia; Cunico, Larissa; Sarup, Bent
2017-01-01
In this work, a systematic identification method for thermodynamic property modelling is proposed. The aim of the method is to improve the quality of phase equilibria prediction by group contribution based property prediction models. The method is applied to lipid systems where the Original UNIFAC...
Surrogate Modeling for Geometry Optimization in Material Design
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas B.; Holzwarth, Natalie A.W.
2007-01-01
We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)......We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)...
Development of Optimized Strength Model of Lateritic Hollow Block ...
African Journals Online (AJOL)
The work is an investigation to develop and optimize a model of the compressive strength of lateritic hollow sandcrete block with mound soil inclusion. The study applies the Scheffe's optimization approach to obtain a mathematical model of the form f(xi1,xi2,xi3,,xi4), where xi are proportions of the concrete components, viz: ...
An Optimization Model Development for Laterized-Concrete Mix ...
African Journals Online (AJOL)
An Optimization Model Development for Laterized-Concrete Mix Proportioning in Building Constructions. ... Nigerian Journal of Technology ... In this study, a mathematical model was developed and was used to optimize the mix proportion that will produce the maximum strength of laterized concrete using Scheffe's simplex ...
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment
A tutorial on fundamental model structures for railway timetable optimization
DEFF Research Database (Denmark)
Harrod, Steven
2012-01-01
This guide explains the role of railway timetables relative to all other railway scheduling activities, and then presents four fundamental timetable formulations suitable for optimization. Timetabling models may be classified according to whether they explicitly model the track structure, and whe......This guide explains the role of railway timetables relative to all other railway scheduling activities, and then presents four fundamental timetable formulations suitable for optimization. Timetabling models may be classified according to whether they explicitly model the track structure...
Directory of Open Access Journals (Sweden)
Zhaohua Gong
2012-01-01
Full Text Available Mathematical modeling and parameter estimation are critical steps in the optimization of biotechnological processes. In the 1,3-propanediol (1,3-PD production by glycerol fermentation process under anaerobic conditions, 3-hydroxypropionaldehyde (3-HPA accumulation would arouse an irreversible cessation of the fermentation process. Considering 3-HPA inhibitions to cells growth and to activities of enzymes, we propose a novel mathematical model to describe glycerol continuous cultures. Some properties of the above model are discussed. On the basis of the concentrations of extracellular substances, a parameter identification model is established to determine the kinetic parameters in the presented system. Through the penalty function technique combined with an extension of the state space method, an improved genetic algorithm is then constructed to solve the parameter identification model. An illustrative numerical example shows the appropriateness of the proposed model and the validity of optimization algorithm. Since it is difficult to measure the concentrations of intracellular substances, a quantitative robustness analysis method is given to infer whether the model is plausible for the intracellular substances. Numerical results show that the proposed model is of good robustness.
Shape optimization in biomimetics by homogenization modelling
International Nuclear Information System (INIS)
Hoppe, Ronald H.W.; Petrova, Svetozara I.
2003-08-01
Optimal shape design of microstructured materials has recently attracted a great deal of attention in material science. The shape and the topology of the microstructure have a significant impact on the macroscopic properties. The present work is devoted to the shape optimization of new biomorphic microcellular ceramics produced from natural wood by biotemplating. We are interested in finding the best material-and-shape combination in order to achieve the optimal prespecified performance of the composite material. The computation of the effective material properties is carried out using the homogenization method. Adaptive mesh-refinement technique based on the computation of recovered stresses is applied in the microstructure to find the homogenized elasticity coefficients. Numerical results show the reliability of the implemented a posteriori error estimator. (author)
Following an Optimal Batch Bioreactor Operations Model
DEFF Research Database (Denmark)
Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.
2012-01-01
-batch reactor for biomass production is studied using a differential geometry approach. The maximization problem is solved by handling both the optimal filling policy and substrate concentration in the inlet stream. In order to follow the OBBOM, a master–slave synchronization is used. The OBBOM is considered...... as the master system which includes the optimal cultivation trajectory for the feed flow rate and the substrate concentration. The “real” bioreactor, the one with unknown dynamics and perturbations, is considered as the slave system. Finally, the controller is designed such that the real bioreactor...
Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions
Morelli, Eugene A.
2013-01-01
A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.
CREATION OF OPTIMIZATION MODEL OF STEAM BOILER RECUPERATIVE AIR HEATER
Directory of Open Access Journals (Sweden)
N. B. Carnickiy
2006-01-01
Full Text Available The paper proposes to use a mathematical modeling as one of the ways intended to improve quality of recuperative air heater design (RAH without significant additional costs, connected with the change of design materials or fuel type. The described conceptual mathematical AHP optimization model of RAH consists of optimized and constant parameters, technical limitations and optimality criteria.The paper considers a methodology for search of design and regime parameters of an air heater which is based on the methods of multi-criteria optimization. Conclusions for expediency of the given approach usage are made in the paper.
Directory of Open Access Journals (Sweden)
Filimonova Ekaterina Aleksandrovna
2012-10-01
The author suggests splitting the aforementioned parameters into the two groups, namely, natural parameters and value-related parameters that are introduced to assess the costs of development, transportation, construction and operation of a structure, as well as the costs of its potential failure. The author proposes a new improved methodology for the identification of the above parameters that ensures optimal solutions to non-linear objective functions accompanied by non-linear restrictions that are critical to the design of reinforced concrete structures. Any structural failure may be interpreted as the bounce of a random process associated with the surplus bearing capacity into the negative domain. Monte Carlo numerical methods make it possible to assess these bounces into the unacc eptable domain.
Deletion analysis of susy-sl promoter for the identification of optimal promoter sequence
International Nuclear Information System (INIS)
Bacha, S.; Khatoon, A.; Asif, M.; Bshir, A.
2015-01-01
The promoter region of sucrose synthase (susy-Sl) was identified and isolated from tomato. The 5? deletion analysis was carried out for the identification of minimum optimal promoter. Transgenic lines of Arabidopsis thaliana were developed by floral dip method incorporating various promoter deletion cassettes controlling GUS reporter gene. GUS assay of transgenic tissues indicated that full length susy-Sl promoter and its deletion mutants were constitutively expressed in vegetative and floral tissues of A. thaliana. The expression was observed in roots, shoots and flowers of A. thaliana. Analysis of 5? deletion series of susy-Sl promoter showed that a minimum of 679 bp fragment of the promoter was sufficient to drive expression of GUS reporter gene in the major tissues of transgenic A. thaliana. (author)
Identification and communication of uncertainties of phenomenological models in PSA
International Nuclear Information System (INIS)
Pulkkinen, U.; Simola, K.
2001-11-01
This report aims at presenting a view upon uncertainty analysis of phenomenological models with an emphasis on the identification and documentation of various types of uncertainties and assumptions in the modelling of the phenomena. In an uncertainty analysis, it is essential to include and document all unclear issues, in order to obtain a maximal coverage of unresolved issues. This holds independently on their nature or type of the issues. The classification of uncertainties is needed in the decomposition of the problem and it helps in the identification of means for uncertainty reduction. Further, an enhanced documentation serves to evaluate the applicability of the results to various risk-informed applications. (au)
Optimization and emergence in marine ecosystem models
DEFF Research Database (Denmark)
Mariani, Patrizio; Visser, Andre
2010-01-01
Ingestion rates and mortality rates of zooplankton are dynamic parameters reflecting a behavioural trade-off between encounters with food and predators. An evolutionarily consistent behaviour is that which optimizes the trade-off in terms of the fitness conferred to an individual. We argue that i...
Optimal Combining Data for Improving Ocean Modeling
2014-09-30
optimal is searched separately. We have fused ADCP and CTD data collected in NAVOCEANO shipboard survey based upon the California Cooperative Fisheries ... reconstructed principal circulation patterns. A physically important result is that many of retrieved eddies turned out to live in rather shallow water, not...deeper than ~ 150 m. 3 Figure1. Reconstruction of circulation from NAVOCEANO data
Stochastic dynamic programming model for optimal resource ...
Indian Academy of Sciences (India)
M Bhuvaneswari
2018-04-11
Apr 11, 2018 ... containers, doctors, nurses, cash and stocks. Similarly, the uncertainty may have different characterizations in these applications. An approximate stochastic dynamic programming (SDP) [3] allows nodes with a number of possible actions with clear strategies for devising an effective decision on optimal ...
Optimizing Classroom Acoustics Using Computer Model Studies.
Reich, Rebecca; Bradley, John
1998-01-01
Investigates conditions relating to the maximum useful-to-detrimental sound ratios present in classrooms and determining the optimum conditions for speech intelligibility. Reveals that speech intelligibility is more strongly influenced by ambient noise levels and that the optimal location for sound absorbing material is on a classroom's upper…
Optimization using surrogate models - by the space mapping technique
DEFF Research Database (Denmark)
Søndergaard, Jacob
2003-01-01
Surrogate modelling and optimization techniques are intended for engineering design in the case where an expensive physical model is involved. This thesis provides a literature overview of the field of surrogate modelling and optimization. The space mapping technique is one such method for constr......Surrogate modelling and optimization techniques are intended for engineering design in the case where an expensive physical model is involved. This thesis provides a literature overview of the field of surrogate modelling and optimization. The space mapping technique is one such method...... conditions are satisfied. So hybrid methods, combining the space mapping technique with classical optimization methods, should be used if convergence to high accuracy is wanted. Approximation abilities of the space mapping surrogate are compared with those of a Taylor model of the expensive model. The space...... mapping surrogate has a lower approximation error for long steps. For short steps, however, the Taylor model of the expensive model is best, due to exact interpolation at the model origin. Five algorithms for space mapping optimization are presented and the numerical performance is evaluated. Three...
Structural robust optimization design based on convex model
Directory of Open Access Journals (Sweden)
Xuyong Chen
Full Text Available There exist a great amount of uncertain factors in actual engineering. In order to involve these uncertain factors in analytical model, they have been expressed as the convex variables. In addition, the convex model was further classified into the hyper-ellipsoidal model and the interval model. After pointing out the intrinsic difference between these two kinds of models, the principle for applying which one of the models within the analysis has been indicated according to the available testing points. After standardizing the convex variables, the difference and relation between these two models for the optimization and solution process have been presented. With the analysis mentality available from the hyper-ellipsoidal model, the basic method about the robust optimization for the interval model was emphasized. After classification of the interval variables within the optimization process, the characteristics of the robust optimization were highlighted with different constraint conditions. Using the target-performance-based analytical scheme, the algorithm, the solution step and the convergence criteria for the robust optimization have been also presented with only one reliability index. Numerical examples and engineering problems were used to demonstrate the effectiveness and correctness of the proposed approach. Keywords: Robust optimization, Non-probabilistic reliability, Interval model, Hyper-ellipsoidal model, Probabilistic index
Global Nonlinear Model Identification with Multivariate Splines
De Visser, C.C.
2011-01-01
At present, model based control systems play an essential role in many aspects of modern society. Application areas of model based control systems range from food processing to medical imaging, and from process control in oil refineries to the flight control systems of modern aircraft. Central to a
The Talent Search Model of Gifted Identification
Assouline, Susan G.; Lupkowski-Shoplik, Ann
2012-01-01
The Talent Search model, founded at Johns Hopkins University by Dr. Julian C. Stanley, is fundamentally an above-level testing program. This simplistic description belies the enduring impact that the Talent Search model has had on the lives of hundreds of thousands of gifted students as well as their parents and teachers. In this article, we…
Identification du modele mathematique d'un helicoptere reduit
Honvo, Japhet
The remote-controlled helicopter remains an interesting topic for research in flight control. This kind of machine, easy to deploy due to their small size, is an ideal candidate to test multiple flight control algorithms. To better understand the dynamics of flight of this vehicle, it is important to have a mathematical model. This thesis follows the logic of obtaining a mathematical model for a stationary hovering helicopter. This thesis aims to provide a testbench for the identification of a mathematical model of a small helicopter and for the application of different flight control laws. First, a review on the identification theory is introduced. The methods presented are applicable to multivariable systems. A particular focus is on the identification of state models. The theory concludes with the presentation of algorithms used in the Matlab/Simulink software. Second, a mathematical model of the helicopter is developed. As part of our research, hypotheses to reduce the model are presented. This model is the basis for determining the right identification methods. The mathematical model provides a guideline for specifying the various components of the test bench. The thesis continues with the presentation of the avionics used in the project. The instrumentation is presented in two parts: the hardware and the software. The acquisition of real-time flight parameters is also presented. Finally, the use of the test bench is detailed for the ground tests and for the flight tests. These tests are designed to collect the data necessary for the deployment of various identification techniques. The thesis concludes with comments on significants results and suggestions of prospects for improving the test bench.
Multipurpose optimization models for high level waste vitrification
International Nuclear Information System (INIS)
Hoza, M.
1994-08-01
Optimal Waste Loading (OWL) models have been developed as multipurpose tools for high-level waste studies for the Tank Waste Remediation Program at Hanford. Using nonlinear programming techniques, these models maximize the waste loading of the vitrified waste and optimize the glass formers composition such that the glass produced has the appropriate properties within the melter, and the resultant vitrified waste form meets the requirements for disposal. The OWL model can be used for a single waste stream or for blended streams. The models can determine optimal continuous blends or optimal discrete blends of a number of different wastes. The OWL models have been used to identify the most restrictive constraints, to evaluate prospective waste pretreatment methods, to formulate and evaluate blending strategies, and to determine the impacts of variability in the wastes. The OWL models will be used to aid in the design of frits and the maximize the waste in the glass for High-Level Waste (HLW) vitrification
Portfolio optimization for index tracking modelling in Malaysia stock market
Siew, Lam Weng; Jaaman, Saiful Hafizah; Ismail, Hamizun
2016-06-01
Index tracking is an investment strategy in portfolio management which aims to construct an optimal portfolio to generate similar mean return with the stock market index mean return without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using the optimization model which adopts regression approach in tracking the benchmark stock market index return. In this study, the data consists of weekly price of stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2013. The results of this study show that the optimal portfolio is able to track FBMKLCI Index at minimum tracking error of 1.0027% with 0.0290% excess mean return over the mean return of FBMKLCI Index. The significance of this study is to construct the optimal portfolio using optimization model which adopts regression approach in tracking the stock market index without purchasing all index components.
Multiscale modeling and topology optimization of poroelastic actuators
DEFF Research Database (Denmark)
Andreasen, Casper Schousboe; Sigmund, Ole
2012-01-01
This paper presents a method for design of optimized poroelastic materials which under internal pressurization turn into actuators for application in, for example, linear motors. The actuators are modeled in a two-scale fluid–structure interaction approach. The fluid saturated material...... microstructure is optimized using topology optimization in order to achieve a better macroscopic performance quantified by vertical or torsional deflections. Constraints are introduced to ensure a certain deflection/extension ratio of the actuator....
Group Elevator Peak Scheduling Based on Robust Optimization Model
ZHANG, J.; ZONG, Q.
2013-01-01
Scheduling of Elevator Group Control System (EGCS) is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization) method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is re...
Modeling and Optimization of Materials and Structures
Karama, Moussa; Borouchaki, Houman; Cherouat, Abel; El Hami, Abdelkhalak
2015-01-01
Preface : The current economic and ecological context requires different industries towards the optimization of structures. One of the most striking examples is the soaring price of oil, which launched the major aircraft manufacturers Airbus and Boeing in a race to relief structures, to reduce their consumption. Therefore, the need of competitive products, both in terms of reliability, performance or operating cost, is pervasive and growing, aviation and elsewhere. Thus, work on the material ...
Parameter identification in ODE models with oscillatory dynamics: a Fourier regularization approach
Chiara D'Autilia, Maria; Sgura, Ivonne; Bozzini, Benedetto
2017-12-01
In this paper we consider a parameter identification problem (PIP) for data oscillating in time, that can be described in terms of the dynamics of some ordinary differential equation (ODE) model, resulting in an optimization problem constrained by the ODEs. In problems with this type of data structure, simple application of the direct method of control theory (discretize-then-optimize) yields a least-squares cost function exhibiting multiple ‘low’ minima. Since in this situation any optimization algorithm is liable to fail in the approximation of a good solution, here we propose a Fourier regularization approach that is able to identify an iso-frequency manifold {{ S}} of codimension-one in the parameter space \
An optimization strategy for a biokinetic model of inhaled radionuclides
International Nuclear Information System (INIS)
Shyr, L.J.; Griffith, W.C.; Boecker, B.B.
1991-01-01
Models for material disposition and dosimetry involve predictions of the biokinetics of the material among compartments representing organs and tissues in the body. Because of a lack of human data for most toxicants, many of the basic data are derived by modeling the results obtained from studies using laboratory animals. Such a biomathematical model is usually developed by adjusting the model parameters to make the model predictions match the measured retention and excretion data visually. The fitting process can be very time-consuming for a complicated model, and visual model selections may be subjective and easily biased by the scale or the data used. Due to the development of computerized optimization methods, manual fitting could benefit from an automated process. However, for a complicated model, an automated process without an optimization strategy will not be efficient, and may not produce fruitful results. In this paper, procedures for, and implementation of, an optimization strategy for a complicated mathematical model is demonstrated by optimizing a biokinetic model for 144Ce in fused aluminosilicate particles inhaled by beagle dogs. The optimized results using SimuSolv were compared to manual fitting results obtained previously using the model simulation software GASP. Also, statistical criteria provided by SimuSolv, such as likelihood function values, were used to help or verify visual model selections
Optimization and modeling of cellulase protein from Trichoderma ...
African Journals Online (AJOL)
Research surface methodology was suggested for optimization of process conditions of cellulase biosynthesis. Logistic kinetic model was the best model for the mixed substrates. A conceptual Artificial Neural Network (ANN) model was well incorporated in the fermentative production of cellulase. By adopting these models ...
Optimal Scaling of Interaction Effects in Generalized Linear Models
van Rosmalen, Joost; Koning, Alex J.; Groenen, Patrick J. F.
2009-01-01
Multiplicative interaction models, such as Goodman's (1981) RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are suitable only for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of…
Optimal vaccination and treatment of an epidemic network model
Energy Technology Data Exchange (ETDEWEB)
Chen, Lijuan [Department of Mathematics, Tongji University, Shanghai 200092 (China); College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002 (China); Sun, Jitao, E-mail: sunjt@sh163.net [Department of Mathematics, Tongji University, Shanghai 200092 (China)
2014-08-22
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1–5 are presented to show the global stability and the efficiency of this optimal control. - Highlights: • Propose an optimally controlled SIRS epidemic model on heterogeneous networks. • Obtain criteria of global stability of the disease-free equilibrium and the endemic equilibrium. • Investigate existence of optimal control for the control problem. • The results be illustrated by some numerical simulations.
Optimal treatment interruptions control of TB transmission model
Nainggolan, Jonner; Suparwati, Titik; Kawuwung, Westy B.
2018-03-01
A tuberculosis model which incorporates treatment interruptions of infectives is established. Optimal control of individuals infected with active TB is given in the model. It is obtained that the control reproduction numbers is smaller than the reproduction number, this means treatment controls could optimize the decrease in the spread of active TB. For this model, controls on treatment of infection individuals to reduce the actively infected individual populations, by application the Pontryagins Maximum Principle for optimal control. The result further emphasized the importance of controlling disease relapse in reducing the number of actively infected and treatment interruptions individuals with tuberculosis.
Asymptotic Optimality of Estimating Function Estimator for CHARN Model
Directory of Open Access Journals (Sweden)
Tomoyuki Amano
2012-01-01
Full Text Available CHARN model is a famous and important model in the finance, which includes many financial time series models and can be assumed as the return processes of assets. One of the most fundamental estimators for financial time series models is the conditional least squares (CL estimator. However, recently, it was shown that the optimal estimating function estimator (G estimator is better than CL estimator for some time series models in the sense of efficiency. In this paper, we examine efficiencies of CL and G estimators for CHARN model and derive the condition that G estimator is asymptotically optimal.
Pavement maintenance optimization model using Markov Decision Processes
Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.
2017-09-01
This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.
Holz, Jasmin A; Boerwinkel, David F; Meijer, Sybren L; Visser, Mike; van Leeuwen, Ton G; Aalders, Maurice C G; Bergman, Jacques J G H M
2013-12-01
Fluorescence spectroscopy has the potential to detect early cellular changes in Barrett's oesophagus before these become visible. As the technique is based on varying concentrations of intrinsic fluorophores, each with its own optimal excitation wavelength, it is important to assess the optimal excitation wavelength(s) for identification of premalignant lesions in patients with Barrett's oesophagus. The endoscopic spectroscopy system used contained five (ultra)violet light sources (λexc=369-416 nm) to generate autofluorescence during routine endoscopic surveillance. Autofluorescence spectroscopy was followed by a biopsy for histological assessment and spectra correlation. Three intensity ratios (r1, r2, r3) were calculated by dividing the area, A, under the spectral curve of selected emission wavelength ranges for each spectrum generated by each excitation wavelength λexc as follows (Equation is included in full-text article.). Double intensity ratios were calculated using two excitation wavelengths. Fifty-eight tissue areas from 22 patients were used for autofluorescence spectra analysis. Excitation with 395, 405 or 410 nm showed a significant (P≤0.0006) differentiation between intestinal metaplasia and grouped high-grade dysplasia/early carcinoma for intensity ratios r2 and r3. A sensitivity of 80.0% and specificity of 89.5% with an area under the ROC curve of 0.85 was achieved using 395 nm excitation and intensity ratio r3. Double excitation showed no additional value over single excitation. The combination of 395 nm excitation and intensity ratio r3 showed optimal conditions to discriminate nondysplastic from early neoplasia in Barrett's oesophagus.
Identification of linear error-models with projected dynamical systems
Czech Academy of Sciences Publication Activity Database
Krejčí, Pavel; Kuhnen, K.
2004-01-01
Roč. 10, č. 1 (2004), s. 59-91 ISSN 1387-3954 Keywords : identification * error models * projected dynamical systems Subject RIV: BA - General Mathematics Impact factor: 0.292, year: 2004 http://www.informaworld.com/smpp/content~db=all~content=a713682517
Optimization of a new mathematical model for bacterial growth
The objective of this research is to optimize a new mathematical equation as a primary model to describe the growth of bacteria under constant temperature conditions. An optimization algorithm was used in combination with a numerical (Runge-Kutta) method to solve the differential form of the new gr...
CEAI: CCM based Email Authorship Identification Model
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah
2013-01-01
authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron data set, while 89.5% accuracy has been achieved on authors' constructed real email data set. The results on Enron data set have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1...
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 ...
System identification application using Hammerstein model
Indian Academy of Sciences (India)
Saban Ozer
because of its advanced theoretical background [3–5, 10]. However, many systems in real life have nonlinear beha- ... To describe a polynomial non-linear system with memory, the Volterra series expansion has been the ... suppression and adaptive noise suppression [19]. 2.3 Hammerstein model. Many systems can be ...
Markowitz portfolio optimization model employing fuzzy measure
Ramli, Suhailywati; Jaaman, Saiful Hafizah
2017-04-01
Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.
Directory of Open Access Journals (Sweden)
Fei Wang
2017-07-01
Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.
A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting
International Nuclear Information System (INIS)
Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge
2014-01-01
Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily
LENUS (Irish Health Repository)
Moore, Jason
2009-02-27
The early identification of kidney allografts at risk of later dysfunction has implications for clinical practice. Donor quality scoring systems (preoperative) and measures of early allograft function (first week postoperative) have previously shown practical utility. This study aimed to determine the optimal parameter(s) (preoperative and postoperative) with greatest predictive power for the development of subsequent allograft dysfunction.
Thruster Modelling for Underwater Vehicle Using System Identification Method
Directory of Open Access Journals (Sweden)
Mohd Shahrieel Mohd Aras
2013-05-01
Full Text Available Abstract This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.
Box & Jenkins Model Identification:A Comparison of Methodologies
Directory of Open Access Journals (Sweden)
Maria Augusta Soares Machado
2012-12-01
Full Text Available This paper focuses on a presentation of a comparison of a neuro-fuzzy back propagation network and Forecast automatic model Identification to identify automatically Box & Jenkins non seasonal models.Recently some combinations of neural networks and fuzzy logic technologies have being used to deal with uncertain and subjective problems. It is concluded on the basis of the obtained results that this type of approach is very powerful to be used.
Analysis and optimization of a camber morphing wing model
Directory of Open Access Journals (Sweden)
Bing Li
2016-09-01
Full Text Available This article proposes a camber morphing wing model that can continuously change its camber. A mathematical model is proposed and a kinematic simulation is performed to verify the wing’s ability to change camber. An aerodynamic model is used to test its aerodynamic characteristics. Some important aerodynamic analyses are performed. A comparative analysis is conducted to explore the relationships between aerodynamic parameters, the rotation angle of the trailing edge, and the angle of attack. An improved artificial fish swarm optimization algorithm is proposed, referred to as the weighted adaptive artificial fish-swarm with embedded Hooke–Jeeves search method. Some comparison tests are used to test the performance of the improved optimization algorithm. Finally, the proposed optimization algorithm is used to optimize the proposed camber morphing wing model.
Deterministic operations research models and methods in linear optimization
Rader, David J
2013-01-01
Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear
RF building block modeling: optimization and synthesis
Cheng, W.
2012-01-01
For circuit designers it is desirable to have relatively simple RF circuit models that do give decent estimation accuracy and provide sufficient understanding of circuits. Chapter 2 in this thesis shows a general weak nonlinearity model that meets these demands. Using a method that is related to
Modelling in Optimal Inspection and Repair
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Rackwitz, R.; Faber, Michael Havbro
1991-01-01
of this model with a preposterior analysis from statistical decision theory is discussed. It is deseribed how information obtained by an inspection can be used in the repair decision process and to update the inspection plan. Stochastic models for inspection, measurement and repair actions are presented...
Optimal model distributions in supervisory adaptive control
Ghosh, D.; Baldi, S.
2017-01-01
Several classes of multi-model adaptive control schemes have been proposed in literature: instead of one single parameter-varying controller, in this adaptive methodology multiple fixed-parameter controllers for different operating regimes (i.e. different models) are utilised. Despite advances in
Constrained optimization with a continuous Hopfield-Lagrange model
J.H. van den Berg (Jan); J.C. Bioch (Cor)
1993-01-01
textabstractIn this paper, a generalized Hopfield model with continuous neurons using Lagrange multipliers, originally introduced Wacholder, Han &Mann [1989], is thoroughly analysed. We have termed the model the Hopfield-Lagrange model. It can be used to resolve constrained optimization problems. In
Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?
Valente, Giordano; Pitto, Lorenzo; Testi, Debora; Seth, Ajay; Delp, Scott L; Stagni, Rita; Viceconti, Marco; Taddei, Fulvia
2014-01-01
Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be
Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?
Directory of Open Access Journals (Sweden)
Giordano Valente
Full Text Available Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312 across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force
Vector-model-supported approach in prostate plan optimization
International Nuclear Information System (INIS)
Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Lehman, Margot; Pryor, David; Chan, Lawrence Wing Chi
2017-01-01
Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration
Reduced Complexity Volterra Models for Nonlinear System Identification
Directory of Open Access Journals (Sweden)
Hacıoğlu Rıfat
2001-01-01
Full Text Available A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.
Directory of Open Access Journals (Sweden)
Huiguo Chen
2017-01-01
Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.
Optimization models in a transition economy
Sergienko, Ivan V; Koshlai, Ludmilla
2014-01-01
This book opens new avenues in understanding mathematical models within the context of a transition economy. The exposition lays out the methods for combining different mathematical structures and tools to effectively build the next model that will accurately reflect real world economic processes. Mathematical modeling of weather phenomena allows us to forecast certain essential weather parameters without any possibility of changing them. By contrast, modeling of transition economies gives us the freedom to not only predict changes in important indexes of all types of economies, but also to influence them more effectively in the desired direction. Simply put: any economy, including a transitional one, can be controlled. This book is useful to anyone who wants to increase profits within their business, or improve the quality of their family life and the economic area they live in. It is beneficial for undergraduate and graduate students specializing in the fields of Economic Informatics, Economic Cybernetic...
Optimizing mouse models for precision cancer prevention.
Le Magnen, Clémentine; Dutta, Aditya; Abate-Shen, Cory
2016-03-01
As cancer has become increasingly prevalent, cancer prevention research has evolved towards placing a greater emphasis on reducing cancer deaths and minimizing the adverse consequences of having cancer. 'Precision cancer prevention' takes into account the collaboration of intrinsic and extrinsic factors in influencing cancer incidence and aggressiveness in the context of the individual, as well as recognizing that such knowledge can improve early detection and enable more accurate discrimination of cancerous lesions. However, mouse models, and particularly genetically engineered mouse (GEM) models, have yet to be fully integrated into prevention research. In this Opinion article, we discuss opportunities and challenges for precision mouse modelling, including the essential criteria of mouse models for prevention research, representative success stories and opportunities for more refined analyses in future studies.
Optimization of experimental human leukemia models (review
Directory of Open Access Journals (Sweden)
D. D. Pankov
2012-01-01
Full Text Available Actual problem of assessing immunotherapy prospects including antigenpecific cell therapy using animal models was covered in this review.Describe the various groups of currently existing animal models and methods of their creating – from different immunodeficient mice to severalvariants of tumor cells engraftment in them. The review addresses the possibility of tumor stem cells studying using mouse models for the leukemia treatment with adoptive cell therapy including WT1. Also issues of human leukemia cells migration and proliferation in a mice withdifferent immunodeficiency degree are discussed. To assess the potential immunotherapy efficacy comparison of immunodeficient mouse model with clinical situation in oncology patients after chemotherapy is proposed.
Directory of Open Access Journals (Sweden)
Kottner R.
2013-12-01
Full Text Available Adhesively bonded joints can be numerically simulated using the cohesive crack model. The critical strain energy release rate and the critical opening displacement are the parameters which must be known when cohesive elements in MSC.Marc software are used. In this work, the parameters of two industrial adhesives Hunstman Araldite 2021 and Gurit Spabond 345 for bonding of epoxy composites are identified. Double Cantilever Beam (DCB and End Notched Flexure (ENF test data were used for the identification. The critical opening displacements were identified using an optimization algorithm where the tests and their numerical simulations were compared.
COBRA-SFS modifications and cask model optimization
International Nuclear Information System (INIS)
Rector, D.R.; Michener, T.E.
1989-01-01
Spent-fuel storage systems are complex systems and developing a computational model for one can be a difficult task. The COBRA-SFS computer code provides many capabilities for modeling the details of these systems, but these capabilities can also allow users to specify a more complex model than necessary. This report provides important guidance to users that dramatically reduces the size of the model while maintaining the accuracy of the calculation. A series of model optimization studies was performed, based on the TN-24P spent-fuel storage cask, to determine the optimal model geometry. Expanded modeling capabilities of the code are also described. These include adding fluid shear stress terms and a detailed plenum model. The mathematical models for each code modification are described, along with the associated verification results. 22 refs., 107 figs., 7 tabs
Optimizing the calculation grid for atmospheric dispersion modelling
International Nuclear Information System (INIS)
Van Thielen, S.; Turcanu, C.; Camps, J.; Keppens, R.
2015-01-01
This paper presents three approaches to find optimized grids for atmospheric dispersion measurements and calculations in emergency planning. This can be useful for deriving optimal positions for mobile monitoring stations, or help to reduce discretization errors and improve recommendations. Indeed, threshold-based recommendations or conclusions may differ strongly on the shape and size of the grid on which atmospheric dispersion measurements or calculations of pollutants are based. Therefore, relatively sparse grids that retain as much information as possible, are required. The grid optimization procedure proposed here is first demonstrated with a simple Gaussian plume model as adopted in atmospheric dispersion calculations, which provides fast calculations. The optimized grids are compared to the Noodplan grid, currently used for emergency planning in Belgium, and to the exact solution. We then demonstrate how it can be used in more realistic dispersion models. - Highlights: • Grid points for atmospheric dispersion calculations are optimized. • Using heuristics the optimization problem results into different grid shapes. • Comparison between optimized models and the Noodplan grid is performed
Airfoil Shape Optimization based on Surrogate Model
Mukesh, R.; Lingadurai, K.; Selvakumar, U.
2018-02-01
Engineering design problems always require enormous amount of real-time experiments and computational simulations in order to assess and ensure the design objectives of the problems subject to various constraints. In most of the cases, the computational resources and time required per simulation are large. In certain cases like sensitivity analysis, design optimisation etc where thousands and millions of simulations have to be carried out, it leads to have a life time of difficulty for designers. Nowadays approximation models, otherwise called as surrogate models (SM), are more widely employed in order to reduce the requirement of computational resources and time in analysing various engineering systems. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. The primary intention of this work is to employ the k-fold cross validation approach to study and evaluate the influence of various theoretical variogram models on the accuracy of the surrogate model construction. Ordinary Kriging and design of experiments (DOE) approaches are used to construct the SMs by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed.
Robust and fast nonlinear optimization of diffusion MRI microstructure models.
Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A
2017-07-15
Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of
Optimal identification of semi-rigid domains in macromolecules from molecular dynamics simulation.
Directory of Open Access Journals (Sweden)
Stefan Bernhard
Full Text Available Biological function relies on the fact that biomolecules can switch between different conformations and aggregation states. Such transitions involve a rearrangement of parts of the biomolecules involved that act as dynamic domains. The reliable identification of such domains is thus a key problem in biophysics. In this work we present a method to identify semi-rigid domains based on dynamical data that can be obtained from molecular dynamics simulations or experiments. To this end the average inter-atomic distance-deviations are computed. The resulting matrix is then clustered by a constrained quadratic optimization problem. The reliability and performance of the method are demonstrated for two artificial peptides. Furthermore we correlate the mechanical properties with biological malfunction in three variants of amyloidogenic transthyretin protein, where the method reveals that a pathological mutation destabilizes the natural dimer structure of the protein. Finally the method is used to identify functional domains of the GroEL-GroES chaperone, thus illustrating the efficiency of the method for large biomolecular machines.
A niche width model of optimal specialization
Bruggeman, J.P.; Ó Nualláin, Breanndán
2000-01-01
Niche width theory, a part of organizational ecology, predicts whether “specialist” or “generalist” forms of organizations have higher “fitness,” in a continually changing environment. To this end, niche width theory uses a mathematical model borrowed from biology. In this paper, we first loosen the
Optimization Model for Reducing Emissions of Greenhouse ...
The EPA Vehicle Greenhouse Gas (VGHG) model is used to apply various technologies to a defined set of vehicles in order to meet a specified GHG emission target, and to then calculate the costs and benefits of doing so. To facilitate its analysis of the costs and benefits of the control of GHG emissions from cars and trucks.
A model for optimal constrained adaptive testing
van der Linden, Willem J.; Reese, Lynda M.
1997-01-01
A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum
Optimal Allocation in Stratified Randomized Response Model
Directory of Open Access Journals (Sweden)
Javid Shabbir
2005-07-01
Full Text Available A Warner (1965 randomized response model based on stratification is used to determine the allocation of samples. Both linear and log-linear cost functions are discussed under uni and double stratification. It observed that by using a log-linear cost function, one can get better allocations.
A model for optimal constrained adaptive testing
van der Linden, Willem J.; Reese, Lynda M.
2001-01-01
A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum
Optimal Experimental Design for Model Discrimination
Myung, Jay I.; Pitt, Mark A.
2009-01-01
Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it…
Reduced order modeling in topology optimization of vibroacoustic problems
DEFF Research Database (Denmark)
Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Brunskog, Jonas
2017-01-01
There is an interest in introducing topology optimization techniques in the design process of structural-acoustic systems. In topology optimization, the design space must be finely meshed in order to obtain an accurate design, which results in large numbers of degrees of freedom when designing...... or size optimization in large vibroacoustic models; however, new challenges are encountered when dealing with topology optimization. Since a design parameter per element is considered, the total number of design variables becomes very large; this poses a challenge to most existing pMOR techniques, which...... suffer from the curse of dimensionality. Moreover, the fact that the nature of the elements changes throughout the optimization (material to void or material to air) makes it more difficult to create a global basis that is accurate throughout the whole design space. In this work, these challenges...
Recursive Model Identification for the Evaluation of Baroreflex Sensitivity.
Le Rolle, Virginie; Beuchée, Alain; Praud, Jean-Paul; Samson, Nathalie; Pladys, Patrick; Hernández, Alfredo I
2016-12-01
A method for the recursive identification of physiological models of the cardiovascular baroreflex is proposed and applied to the time-varying analysis of vagal and sympathetic activities. The proposed method was evaluated with data from five newborn lambs, which were acquired during injection of vasodilator and vasoconstrictors and the results show a close match between experimental and simulated signals. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge and the obtained estimators of vagal and sympathetic activities were compared to traditional markers associated with baroreflex sensitivity. High correlations were observed between traditional markers and model-based indices.
Optimizing Biorefinery Design and Operations via Linear Programming Models
Energy Technology Data Exchange (ETDEWEB)
Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric
2017-03-28
The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for
Validation of the measurement model concept for error structure identification
International Nuclear Information System (INIS)
Shukla, Pavan K.; Orazem, Mark E.; Crisalle, Oscar D.
2004-01-01
The development of different forms of measurement models for impedance has allowed examination of key assumptions on which the use of such models to assess error structure are based. The stochastic error structures obtained using the transfer-function and Voigt measurement models were identical, even when non-stationary phenomena caused some of the data to be inconsistent with the Kramers-Kronig relations. The suitability of the measurement model for assessment of consistency with the Kramers-Kronig relations, however, was found to be more sensitive to the confidence interval for the parameter estimates than to the number of parameters in the model. A tighter confidence interval was obtained for Voigt measurement model, which made the Voigt measurement model a more sensitive tool for identification of inconsistencies with the Kramers-Kronig relations
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.
Aggressive symbolic model identification in 13 year-old youths
Directory of Open Access Journals (Sweden)
Miguel A. Vidal
2009-01-01
Full Text Available Although a great amount of research has been carried out about the effects of media on the audience, few studies deal with the process that determines why the viewers identify with a specific symbolic model instead of choosing any other. In this descriptive study we try to highlight similarity identification, focusing on aggressive model identification. A sample of 203 participants, both male and female, aged 13, and with a high socioeconomic level viewed different films sequences. They were asked to answer to a questionnaire both before and after watching the clip. This questionnaire included an adjective list about the traits that best defined themselves, their favorite characters, and characters they didn’t like. Results show a clear correspondence between the participants’ self-perceived traits and those perceived for the main characters in the film. Self-perceived traits were opposed to those perceived in the main characters opponents.
A corticothalamic circuit model for sound identification in complex scenes.
Directory of Open Access Journals (Sweden)
Gonzalo H Otazu
Full Text Available The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal.
System Identification, Environmental Modelling, and Control System Design
Garnier, Hugues
2012-01-01
System Identification, Environmetric Modelling, and Control Systems Design is dedicated to Professor Peter Young on the occasion of his seventieth birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume is comprised of a collection of contributions by leading experts in system identification, time-series analysis, environmetric modelling and control system design – modern research in topics that reflect important areas of interest in Professor Young’s research career. Recent theoretical developments in and relevant applications of these areas are explored treating the various subjects broadly and in depth. The authoritative and up-to-date research presented here will be of interest to academic researcher in control and disciplines related to environmental research, particularly those to with water systems. The tutorial style in which many of the contributions are composed also makes the book suitable as ...
Ben Taheur, Fadia; Fdhila, Kais; Elabed, Hamouda; Bouguerra, Amel; Kouidhi, Bochra; Bakhrouf, Amina; Chaieb, Kamel
2016-04-01
Three bacterial strains (TE1, TD3 and FB2) were isolated from date palm (degla), pistachio and barley. The presence of nitrate reductase (narG) and nitrite reductase (nirS and nirK) genes in the selected strains was detected by PCR technique. Molecular identification based on 16S rDNA sequencing method was applied to identify positive strains. In addition, the D-optimal mixture experimental design was used to optimize the optimal formulation of probiotic bacteria for denitrification process. Strains harboring denitrification genes were identified as: TE1, Agrococcus sp LN828197; TD3, Cronobacter sakazakii LN828198 and FB2, Pedicoccus pentosaceus LN828199. PCR results revealed that all strains carried the nirS gene. However only C. sakazakii LN828198 and Agrococcus sp LN828197 harbored the nirK and the narG genes respectively. Moreover, the studied bacteria were able to form biofilm on abiotic surfaces with different degree. Process optimization showed that the most significant reduction of nitrate was 100% with 14.98% of COD consumption and 5.57 mg/l nitrite accumulation. Meanwhile, the response values were optimized and showed that the most optimal combination was 78.79% of C. sakazakii LN828198 (curve value), 21.21% of P. pentosaceus LN828199 (curve value) and absence (0%) of Agrococcus sp LN828197 (curve value). Copyright © 2016 Elsevier Ltd. All rights reserved.
Learning optimal quantum models is NP-hard
Stark, Cyril J.
2018-02-01
Physical modeling translates measured data into a physical model. Physical modeling is a major objective in physics and is generally regarded as a creative process. How good are computers at solving this task? Here, we show that in the absence of physical heuristics, the inference of optimal quantum models cannot be computed efficiently (unless P=NP ). This result illuminates rigorous limits to the extent to which computers can be used to further our understanding of nature.
Neural networks for nonlinear dynamic system modelling and identification
Chen, S.; Billings, S. A.
1992-01-01
Many real-world systems exhibit complex non-linear characteristics and cannot be treated satisfactorily using linear systems theory. A neural network which has the ability to learn sophisticated non-linear relationships provides an ideal means of modelling complicated non-linear systems. This paper addresses the issues related to the identification of non-linear discrete-time dynamic systems using neural networks..........
Space engineering modeling and optimization with case studies
Pintér, János
2016-01-01
This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...
Model-Based Optimization for Flapping Foil Actuation
Izraelevitz, Jacob; Triantafyllou, Michael
2014-11-01
Flapping foil actuation in nature, such as wings and flippers, often consist of highly complex joint kinematics which present an impossibly large parameter space for designing bioinspired mechanisms. Designers therefore often build a simplified model to limit the parameter space so an optimum motion trajectory can be experimentally found, or attempt to replicate exactly the joint geometry and kinematics of a suitable organism whose behavior is assumed to be optimal. We present a compromise: using a simple local fluids model to guide the design of optimized trajectories through a succession of experimental trials, even when the parameter space is too large to effectively search. As an example, we illustrate an optimization routine capable of designing asymmetric flapping trajectories for a large aspect-ratio pitching and heaving foil, with the added degree of freedom of allowing the foil to move parallel to flow. We then present PIV flow visualizations of the optimized trajectories.
Optimizing the calculation grid for atmospheric dispersion modelling.
Van Thielen, S; Turcanu, C; Camps, J; Keppens, R
2015-04-01
This paper presents three approaches to find optimized grids for atmospheric dispersion measurements and calculations in emergency planning. This can be useful for deriving optimal positions for mobile monitoring stations, or help to reduce discretization errors and improve recommendations. Indeed, threshold-based recommendations or conclusions may differ strongly on the shape and size of the grid on which atmospheric dispersion measurements or calculations of pollutants are based. Therefore, relatively sparse grids that retain as much information as possible, are required. The grid optimization procedure proposed here is first demonstrated with a simple Gaussian plume model as adopted in atmospheric dispersion calculations, which provides fast calculations. The optimized grids are compared to the Noodplan grid, currently used for emergency planning in Belgium, and to the exact solution. We then demonstrate how it can be used in more realistic dispersion models. Copyright © 2015 Elsevier Ltd. All rights reserved.
Integer programming model for optimizing bus timetable using genetic algorithm
Wihartiko, F. D.; Buono, A.; Silalahi, B. P.
2017-01-01
Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.
Optimal foraging in marine ecosystem models: selectivity, profitability and switching
DEFF Research Database (Denmark)
Visser, Andre W.; Fiksen, Ø.
2013-01-01
ecological mechanics and evolutionary logic as a solution to diet selection in ecosystem models. When a predator can consume a range of prey items it has to choose which foraging mode to use, which prey to ignore and which ones to pursue, and animals are known to be particularly skilled in adapting...... to the preference functions commonly used in models today. Indeed, depending on prey class resolution, optimal foraging can yield feeding rates that are considerably different from the ‘switching functions’ often applied in marine ecosystem models. Dietary inclusion is dictated by two optimality choices: 1...... by letting predators maximize energy intake or more properly, some measure of fitness where predation risk and cost are also included. An optimal foraging or fitness maximizing approach will give marine ecosystem models a sound principle to determine trophic interactions...
Time dependent optimal switching controls in online selling models
Energy Technology Data Exchange (ETDEWEB)
Bradonjic, Milan [Los Alamos National Laboratory; Cohen, Albert [MICHIGAN STATE UNIV
2010-01-01
We present a method to incorporate dishonesty in online selling via a stochastic optimal control problem. In our framework, the seller wishes to maximize her average wealth level W at a fixed time T of her choosing. The corresponding Hamilton-Jacobi-Bellmann (HJB) equation is analyzed for a basic case. For more general models, the admissible control set is restricted to a jump process that switches between extreme values. We propose a new approach, where the optimal control problem is reduced to a multivariable optimization problem.
Neutron density optimal control of A-1 reactor analoque model
International Nuclear Information System (INIS)
Grof, V.
1975-01-01
Two applications are described of the optimal control of a reactor analog model. Both cases consider the control of neutron density. Control loops containing the on-line controlled process, the reactor of the first Czechoslovak nuclear power plant A-1, are simulated on an analog computer. Two versions of the optimal control algorithm are derived using modern control theory (Pontryagin's maximum principle, the calculus of variations, and Kalman's estimation theory), the minimum time performance index, and the quadratic performance index. The results of the optimal control analysis are compared with the A-1 reactor conventional control. (author)
Optimal control of information epidemics modeled as Maki Thompson rumors
Kandhway, Kundan; Kuri, Joy
2014-12-01
We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns.
Parameter identification and optimization of slide guide joint of CNC machine tools
Zhou, S.; Sun, B. B.
2017-11-01
The joint surface has an important influence on the performance of CNC machine tools. In order to identify the dynamic parameters of slide guide joint, the parametric finite element model of the joint is established and optimum design method is used based on the finite element simulation and modal test. Then the mode that has the most influence on the dynamics of slip joint is found through harmonic response analysis. Take the frequency of this mode as objective, the sensitivity analysis of the stiffness of each joint surface is carried out using Latin Hypercube Sampling and Monte Carlo Simulation. The result shows that the vertical stiffness of slip joint surface constituted by the bed and the slide plate has the most obvious influence on the structure. Therefore, this stiffness is taken as the optimization variable and the optimal value is obtained through studying the relationship between structural dynamic performance and stiffness. Take the stiffness values before and after optimization into the FEM of machine tool, and it is found that the dynamic performance of the machine tool is improved.
Metodology of identification parameters of models control objects of automatic trailing system
Directory of Open Access Journals (Sweden)
I.V. Zimchuk
2017-04-01
Full Text Available The determining factor for the successful solution of the problem of synthesis of optimal control systems of different processes are adequacy of mathematical model of control object. In practice, the options can differ from the objects taken priori, causing a need to clarification of them. In this context, the article presents the results of the development and application of methods parameters identification of mathematical models of control object of automatic trailing system. The stated problem in the article is solved provided that control object is fully controlled and observed, and a differential equation of control object is known a priori. The coefficients of this equation to be determined. Identifying quality criterion is to minimize the integral value of squared error of identification. The method is based on a description of the dynamics of the object in space state. Equation of identification synthesized using the vector-matrix representation of model. This equation describes the interconnection of coefficients of matrix state and control with inputs and outputs of object. The initial data for calculation are the results of experimental investigation of the reaction of phase coordinates of control object at a typical input signal. The process of calculating the model parameters is reduced to solving the system of equations of the first order each. Application the above approach is illustrated in the example identification of coefficients transfer function of control object first order. Results of digital simulation are presented, they are confirming the justice of set out mathematical calculations. The approach enables to do the identification of models of one-dimensional and multidimensional objects and does not require a large amount of calculation for its implementation. The order of identified model is limited capabilities of measurement phase coordinates of corresponding control object. The practical significance of the work is
Models and Methods for Free Material Optimization
DEFF Research Database (Denmark)
Weldeyesus, Alemseged Gebrehiwot
conditions for physical attainability, in the context that, it has to be symmetric and positive semidefinite. FMO problems have been studied for the last two decades in many articles that led to the development of a wide range of models, methods, and theories. As the design variables in FMO are the local...... programs. The method has successfully obtained solutions to large-scale classical FMO problems of simultaneous analysis and design, nested and dual formulations. The second goal is to extend the method and the FMO problem formulations to general laminated shell structures. The thesis additionally addresses...
Sparse optimization for inverse problems in atmospheric modelling
Czech Academy of Sciences Publication Activity Database
Adam, Lukáš; Branda, Martin
2016-01-01
Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf
Mathematical Model for the Optimization of Compressive Strength of ...
African Journals Online (AJOL)
These mathematical models are adopted for optimization of strength of sandcrete block in compression. With the model, any desired strength of sandcrete block, given any mix proportions, is easily evaluated. Basic Language is used in the development of the computer program. The maximum compressive strength ...
Mathematical programming model for the optimization of nutritional ...
African Journals Online (AJOL)
The use of a mathematical programming model for determining optimal nutritional strategy for a dairy cow is described. Mixed Integer Programming (MIP) may be used to fit curvilinear functions, such as the changes in the nutrient requirements of the cow, into a standard mathematical programme. The model determines the.
Rare earth-doped integrated glass components: modeling and optimization
DEFF Research Database (Denmark)
Lumholt, Ole; Bjarklev, Anders Overgaard; Rasmussen, Thomas
1995-01-01
For the integrated optic erbium-doped phosphate silica-amplifier, a comprehensive model is presented which includes high-concentration dissipative ion-ion interactions. Based on actual waveguide parameters, the model is seen to reproduce measured gains closely. A rigorous design optimization is p...
Optimal Model-Based Control in HVAC Systems
DEFF Research Database (Denmark)
Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik
2008-01-01
This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...
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
Optimization of multi-model ensemble forecasting of typhoon waves
Directory of Open Access Journals (Sweden)
Shun-qi Pan
2016-01-01
Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.
Optimal dimensioning model of water distribution systems | Gomes ...
African Journals Online (AJOL)
This study is aimed at developing a pipe-sizing model for a water distribution system. The optimal solution minimises the system's total cost, which comprises the hydraulic network capital cost, plus the capitalised cost of pumping energy. The developed model, called Lenhsnet, may also be used for economical design when ...
Optimization methods and silicon solar cell numerical models
Girardini, K.; Jacobsen, S. E.
1986-01-01
An optimization algorithm for use with numerical silicon solar cell models was developed. By coupling an optimization algorithm with a solar cell model, it is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junction depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm was developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAP1D). SCAP1D uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the performance of a solar cell. A major obstacle is that the numerical methods used in SCAP1D require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the values associated with the maximum efficiency. This problem was alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution.
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Krosche, M.; Stekolschikov, K. [Scandpower Petroleum Technology GmbH, Hamburg (Germany); Fahimuddin, A. [Technische Univ. Braunschweig (Germany)
2007-09-13
History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results are compared to a single-objective optimization method. (orig.)
Modeling to Optimize Hospital Evacuation Planning in EMS Systems.
Bish, Douglas R; Tarhini, Hussein; Amara, Roel; Zoraster, Richard; Bosson, Nichole; Gausche-Hill, Marianne
2017-01-01
To develop optimal hospital evacuation plans within a large urban EMS system using a novel evacuation planning model and a realistic hospital evacuation scenario, and to illustrate the ways in which a decision support model may be useful in evacuation planning. An optimization model was used to produce detailed evacuation plans given the number and type of patients in the evacuating hospital, resource levels (teams to move patients, vehicles, and beds at other hospitals), and evacuation rules. Optimal evacuation plans under various resource levels and rules were developed and high-level metrics were calculated, including evacuation duration and the utilization of resources. Using this model we were able to determine the limiting resources and demonstrate how strategically augmenting the resource levels can improve the performance of the evacuation plan. The model allowed the planner to test various evacuation conditions and resource levels to demonstrate the effect on performance of the evacuation plan. We present a hospital evacuation planning analysis for a hospital in a large urban EMS system using an optimization model. This model can be used by EMS administrators and medical directors to guide planning decisions and provide a better understanding of various resource allocation decisions and rules that govern a hospital evacuation.
Sitting biomechanics, part II: optimal car driver's seat and optimal driver's spinal model.
Harrison, D D; Harrison, S O; Croft, A C; Harrison, D E; Troyanovich, S J
2000-01-01
Driving has been associated with signs and symptoms caused by vibrations. Sitting causes the pelvis to rotate backwards and the lumbar lordosis to reduce. Lumbar support and armrests reduce disc pressure and electromyographically recorded values. However, the ideal driver's seat and an optimal seated spinal model have not been described. To determine an optimal automobile seat and an ideal spinal model of a driver. Information was obtained from peer-reviewed scientific journals and texts, automotive engineering reports, and the National Library of Medicine. Driving predisposes vehicle operators to low-back pain and degeneration. The optimal seat would have an adjustable seat back incline of 100 degrees from horizontal, a changeable depth of seat back to front edge of seat bottom, adjustable height, an adjustable seat bottom incline, firm (dense) foam in the seat bottom cushion, horizontally and vertically adjustable lumbar support, adjustable bilateral arm rests, adjustable head restraint with lordosis pad, seat shock absorbers to dampen frequencies in the 1 to 20 Hz range, and linear front-back travel of the seat enabling drivers of all sizes to reach the pedals. The lumbar support should be pulsating in depth to reduce static load. The seat back should be damped to reduce rebounding of the torso in rear-end impacts. The optimal driver's spinal model would be the average Harrison model in a 10 degrees posterior inclining seat back angle.
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.
A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction
Danandeh Mehr, Ali; Kahya, Ercan
2017-06-01
Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.
A dynamic optimization model for solid waste recycling.
Anghinolfi, Davide; Paolucci, Massimo; Robba, Michela; Taramasso, Angela Celeste
2013-02-01
Recycling is an important part of waste management (that includes different kinds of issues: environmental, technological, economic, legislative, social, etc.). Differently from many works in literature, this paper is focused on recycling management and on the dynamic optimization of materials collection. The developed dynamic decision model is characterized by state variables, corresponding to the quantity of waste in each bin per each day, and control variables determining the quantity of material that is collected in the area each day and the routes for collecting vehicles. The objective function minimizes the sum of costs minus benefits. The developed decision model is integrated in a GIS-based Decision Support System (DSS). A case study related to the Cogoleto municipality is presented to show the effectiveness of the proposed model. From optimal results, it has been found that the net benefits of the optimized collection are about 2.5 times greater than the estimated current policy. Copyright © 2012 Elsevier Ltd. All rights reserved.
Optimal control for micro-algae on a raceway model.
Hurst, Todd; Rehbock, Volker
2018-01-01
We apply numerical optimal control methods to an existing algae growth model with the aim to determine the best performance of the model under known conditions using a variety of decision variables. We transform the system of differential algebraic equations in the existing model to a system of ordinary differential equations which introduces dynamics for average light intensity and chlorophyll. In addition, we allow for variable nitrogen concentration of the inflow as well as variable initial nitrogen concentration of the raceway. Our main focus is on optimizing of the production of lipids. We calculate both open and closed loop optimal controllers and test their robustness. Finally, we also consider raceway depth as a decision variable. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:107-119, 2018. © 2017 American Institute of Chemical Engineers.
Characterization, Modeling, and Optimization of Light-Emitting Diode Systems
DEFF Research Database (Denmark)
Thorseth, Anders
This thesis explores, characterization, modeling, and optimization of light-emitting diodes (LED) for general illumination. An automated setup has been developed for spectral radiometric characterization of LED components with precise control of the settings of forward current and operating...... temperature. The automated setup has been used to characterize commercial LED components with respect to multiple settings. It is shown that the droop in quantum efficiency can be approximated by a simple parabolic function. The investigated models of the spectral power distributions (SPD) from LEDs...... comparing the chromaticity of the measured SPD with tted models, the deviation is found to be larger than the lower limit of human color perception. A method has been developed to optimize multicolored cluster LED systems with respect to light quality, using multi objective optimization. The results...
A stochastic discrete optimization model for designing container terminal facilities
Zukhruf, Febri; Frazila, Russ Bona; Burhani, Jzolanda Tsavalista
2017-11-01
As uncertainty essentially affect the total transportation cost, it remains important in the container terminal that incorporates several modes and transshipments process. This paper then presents a stochastic discrete optimization model for designing the container terminal, which involves the decision of facilities improvement action. The container terminal operation model is constructed by accounting the variation of demand and facilities performance. In addition, for illustrating the conflicting issue that practically raises in the terminal operation, the model also takes into account the possible increment delay of facilities due to the increasing number of equipment, especially the container truck. Those variations expectantly reflect the uncertainty issue in the container terminal operation. A Monte Carlo simulation is invoked to propagate the variations by following the observed distribution. The problem is constructed within the framework of the combinatorial optimization problem for investigating the optimal decision of facilities improvement. A new variant of glow-worm swarm optimization (GSO) is thus proposed for solving the optimization, which is rarely explored in the transportation field. The model applicability is tested by considering the actual characteristics of the container terminal.
Modeling and energy efficiency optimization of belt conveyors
International Nuclear Information System (INIS)
Zhang, Shirong; Xia, Xiaohua
2011-01-01
Highlights: → We take optimization approach to improve operation efficiency of belt conveyors. → An analytical energy model, originating from ISO 5048, is proposed. → Then an off-line and an on-line parameter estimation schemes are investigated. → In a case study, six optimization problems are formulated with solutions in simulation. - Abstract: The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment and operation levels. Specifically, variable speed control, an equipment level intervention, is recommended to improve operation efficiency of belt conveyors. However, the current implementations mostly focus on lower level control loops without operational considerations at the system level. This paper intends to take a model based optimization approach to improve the efficiency of belt conveyors at the operational level. An analytical energy model, originating from ISO 5048, is firstly proposed, which lumps all the parameters into four coefficients. Subsequently, both an off-line and an on-line parameter estimation schemes are applied to identify the new energy model, respectively. Simulation results are presented for the estimates of the four coefficients. Finally, optimization is done to achieve the best operation efficiency of belt conveyors under various constraints. Six optimization problems of a typical belt conveyor system are formulated, respectively, with solutions in simulation for a case study.
Model optimizing production structure of coal enterprise and its application
Energy Technology Data Exchange (ETDEWEB)
Zhang, Q. [China University of Mining and Technology, Xuzhou (China). College of Business and Adminsitration
2000-05-01
The models which optimize the product structure and maximize the profit of a coal enterprise were established. These models are mainly the increment and total analysis model for determining the optimum coal preparation scheme, the model of management risk analysis, and the linear plan model for deciding the optimum product structure under the condition of a given technology. They have been applied to coal enterprise in practice and obtained obvious economic benefit. This shows that the models are scientifically reasonable and efficient, and are valuable in designing preparation plant and in planning production under the condition of market economy. 2 refs., 2 tabs.
A Framework for Cloudy Model Optimization and Database Storage
Calvén, Emilia; Helton, Andrew; Sankrit, Ravi
2018-01-01
We present a framework for producing Cloudy photoionization models of the nebular emission from novae ejecta and storing a subset of the results in SQL database format for later usage. The database can be searched for models best fitting observed spectral line ratios. Additionally, the framework includes an optimization feature that can be used in tandem with the database to search for and improve on models by creating new Cloudy models while, varying the parameters. The database search and optimization can be used to explore the structures of nebulae by deriving their properties from the best-fit models. The goal is to provide the community with a large database of Cloudy photoionization models, generated from parameters reflecting conditions within novae ejecta, that can be easily fitted to observed spectral lines; either by directly accessing the database using the framework code or by usage of a website specifically made for this purpose.
Detailed modelling and optimal design of membrane separation systems
Marriott, J. I.
2001-01-01
The search for alternatives to traditional energy intensive separation methods such as distillation has led to the introduction of processes based on membranes. In this research, the use of detailed mathematical models for the optimal design of membrane systems is investigated. Mathematical models of hollow-fibre and spiral-wound membrane modules are presented in this thesis. The models are developed from rigorous mass, momentum and energy balances and can be used to describ...
Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model
Tahriri, Farzad; Zawiah Md Dawal, Siti; Taha, Zahari
2014-01-01
It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary go...
Group Elevator Peak Scheduling Based on Robust Optimization Model
Directory of Open Access Journals (Sweden)
ZHANG, J.
2013-08-01
Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.
Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm
Directory of Open Access Journals (Sweden)
Wen-Jing Shen
2017-03-01
Full Text Available This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB electric model by using a combination of particle swarm optimization (PSO and Levenberg-Marquardt (LM algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the identified parameter values are in good agreement with those from the experiments at different discharge/charge rates.
A neural network model of lateralization during letter identification.
Shevtsova, N; Reggia, J A
1999-03-01
The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.
Optimization of morphing flaps based on fluid structure interaction modeling
DEFF Research Database (Denmark)
Barlas, Athanasios; Akay, Busra
2018-01-01
This article describes the design optimization of morphing trailing edge flaps for wind turbines with ‘smart blades’. A high fidelity Fluid Structure Interaction (FSI) simulation framework is utilized, comprised of 2D Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) models....... A coupled aero-structural simulation of a 10% chordwise length morphing trailing edge flap for a 4 MW wind turbine rotor is carried out and response surfaces are produced with respect to the flap internal geometry design parameters for the design conditions. Surrogate model based optimization is applied...
Identification of fast-steering mirror based on chicken swarm optimization algorithm
Ren, Wei; Deng, Chao; Zhang, Chao; Mao, Yao
2017-06-01
According to the transfer function identification method of fast steering mirror exists problems which estimate the initial value is complicated in the process of using, put forward using chicken swarm algorithm to simplify the identification operation, reducing the workload of identification. chicken swarm algorithm is a meta heuristic intelligent population algorithm, which shows global convergence is efficient in the identification experiment, and the convergence speed is fast. The convergence precision is also high. Especially there are many parameters are needed to identificate in the transfer function without considering the parameters estimation problem. Therefore, compared with the traditional identification methods, the proposed approach is more convenient, and greatly achieves the intelligent design of fast steering mirror control system in enginerring application, shorten time of controller designed.
GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS
International Nuclear Information System (INIS)
Rogers, Adam; Fiege, Jason D.
2011-01-01
Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image χ 2 and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest χ 2 is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.
2015-01-01
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Electrochemical model based charge optimization for lithium-ion batteries
Pramanik, Sourav; Anwar, Sohel
2016-05-01
In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.
A model for optimization of process integration investments under uncertainty
International Nuclear Information System (INIS)
Svensson, Elin; Stroemberg, Ann-Brith; Patriksson, Michael
2011-01-01
The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. -- Highlights: → Stochastic programming approach to long-term planning of process integration investments. → Extensive mathematical model formulation. → Multi-stage investment decisions and scenario-based modelling of uncertain energy prices. → Results illustrate how investments made now affect later investment and operation opportunities. → Approach for evaluation of robustness with respect to variations in probability distribution.
A Survey of Modelling and Identification of Quadrotor Robot
Directory of Open Access Journals (Sweden)
Xiaodong Zhang
2014-01-01
Full Text Available A quadrotor is a rotorcraft capable of hover, forward flight, and VTOL and is emerging as a fundamental research and application platform at present with flexibility, adaptability, and ease of construction. Since a quadrotor is basically considered an unstable system with the characteristics of dynamics such as being intensively nonlinear, multivariable, strongly coupled, and underactuated, a precise and practical model is critical to control the vehicle which seems to be simple to operate. As a rotorcraft, the dynamics of a quadrotor is mainly dominated by the complicated aerodynamic effects of the rotors. This paper gives a tutorial of the platform configuration, methodology of modeling, comprehensive nonlinear model, the aerodynamic effects, and model identification for a quadrotor.
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Modeling of biological intelligence for SCM system optimization.
Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang
2012-01-01
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
Modeling of Biological Intelligence for SCM System Optimization
Directory of Open Access Journals (Sweden)
Shengyong Chen
2012-01-01
Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
Modeling of Biological Intelligence for SCM System Optimization
Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang
2012-01-01
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724
Optlang: An algebraic modeling language for mathematical optimization
DEFF Research Database (Denmark)
Jensen, Kristian; Cardoso, Joao; Sonnenschein, Nikolaus
2016-01-01
Optlang is a Python package implementing a modeling language for solving mathematical optimization problems, i.e., maximizing or minimizing an objective function over a set of variables subject to a number of constraints. It provides a common native Python interface to a series of optimization...... tools, so different solver backends can be used and changed in a transparent way. Optlang’s object-oriented API takes advantage of the symbolic math library SymPy (Team 2016) to allow objective functions and constraints to be easily formulated algebraically from symbolic expressions of variables....... Optlang targets scientists who can thus focus on formulating optimization problems based on mathematical equations derived from domain knowledge. Solver interfaces can be added by subclassing the four main classes of the optlang API (Variable, Constraint, Objective, and Model) and implementing...
A method for model identification and parameter estimation
International Nuclear Information System (INIS)
Bambach, M; Heinkenschloss, M; Herty, M
2013-01-01
We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)
Study on Identification of Material Model Parameters from Compact Tension Test on Concrete Specimens
Hokes, Filip; Kral, Petr; Husek, Martin; Kala, Jiri
2017-10-01
Identification of a concrete material model parameters using optimization is based on a calculation of a difference between experimentally measured and numerically obtained data. Measure of the difference can be formulated via root mean squared error that is often used for determination of accuracy of a mathematical model in the field of meteorology or demography. The quality of the identified parameters is, however, determined not only by right choice of an objective function but also by the source experimental data. One of the possible way is to use load-displacement curves from three-point bending tests that were performed on concrete specimens. This option shows the significance of modulus of elasticity, tensile strength and specific fracture energy. Another possible option is to use experimental data from compact tension test. It is clear that the response in the second type of test is also dependent on the above mentioned material parameters. The question is whether the parameters identified within three-point bending test and within compact tension test will reach the same values. The presented article brings the numerical study of inverse identification of material model parameters from experimental data measured during compact tension tests. The article also presents utilization of the modified sensitivity analysis that calculates the sensitivity of the material model parameters for different parts of loading curve. The main goal of the article is to describe the process of inverse identification of parameters for plasticity-based material model of concrete and prepare data for future comparison with identified values of the material model parameters from different type of fracture tests.
Bolodurina, I. P.; Parfenov, D. I.
2017-10-01
The goal of our investigation is optimization of network work in virtual data center. The advantage of modern infrastructure virtualization lies in the possibility to use software-defined networks. However, the existing optimization of algorithmic solutions does not take into account specific features working with multiple classes of virtual network functions. The current paper describes models characterizing the basic structures of object of virtual data center. They including: a level distribution model of software-defined infrastructure virtual data center, a generalized model of a virtual network function, a neural network model of the identification of virtual network functions. We also developed an efficient algorithm for the optimization technology of containerization of virtual network functions in virtual data center. We propose an efficient algorithm for placing virtual network functions. In our investigation we also generalize the well renowned heuristic and deterministic algorithms of Karmakar-Karp.
Identification of walking human model using agent-based modelling
Shahabpoor, Erfan; Pavic, Aleksandar; Racic, Vitomir
2018-03-01
The interaction of walking people with large vibrating structures, such as footbridges and floors, in the vertical direction is an important yet challenging phenomenon to describe mathematically. Several different models have been proposed in the literature to simulate interaction of stationary people with vibrating structures. However, the research on moving (walking) human models, explicitly identified for vibration serviceability assessment of civil structures, is still sparse. In this study, the results of a comprehensive set of FRF-based modal tests were used, in which, over a hundred test subjects walked in different group sizes and walking patterns on a test structure. An agent-based model was used to simulate discrete traffic-structure interactions. The occupied structure modal parameters found in tests were used to identify the parameters of the walking individual's single-degree-of-freedom (SDOF) mass-spring-damper model using 'reverse engineering' methodology. The analysis of the results suggested that the normal distribution with the average of μ = 2.85Hz and standard deviation of σ = 0.34Hz can describe human SDOF model natural frequency. Similarly, the normal distribution with μ = 0.295 and σ = 0.047 can describe the human model damping ratio. Compared to the previous studies, the agent-based modelling methodology proposed in this paper offers significant flexibility in simulating multi-pedestrian walking traffics, external forces and simulating different mechanisms of human-structure and human-environment interaction at the same time.
Pareto-Optimal Model Selection via SPRINT-Race.
Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2018-02-01
In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Lentjes, M.; Dickmann, K.; Meijer, J.
2007-01-01
Linear correlation analysis may be used as a technique for the identification of samples with a very similar chemical composition by laser induced breakdown spectroscopy. The spectrum of the “unknown” sample is correlated with a library of reference spectra. The probability of identification by
Stochastic Modelling and Optimization of Complex Infrastructure Systems
DEFF Research Database (Denmark)
Thoft-Christensen, Palle
In this paper it is shown that recent progress in stochastic modelling and optimization in combination with advanced computer systems has now made it possible to improve the design and the maintenance strategies for infrastructure systems. The paper concentrates on highway networks and single large...
Optimal Tax Reduction by Depreciation : A Stochastic Model
Berg, M.; De Waegenaere, A.M.B.; Wielhouwer, J.L.
1996-01-01
This paper focuses on the choice of a depreciation method, when trying to minimize the expected value of the present value of future tax payments.In a quite general model that allows for stochastic future cash- ows and a tax structure with tax brackets, we determine the optimal choice between the
A mechanistic model for rational design of optimal cellulase mixtures.
Levine, Seth E; Fox, Jerome M; Clark, Douglas S; Blanch, Harvey W
2011-11-01
A model-based framework is described that permits the optimal composition of cellulase enzyme mixtures to be found for lignocellulose hydrolysis. The rates of hydrolysis are shown to be dependent on the nature of the substrate. For bacterial microcrystalline cellulose (BMCC) hydrolyzed by a ternary cellulase mixture of EG2, CBHI, and CBHII, the optimal predicted mixture was 1:0:1 EG2:CBHI:CBHII at 24 h and 1:1:0 at 72 h, at loadings of 10 mg enzyme per g substrate. The model was validated with measurements of soluble cello-oligosaccharide production from BMCC during both single enzyme and mixed enzyme hydrolysis. Three-dimensional diagrams illustrating cellulose conversion were developed for mixtures of EG2, CBHI, CBHII acting on BMCC and predicted for other substrates with a range of substrate properties. Model predictions agreed well with experimental values of conversion after 24 h for a variety of enzyme mixtures. The predicted mixture performances for substrates with varying properties demonstrated the effects of initial degree of polymerization (DP) and surface area on the performance of cellulase mixtures. For substrates with a higher initial DP, endoglucanase enzymes accounted for a larger fraction of the optimal mixture. Substrates with low surface areas showed significantly reduced hydrolysis rates regardless of mixture composition. These insights, along with the quantitative predictions, demonstrate the utility of this model-based framework for optimizing cellulase mixtures. Copyright © 2011 Wiley Periodicals, Inc.
Modeling and Optimization in USEF-compliant Hierarchical Energy Markets
Nguyen, Dinh Bao; Scherpen, Jacquelien M.A.; ter Haar, B.; Bliek, Frits
2016-01-01
This paper presents a new model and optimization method for balancing in the Universal Smart Energy Framework. We address the problem of minimizing the error between the forecasted and the actual load in the power system that arise from the uncertainties of renewable energy production. The algorithm
The Optimal Portfolio Selection Model under g-Expectation
Directory of Open Access Journals (Sweden)
Li Li
2014-01-01
complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.
Real-Time Optimization for Economic Model Predictive Control
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca
2012-01-01
In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...
Discover for Yourself: An Optimal Control Model in Insect Colonies
Winkel, Brian
2013-01-01
We describe the enlightening path of self-discovery afforded to the teacher of undergraduate mathematics. This is demonstrated as we find and develop background material on an application of optimal control theory to model the evolutionary strategy of an insect colony to produce the maximum number of queen or reproducer insects in the colony at…
Energy Technology Data Exchange (ETDEWEB)
Janot, A
2007-12-15
This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
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
Galerkin v. discrete-optimal projection in nonlinear model reduction
Energy Technology Data Exchange (ETDEWEB)
Carlberg, Kevin Thomas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Barone, Matthew Franklin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Antil, Harbir [George Mason Univ., Fairfax, VA (United States)
2015-04-01
Discrete-optimal model-reduction techniques such as the Gauss{Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible ow problems where standard Galerkin techniques have failed. However, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform projection at the time-continuous level, while discrete-optimal techniques do so at the time-discrete level. This work provides a detailed theoretical and experimental comparison of the two techniques for two common classes of time integrators: linear multistep schemes and Runge{Kutta schemes. We present a number of new ndings, including conditions under which the discrete-optimal ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and experimentally that decreasing the time step does not necessarily decrease the error for the discrete-optimal ROM; instead, the time step should be `matched' to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible- ow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the discrete-optimal reduced-order model by an order of magnitude.
Text-Independent Speaker Identification Using the Histogram Transform Model
DEFF Research Database (Denmark)
Ma, Zhanyu; Yu, Hong; Tan, Zheng-Hua
2016-01-01
In this paper, we propose a novel probabilistic method for the task of text-independent speaker identification (SI). In order to capture the dynamic information during SI, we design a super-MFCCs features by cascading three neighboring Mel-frequency Cepstral coefficients (MFCCs) frames together....... These super-MFCC vectors are utilized for probabilistic model training such that the speaker’s characteristics can be sufficiently captured. The probability density function (PDF) of the aforementioned super-MFCCs features is estimated by the recently proposed histogram transform (HT) method. To recedes...
Modeling, estimation and optimal filtration in signal processing
Najim, Mohamed
2010-01-01
The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the
Health benefit modelling and optimization of vehicular pollution control strategies
Sonawane, Nayan V.; Patil, Rashmi S.; Sethi, Virendra
2012-12-01
This study asserts that the evaluation of pollution reduction strategies should be approached on the basis of health benefits. The framework presented could be used for decision making on the basis of cost effectiveness when the strategies are applied concurrently. Several vehicular pollution control strategies have been proposed in literature for effective management of urban air pollution. The effectiveness of these strategies has been mostly studied as a one at a time approach on the basis of change in pollution concentration. The adequacy and practicality of such an approach is studied in the present work. Also, the assessment of respective benefits of these strategies has been carried out when they are implemented simultaneously. An integrated model has been developed which can be used as a tool for optimal prioritization of various pollution management strategies. The model estimates health benefits associated with specific control strategies. ISC-AERMOD View has been used to provide the cause-effect relation between control options and change in ambient air quality. BenMAP, developed by U.S. EPA, has been applied for estimation of health and economic benefits associated with various management strategies. Valuation of health benefits has been done for impact indicators of premature mortality, hospital admissions and respiratory syndrome. An optimization model has been developed to maximize overall social benefits with determination of optimized percentage implementations for multiple strategies. The model has been applied for sub-urban region of Mumbai city for vehicular sector. Several control scenarios have been considered like revised emission standards, electric, CNG, LPG and hybrid vehicles. Reduction in concentration and resultant health benefits for the pollutants CO, NOx and particulate matter are estimated for different control scenarios. Finally, an optimization model has been applied to determine optimized percentage implementation of specific
Determining Reduced Order Models for Optimal Stochastic Reduced Order Models
Energy Technology Data Exchange (ETDEWEB)
Bonney, Matthew S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Brake, Matthew R.W. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2015-08-01
The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a Taylor series using finite difference, a proper orthogonal decomposition of the the output, a Craig-Bampton representation of the model, a method that uses Hyper-Dual numbers to determine the sensitivities, and a Meta-Model method that uses the Hyper-Dual results and constructs a polynomial curve to better represent the output data. The methods are compared against a parameter sweep and a distribution propagation where the first four statistical moments are used as a comparison. Each method produces very accurate results with the Craig-Bampton reduction having the least accurate results. The models are also compared based on time requirements for the evaluation of each model where the Meta- Model requires the least amount of time for computation by a significant amount. Each of the five models provided accurate results in a reasonable time frame. The determination of which model to use is dependent on the availability of the high-fidelity model and how many evaluations can be performed. Analysis of the output distribution is examined by using a large Monte-Carlo simulation along with a reduced simulation using Latin Hypercube and the stochastic reduced order model sampling technique. Both techniques produced accurate results. The stochastic reduced order modeling technique produced less error when compared to an exhaustive sampling for the majority of methods.
Fuzzy Modelling of Knee Joint with Genetic Optimization
Directory of Open Access Journals (Sweden)
B. S. K. K. Ibrahim
2011-01-01
Full Text Available Modelling of joint properties of lower limbs in people with spinal cord injury is significantly challenging for researchers due to the complexity of the system. The objective of this study is to develop a knee joint model capable of relating electrical parameters to dynamic joint torque as well as knee angle for functional electrical stimulation application. The joint model consists of a segmental dynamic, time-invariant passive properties and uncertain time-variant active properties. The knee joint model structure comprising optimised equations of motion and fuzzy models to represent the passive viscoelasticity and active muscle properties is formulated. The model thus formulated is optimised using genetic optimization, and validated against experimental data. The developed model can be used for simulation of joint movements as well as for control development. The results show that the model developed gives an accurate dynamic characterisation of the knee joint.
Nonlinear State Space Modeling and System Identification for Electrohydraulic Control
Directory of Open Access Journals (Sweden)
Jun Yan
2013-01-01
Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.
Global sensitivity analysis in the identification of cohesive models using full-field kinematic data
Alfano, Marco
2015-03-01
Failure of adhesive bonded structures often occurs concurrent with the formation of a non-negligible fracture process zone in front of a macroscopic crack. For this reason, the analysis of damage and fracture is effectively carried out using the cohesive zone model (CZM). The crucial aspect of the CZM approach is the precise determination of the traction-separation relation. Yet it is usually determined empirically, by using calibration procedures combining experimental data, such as load-displacement or crack length data, with finite element simulation of fracture. Thanks to the recent progress in image processing, and the availability of low-cost CCD cameras, it is nowadays relatively easy to access surface displacements across the fracture process zone using for instance Digital Image Correlation (DIC). The rich information provided by correlation techniques prompted the development of versatile inverse parameter identification procedures combining finite element (FE) simulations and full field kinematic data. The focus of the present paper is to assess the effectiveness of these methods in the identification of cohesive zone models. In particular, the analysis is developed in the framework of the variance based global sensitivity analysis. The sensitivity of kinematic data to the sought cohesive properties is explored through the computation of the so-called Sobol sensitivity indexes. The results show that the global sensitivity analysis can help to ascertain the most influential cohesive parameters which need to be incorporated in the identification process. In addition, it is shown that suitable displacement sampling in time and space can lead to optimized measurements for identification purposes.
Modeling Power-Constrained Optimal Backlight Dimming for Color Displays
DEFF Research Database (Denmark)
Burini, Nino; Nadernejad, Ehsan; Korhonen, Jari
2013-01-01
In this paper, we present a framework for modeling color liquid crystal displays (LCDs) having local light-emitting diode (LED) backlight with dimming capability. The proposed framework includes critical aspects like leakage, clipping, light diffusion and human perception of luminance and allows...... adjustable penalization of power consumption. Based on the framework, we have designed a set of optimization-based backlight dimming algorithms providing a perceptual optimal balance of clipping and leakage, if necessary. The novel algorithms are compared with several other schemes known from the literature...
Replica Analysis for Portfolio Optimization with Single-Factor Model
Shinzato, Takashi
2017-06-01
In this paper, we use replica analysis to investigate the influence of correlation among the return rates of assets on the solution of the portfolio optimization problem. We consider the behavior of an optimal solution for the case where the return rate is described with a single-factor model and compare the findings obtained from our proposed methods with correlated return rates with those obtained with independent return rates. We then analytically assess the increase in the investment risk when correlation is included. Furthermore, we also compare our approach with analytical procedures for minimizing the investment risk from operations research.
Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis
Directory of Open Access Journals (Sweden)
Joshua Kiddy K. Asamoah
2017-01-01
Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.
Process model development for optimization of forged disk manufacturing processes
Energy Technology Data Exchange (ETDEWEB)
Fischer, C.E.; Gunasekera, J.S. [Ohio Univ., Athens, OH (United States). Center for Advanced Materials Processing; Malas, J.C. [Wright Labs., Wright Patterson AFB, OH (United States). Materials Directorate
1997-12-31
This paper addresses the development of a system which will enable the optimization of an entire processing sequence for a forged part. Typically such a sequence may involve several stages and alternative routes of manufacturing a given part. It is important that such a system be optimized globally, (rather than locally, as is the current practice) in order to achieve improvements in affordability, producibility, and performance. This paper demonstrates the development of a simplified forging model, discussion techniques for searching and reducing a very large design space, and an objective function to evaluate the cost of a design sequence.
Model-predictive control and real-time optimization of a cat cracker unit
Directory of Open Access Journals (Sweden)
Stig Strand
1997-04-01
Full Text Available A project for control and optimization of the Residual Catalytic Cracking Process at the Mongstad refinery is near completion. Four model-predictive control applications have been successfully implemented, using the IDCOM control software from Setpoint Inc. The most attractive feature of the controller is the well-defined control prioritizing hierarchy, and the linear impulse-response models have proved to give satisfactory performance on this process. Excitation and identification of the dynamic models proved to be a difficult task, and careful design and monitoring of the tests was mandatory in order to produce good results. Multi-variable Pseudo Random Binary Test Sequences were used for the excitation. Technical performance and operator acceptance of the new control functions have been good, but it is realized that a continuing effort is needed to fine-tune and maintain such functions.
Optimizing Computing Platforms for Climate-Driven Ecological Forecasting Models
Farley, S. S.; Williams, J. W.
2016-12-01
Species distribution models are widely used, climate-driven ecological forecasting tools that use machine-learning techniques to predict species range shifts and ecological responses to 21st century climate change. As high-resolution modern and fossil biodiversity data becomes increasingly available and statistical learning methods become more computationally intensive, choosing the correct computing configuration on which to run these models becomes more important. With a variety of low-cost cloud and desktop computing options available, users of forecasting models must balance performance gains achieved by provisioning more powerful hardware with the cost of using these resources. We present a framework for estimating the optimal computing solution for a given modeling activity. We argue that this framework is capable of identifying the optimal computing solution - the one that maximizes model accuracy while minimizing resource cost and computing time. Our framework is built on constituent models of algorithm execution time, predictive skill, and computing cost. We demonstrate the results of the framework using four leading species distribution models: multivariate adaptive regression splines, generalized additive models, support vector machines, and boosted regression trees. The constituent models themselves are shown to have high predictive accuracy, and can be used independently to estimate the effects of using larger input datasets, such as those that incorporate data from the fossil record. When used together, our framework shows highly significant predictive ability, and is designed to be used by researchers to inform future computing provisioning strategies.
Diyana Rosli, Anis; Adenan, Nur Sabrina; Hashim, Hadzli; Ezan Abdullah, Noor; Sulaiman, Suhaimi; Baharudin, Rohaiza
2018-03-01
This paper shows findings of the application of Particle Swarm Optimization (PSO) algorithm in optimizing an Artificial Neural Network that could categorize between ripeness and unripeness stage of citrus suhuensis. The algorithm would adjust the network connections weights and adapt its values during training for best results at the output. Initially, citrus suhuensis fruit’s skin is measured using optically non-destructive method via spectrometer. The spectrometer would transmit VIS (visible spectrum) photonic light radiation to the surface (skin of citrus) of the sample. The reflected light from the sample’s surface would be received and measured by the same spectrometer in terms of reflectance percentage based on VIS range. These measured data are used to train and test the best optimized ANN model. The accuracy is based on receiver operating characteristic (ROC) performance. The result outcomes from this investigation have shown that the achieved accuracy for the optimized is 70.5% with a sensitivity and specificity of 60.1% and 80.0% respectively.
Hidden Markov model using Dirichlet process for de-identification.
Chen, Tao; Cullen, Richard M; Godwin, Marshall
2015-12-01
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance. Copyright © 2015 Elsevier Inc. All rights reserved.
A Convex Optimization Model and Algorithm for Retinex
Directory of Open Access Journals (Sweden)
Qing-Nan Zhao
2017-01-01
Full Text Available Retinex is a theory on simulating and explaining how human visual system perceives colors under different illumination conditions. The main contribution of this paper is to put forward a new convex optimization model for Retinex. Different from existing methods, the main idea is to rewrite a multiplicative form such that the illumination variable and the reflection variable are decoupled in spatial domain. The resulting objective function involves three terms including the Tikhonov regularization of the illumination component, the total variation regularization of the reciprocal of the reflection component, and the data-fitting term among the input image, the illumination component, and the reciprocal of the reflection component. We develop an alternating direction method of multipliers (ADMM to solve the convex optimization model. Numerical experiments demonstrate the advantages of the proposed model which can decompose an image into the illumination and the reflection components.
A model for HIV/AIDS pandemic with optimal control
Sule, Amiru; Abdullah, Farah Aini
2015-05-01
Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is pandemic. It has affected nearly 60 million people since the detection of the disease in 1981 to date. In this paper basic deterministic HIV/AIDS model with mass action incidence function are developed. Stability analysis is carried out. And the disease free equilibrium of the basic model was found to be locally asymptotically stable whenever the threshold parameter (RO) value is less than one, and unstable otherwise. The model is extended by introducing two optimal control strategies namely, CD4 counts and treatment for the infective using optimal control theory. Numerical simulation was carried out in order to illustrate the analytic results.
Optimized volume models of earthquake-triggered landslides.
Xu, Chong; Xu, Xiwei; Shen, Lingling; Yao, Qi; Tan, Xibin; Kang, Wenjun; Ma, Siyuan; Wu, Xiyan; Cai, Juntao; Gao, Mingxing; Li, Kang
2016-07-12
In this study, we proposed three optimized models for calculating the total volume of landslides triggered by the 2008 Wenchuan, China Mw 7.9 earthquake. First, we calculated the volume of each deposit of 1,415 landslides triggered by the quake based on pre- and post-quake DEMs in 20 m resolution. The samples were used to fit the conventional landslide "volume-area" power law relationship and the 3 optimized models we proposed, respectively. Two data fitting methods, i.e. log-transformed-based linear and original data-based nonlinear least square, were employed to the 4 models. Results show that original data-based nonlinear least square combining with an optimized model considering length, width, height, lithology, slope, peak ground acceleration, and slope aspect shows the best performance. This model was subsequently applied to the database of landslides triggered by the quake except for two largest ones with known volumes. It indicates that the total volume of the 196,007 landslides is about 1.2 × 10(10) m(3) in deposit materials and 1 × 10(10) m(3) in source areas, respectively. The result from the relationship of quake magnitude and entire landslide volume related to individual earthquake is much less than that from this study, which reminds us the necessity to update the power-law relationship.
Optimal control in a model of malaria with differential susceptibility
Hincapié, Doracelly; Ospina, Juan
2014-06-01
A malaria model with differential susceptibility is analyzed using the optimal control technique. In the model the human population is classified as susceptible, infected and recovered. Susceptibility is assumed dependent on genetic, physiological, or social characteristics that vary between individuals. The model is described by a system of differential equations that relate the human and vector populations, so that the infection is transmitted to humans by vectors, and the infection is transmitted to vectors by humans. The model considered is analyzed using the optimal control method when the control consists in using of insecticide-treated nets and educational campaigns; and the optimality criterion is to minimize the number of infected humans, while keeping the cost as low as is possible. One first goal is to determine the effects of differential susceptibility in the proposed control mechanism; and the second goal is to determine the algebraic form of the basic reproductive number of the model. All computations are performed using computer algebra, specifically Maple. It is claimed that the analytical results obtained are important for the design and implementation of control measures for malaria. It is suggested some future investigations such as the application of the method to other vector-borne diseases such as dengue or yellow fever; and also it is suggested the possible application of free software of computer algebra like Maxima.
Modeling, simulation and optimization for science and technology
Kuznetsov, Yuri; Neittaanmäki, Pekka; Pironneau, Olivier
2014-01-01
This volume contains thirteen articles on advances in applied mathematics and computing methods for engineering problems. Six papers are on optimization methods and algorithms with emphasis on problems with multiple criteria; four articles are on numerical methods for applied problems modeled with nonlinear PDEs; two contributions are on abstract estimates for error analysis; finally one paper deals with rare events in the context of uncertainty quantification. Applications include aerospace, glaciology and nonlinear elasticity. Herein is a selection of contributions from speakers at two conferences on applied mathematics held in June 2012 at the University of Jyväskylä, Finland. The first conference, “Optimization and PDEs with Industrial Applications” celebrated the seventieth birthday of Professor Jacques Périaux of the University of Jyväskylä and Polytechnic University of Catalonia (Barcelona Tech), and the second conference, “Optimization and PDEs with Applications” celebrated the seventy-fi...
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...
Mathematical model of the metal mould surface temperature optimization
International Nuclear Information System (INIS)
Mlynek, Jaroslav; Knobloch, Roman; Srb, Radek
2015-01-01
The article is focused on the problem of generating a uniform temperature field on the inner surface of shell metal moulds. Such moulds are used e.g. in the automotive industry for artificial leather production. To produce artificial leather with uniform surface structure and colour shade the temperature on the inner surface of the mould has to be as homogeneous as possible. The heating of the mould is realized by infrared heaters located above the outer mould surface. The conceived mathematical model allows us to optimize the locations of infrared heaters over the mould, so that approximately uniform heat radiation intensity is generated. A version of differential evolution algorithm programmed in Matlab development environment was created by the authors for the optimization process. For temperate calculations software system ANSYS was used. A practical example of optimization of heaters locations and calculation of the temperature of the mould is included at the end of the article
Fuel cycle optimization using the nonlinear reactivity model
International Nuclear Information System (INIS)
Yueksel, Z.; Cecen, Y.; Tombakoglu, M.
2002-01-01
Fuel cycle optimization is one of the key subjects of reactor operation. In this study, fuel cycles of Spectral Shift PWR and Pebble Bed HTGR are optimized by using nonlinear reactivity model. The Spectral Shift concept is based on the adjustments of fuel to moderator ratio as a function of burnup. For n-batch fuel cycle, where n is equal to 3 and 4, the fuel to moderator ratio is determined as a function of burnup to maximize discharge burnup, Bd. Results show that it is possible to increase discharge burnup up to 25 percent compared to typical commercial PWR designs. Another problem arises in the design of PB-HTGR's fuel pebbles and mixing ratio. The optimization of the composition of fuel pebbles and mixing ratio for direct and n-pass fuel cycles are analyzed to maximize discharge burnup. We compared our results with the current design parameters of HTR-10 and PBMR.(author)
Optimization model of vaccination strategy for dengue transmission
Widayani, H.; Kallista, M.; Nuraini, N.; Sari, M. Y.
2014-02-01
Dengue fever is emerging tropical and subtropical disease caused by dengue virus infection. The vaccination should be done as a prevention of epidemic in population. The host-vector model are modified with consider a vaccination factor to prevent the occurrence of epidemic dengue in a population. An optimal vaccination strategy using non-linear objective function was proposed. The genetic algorithm programming techniques are combined with fourth-order Runge-Kutta method to construct the optimal vaccination. In this paper, the appropriate vaccination strategy by using the optimal minimum cost function which can reduce the number of epidemic was analyzed. The numerical simulation for some specific cases of vaccination strategy is shown.
Modeling and Model Identification of Autonomous Underwater Vehicles
2015-06-01
shape of the Standard REMUS AUV. Figure 4. Standard Hydroid REMUS 100 The REMUS AUV uses a single DC brushless motor to power a 3 bladed...The standard model comes with six brushless DC thrusters, four of them placed to control planar motion and whose angles can be manually changed prior...Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE June 2015 3. REPORT TYPE AND DATES COVERED
Control Valve Stiction Identification, Modelling, Quantification and Control - A Review
Directory of Open Access Journals (Sweden)
Srinivasan Arumugam
2011-09-01
Full Text Available Most of the processes found in process industries exhibit undesirable nonlinearity due to backlash, saturation, hysteresis, stiction (friction, dead-zone and stuck-fault existing in control valves. The control valve is the actuator for most process control loops and, as the only moving part in the loop, its function is to implement the control action. If the control valve malfunctions, the performance of the control loop is likely to deteriorate, no matter how good the controller is. Commonly encountered control valve problems include nonlinear responses to the demand signal caused by effects such as stiction, dead-band or saturation. Because of these problems, the control loop may be oscillatory, which in turn may cause oscillations in many process variables causing a range of operational problems including increased valve wear. Understanding nonlinear behaviour of control valves in order to maintain the quality of the end products in the industry, this review article surveys the identification, modelling, estimation and design of dynamic models of stiction nonlinearity and providing appropriate controller to obtain optimum responses of the process. The primary objective of this work is to present state-of-art-review of common nonlinear problems associated with mechanical and chemical processes for encouraging researchers, practicing engineers working in this field, so that readers can invent their goals for future research work on nonlinear systems identification and control.
Pescara benchmark: overview of modelling, testing and identification
Energy Technology Data Exchange (ETDEWEB)
Bellino, A; Garibaldi, L; Marchesiello, S [Dynamics/Identification Research Group, Department of Mechanics, Politecnico of Torino, C.so Duca degli Abruzzi 24, 10129 Torino (Italy); Brancaleoni, F; Gabriele, S; Spina, D [Department of Structures, University ' Roma Tre' of Rome, Via C. Segre 4/6, 00146 Rome (Italy); Bregant, L [Department of Mechanical and Marine Engineering , University of Trieste, Via Valerio 8, 34127 Trieste (Italy); Carminelli, A; Catania, G; Sorrentino, S [Diem Department of Mechanical Engineering, University of Bologna, Viale Risorgimento 2, 40136 Bologna (Italy); Di Evangelista, A; Valente, C; Zuccarino, L, E-mail: c.valente@unich.it [Department of Engineering, University ' G. d' Annunzio' of Chieti-Pescara Viale Pindaro 42, 65127 Pescara (Italy)
2011-07-19
The 'Pescara benchmark' is part of the national research project 'BriViDi' (BRIdge VIbrations and DIagnosis) supported by the Italian Ministero dell'Universita e Ricerca. The project is aimed at developing an integrated methodology for the structural health evaluation of railway r/c, p/c bridges. The methodology should provide for applicability in operating conditions, easy data acquisition through common industrial instrumentation, robustness and reliability against structural and environmental uncertainties. The Pescara benchmark consisted in lab tests to get a consistent and large experimental data base and subsequent data processing. Special tests were devised to simulate the train transit effects in actual field conditions. Prestressed concrete beams of current industrial production both sound and damaged at various severity corrosion levels were tested. The results were collected either in a deterministic setting and in a form suitable to deal with experimental uncertainties. Damage identification was split in two approaches: with or without a reference model. In the first case f.e. models were used in conjunction with non conventional updating techniques. In the second case, specialized output-only identification techniques capable to deal with time-variant and possibly non linear systems were developed. The lab tests allowed validating the above approaches and the performances of classical modal based damage indicators.
Pescara benchmark: overview of modelling, testing and identification
Bellino, A.; Brancaleoni, F.; Bregant, L.; Carminelli, A.; Catania, G.; Di Evangelista, A.; Gabriele, S.; Garibaldi, L.; Marchesiello, S.; Sorrentino, S.; Spina, D.; Valente, C.; Zuccarino, L.
2011-07-01
The `Pescara benchmark' is part of the national research project `BriViDi' (BRIdge VIbrations and DIagnosis) supported by the Italian Ministero dell'Universitá e Ricerca. The project is aimed at developing an integrated methodology for the structural health evaluation of railway r/c, p/c bridges. The methodology should provide for applicability in operating conditions, easy data acquisition through common industrial instrumentation, robustness and reliability against structural and environmental uncertainties. The Pescara benchmark consisted in lab tests to get a consistent and large experimental data base and subsequent data processing. Special tests were devised to simulate the train transit effects in actual field conditions. Prestressed concrete beams of current industrial production both sound and damaged at various severity corrosion levels were tested. The results were collected either in a deterministic setting and in a form suitable to deal with experimental uncertainties. Damage identification was split in two approaches: with or without a reference model. In the first case f.e. models were used in conjunction with non conventional updating techniques. In the second case, specialized output-only identification techniques capable to deal with time-variant and possibly non linear systems were developed. The lab tests allowed validating the above approaches and the performances of classical modal based damage indicators.
Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D
2017-01-25
Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open
In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data
Yi, Yeon-Sook
2017-01-01
This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model…
Research on potential user identification model for electric energy substitution
Xia, Huaijian; Chen, Meiling; Lin, Haiying; Yang, Shuo; Miao, Bo; Zhu, Xinzhi
2018-01-01
The implementation of energy substitution plays an important role in promoting the development of energy conservation and emission reduction in china. Energy service management platform of alternative energy users based on the data in the enterprise production value, product output, coal and other energy consumption as a potential evaluation index, using principal component analysis model to simplify the formation of characteristic index, comprehensive index contains the original variables, and using fuzzy clustering model for the same industry user’s flexible classification. The comprehensive index number and user clustering classification based on constructed particle optimization neural network classification model based on the user, user can replace electric potential prediction. The results of an example show that the model can effectively predict the potential of users’ energy potential.
A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment
International Nuclear Information System (INIS)
Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir
2015-01-01
This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL ® software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0–238 N s m −1 through the viscous and electromagnetic components, respectively. (paper)
RISK LOAN PORTFOLIO OPTIMIZATION MODEL BASED ON CVAR RISK MEASURE
Directory of Open Access Journals (Sweden)
Ming-Chang LEE
2015-07-01
Full Text Available In order to achieve commercial banks liquidity, safety and profitability objective requirements, loan portfolio risk analysis based optimization decisions are rational allocation of assets. The risk analysis and asset allocation are the key technology of banking and risk management. The aim of this paper, build a loan portfolio optimization model based on risk analysis. Loan portfolio rate of return by using Value-at-Risk (VaR and Conditional Value-at-Risk (CVaR constraint optimization decision model reflects the bank's risk tolerance, and the potential loss of direct control of the bank. In this paper, it analyze a general risk management model applied to portfolio problems with VaR and CVaR risk measures by using Using the Lagrangian Algorithm. This paper solves the highly difficult problem by matrix operation method. Therefore, the combination of this paper is easy understanding the portfolio problems with VaR and CVaR risk model is a hyperbola in mean-standard deviation space. It is easy calculation in proposed method.
Applying the Team Identification-Social Psychological Health Model to Older Sport Fans
Wann, Daniel L.; Rogers, Kelly; Dooley, Keith; Foley, Mary
2011-01-01
According to the Team Identification-Social Psychological Health Model (Wann, 2006b), team identification and social psychological health should be positively correlated because identification leads to important social connections which, in turn, facilitate well-being. Although past research substantiates the hypothesized positive relationship…
An optimization model for transportation of hazardous materials
International Nuclear Information System (INIS)
Seyed-Hosseini, M.; Kheirkhah, A. S.
2005-01-01
In this paper, the optimal routing problem for transportation of hazardous materials is studied. Routing for the purpose of reducing the risk of transportation of hazardous materials has been studied and formulated by many researcher and several routing models have been presented up to now. These models can be classified into the categories: the models for routing a single movement and the models for routing multiple movements. In this paper, according to the current rules and regulations of road transportations of hazardous materials in Iran, a routing problem is designed. In this problem, the routs for several independent movements are simultaneously determined. To examine the model, the problem the transportations of two different dangerous materials in the road network of Mazandaran province in the north of Iran is formulated and solved by applying Integer programming model
Directory of Open Access Journals (Sweden)
Michelle Cronin
2010-09-01
Full Text Available The time of year and day, the state of the tide and prevailing environmental conditions significantly influence seal haulout behaviour. Understanding these effects is fundamentally important in deriving accurate estimates of harbour seal abundance from haulout data. We present a modelling approach to assess the influence of these variables on seals’ haulout behaviour and, by identifying the combination of covariates during which seal abundance is highest, predict the optimal time and conditions for future surveys. Count data of harbour seals at haulouts in southwest Ireland collected during 2003-2005 were included in mixed additive models together with environmental covariates, including season, time of day and weather conditions. The models show maximum abundance at haulout sites occurred during midday periods during August and in late afternoon/early evening during September. Accurate national and local population estimates are essential for the effective monitoring of the conservation status of the species and for the identification, management and monitoring of Special Areas of Conservation (SAC in accordance with the EU Habitats Directive. Our model based approach provides a useful tool for optimising the timing of harbourseal surveys in Ireland and the modelling framework is useful for predicting optimal survey periods for other protected, endangered or significant species worldwide.
Estimation of collapse moment for the wall-thinned pipe bends using fuzzy model identification
International Nuclear Information System (INIS)
Na, Man Gyun; Kim, Jin Weon; Hwang, In Joon
2006-01-01
In this work, the collapse moment due to wall-thinned defects is estimated through fuzzy model identification. A subtractive clustering method is used as the basis of a fast and robust algorithm for identifying the fuzzy model. The fuzzy model is optimized by a genetic algorithm combined with a least squares method. The developed fuzzy model has been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy model to reduce the sensitivity to the input change and the fuzzy model are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, three fuzzy models are trained, respectively, for three data sets divided into the three classes of extrados, intrados, and crown defects, which is because they have different characteristics. The relative root mean square (RMS) errors of the estimated collapse moment are 0.5397% for the training data and 0.8673% for the test data. It is known from this result that the fuzzy models are sufficiently accurate to be used in the integrity evaluation of wall-thinned pipe bends and elbows
Estimation of collapse moment for the wall-thinned pipe bends using fuzzy model identification
Energy Technology Data Exchange (ETDEWEB)
Na, Man Gyun [Department of Nuclear Engineering, Chosun University 375 Seosuk-Dong, Dong-Gu, Gwangju 501-759 (Korea, Republic of)]. E-mail: magyna@chosun.ac.kr; Kim, Jin Weon [Department of Nuclear Engineering, Chosun University 375 Seosuk-Dong, Dong-Gu, Gwangju 501-759 (Korea, Republic of); Hwang, In Joon [Department of Nuclear Engineering, Chosun University 375 Seosuk-Dong, Dong-Gu, Gwangju 501-759 (Korea, Republic of)
2006-07-15
In this work, the collapse moment due to wall-thinned defects is estimated through fuzzy model identification. A subtractive clustering method is used as the basis of a fast and robust algorithm for identifying the fuzzy model. The fuzzy model is optimized by a genetic algorithm combined with a least squares method. The developed fuzzy model has been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy model to reduce the sensitivity to the input change and the fuzzy model are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, three fuzzy models are trained, respectively, for three data sets divided into the three classes of extrados, intrados, and crown defects, which is because they have different characteristics. The relative root mean square (RMS) errors of the estimated collapse moment are 0.5397% for the training data and 0.8673% for the test data. It is known from this result that the fuzzy models are sufficiently accurate to be used in the integrity evaluation of wall-thinned pipe bends and elbows.
Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU.
Xia, Yong; Wang, Kuanquan; Zhang, Henggui
2015-01-01
Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation) and the other is the diffusion term of the monodomain model (partial differential equation). Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations.
Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU
Directory of Open Access Journals (Sweden)
Yong Xia
2015-01-01
Full Text Available Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation and the other is the diffusion term of the monodomain model (partial differential equation. Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations.
Pumping Optimization Model for Pump and Treat Systems - 15091
Energy Technology Data Exchange (ETDEWEB)
Baker, S.; Ivarson, Kristine A.; Karanovic, M.; Miller, Charles W.; Tonkin, M.
2015-01-15
Pump and Treat systems are being utilized to remediate contaminated groundwater in the Hanford 100 Areas adjacent to the Columbia River in Eastern Washington. Design of the systems was supported by a three-dimensional (3D) fate and transport model. This model provided sophisticated simulation capabilities but requires many hours to calculate results for each simulation considered. Many simulations are required to optimize system performance, so a two-dimensional (2D) model was created to reduce run time. The 2D model was developed as a equivalent-property version of the 3D model that derives boundary conditions and aquifer properties from the 3D model. It produces predictions that are very close to the 3D model predictions, allowing it to be used for comparative remedy analyses. Any potential system modifications identified by using the 2D version are verified for use by running the 3D model to confirm performance. The 2D model was incorporated into a comprehensive analysis system (the Pumping Optimization Model, POM) to simplify analysis of multiple simulations. It allows rapid turnaround by utilizing a graphical user interface that: 1 allows operators to create hypothetical scenarios for system operation, 2 feeds the input to the 2D fate and transport model, and 3 displays the scenario results to evaluate performance improvement. All of the above is accomplished within the user interface. Complex analyses can be completed within a few hours and multiple simulations can be compared side-by-side. The POM utilizes standard office computing equipment and established groundwater modeling software.
Optimal hedging with the cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Gatarek, Lukasz; Johansen, Søren
We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated...... with the hedged asset and among themselves. We nd that the minimum variance hedge for assets driven by the CVAR, depends strongly on the portfolio holding period. The hedge is dened as a function of correlation and cointegration parameters. For short holding periods the correlation impact is predominant. For long...... horizons, the hedge ratio should overweight the cointegration parameters rather then short-run correlation information. In the innite horizon, the hedge ratios shall be equal to the cointegrating vector. The hedge ratios for any intermediate portfolio holding period should be based on the weighted average...
Linear Model for Optimal Distributed Generation Size Predication
Directory of Open Access Journals (Sweden)
Ahmed Al Ameri
2017-01-01
Full Text Available This article presents a linear model predicting optimal size of Distributed Generation (DG that addresses the minimum power loss. This method is based fundamentally on strong coupling between active power and voltage angle as well as between reactive power and voltage magnitudes. This paper proposes simplified method to calculate the total power losses in electrical grid for different distributed generation sizes and locations. The method has been implemented and tested on several IEEE bus test systems. The results show that the proposed method is capable of predicting approximate optimal size of DG when compared with precision calculations. The method that linearizes a complex model showed a good result, which can actually reduce processing time required. The acceptable accuracy with less time and memory required can help the grid operator to assess power system integrated within large-scale distribution generation.
Autonomous guided vehicles methods and models for optimal path planning
Fazlollahtabar, Hamed
2015-01-01
This book provides readers with extensive information on path planning optimization for both single and multiple Autonomous Guided Vehicles (AGVs), and discusses practical issues involved in advanced industrial applications of AGVs. After discussing previously published research in the field and highlighting the current gaps, it introduces new models developed by the authors with the goal of reducing costs and increasing productivity and effectiveness in the manufacturing industry. The new models address the increasing complexity of manufacturing networks, due for example to the adoption of flexible manufacturing systems that involve automated material handling systems, robots, numerically controlled machine tools, and automated inspection stations, while also considering the uncertainty and stochastic nature of automated equipment such as AGVs. The book discusses and provides solutions to important issues concerning the use of AGVs in the manufacturing industry, including material flow optimization with A...
Sustainable logistics and transportation optimization models and algorithms
Gakis, Konstantinos; Pardalos, Panos
2017-01-01
Focused on the logistics and transportation operations within a supply chain, this book brings together the latest models, algorithms, and optimization possibilities. Logistics and transportation problems are examined within a sustainability perspective to offer a comprehensive assessment of environmental, social, ethical, and economic performance measures. Featured models, techniques, and algorithms may be used to construct policies on alternative transportation modes and technologies, green logistics, and incentives by the incorporation of environmental, economic, and social measures. Researchers, professionals, and graduate students in urban regional planning, logistics, transport systems, optimization, supply chain management, business administration, information science, mathematics, and industrial and systems engineering will find the real life and interdisciplinary issues presented in this book informative and useful.
The PDB_REDO server for macromolecular structure model optimization
Directory of Open Access Journals (Sweden)
Robbie P. Joosten
2014-07-01
Full Text Available The refinement and validation of a crystallographic structure model is the last step before the coordinates and the associated data are submitted to the Protein Data Bank (PDB. The success of the refinement procedure is typically assessed by validating the models against geometrical criteria and the diffraction data, and is an important step in ensuring the quality of the PDB public archive [Read et al. (2011, Structure, 19, 1395–1412]. The PDB_REDO procedure aims for `constructive validation', aspiring to consistent and optimal refinement parameterization and pro-active model rebuilding, not only correcting errors but striving for optimal interpretation of the electron density. A web server for PDB_REDO has been implemented, allowing thorough, consistent and fully automated optimization of the refinement procedure in REFMAC and partial model rebuilding. The goal of the web server is to help practicing crystallographers to improve their model prior to submission to the PDB. For this, additional steps were implemented in the PDB_REDO pipeline, both in the refinement procedure, e.g. testing of resolution limits and k-fold cross-validation for small test sets, and as new validation criteria, e.g. the density-fit metrics implemented in EDSTATS and ligand validation as implemented in YASARA. Innovative ways to present the refinement and validation results to the user are also described, which together with auto-generated Coot scripts can guide users to subsequent model inspection and improvement. It is demonstrated that using the server can lead to substantial improvement of structure models before they are submitted to the PDB.
Identification of reverse logistics decision types from mathematical models
Directory of Open Access Journals (Sweden)
Pascual Cortés Pellicer
2018-04-01
Full Text Available Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL. At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified. Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL. Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions. Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.
Thin film bulk acoustic wave devices : performance optimization and modeling
Pensala, Tuomas
2011-01-01
Thin film bulk acoustic wave (BAW) resonators and filters operating in the GHz range are used in mobile phones for the most demanding filtering applications and complement the surface acoustic wave (SAW) based filters. Their main advantages are small size and high performance at frequencies above 2 GHz. This work concentrates on the characterization, performance optimization, and modeling techniques of thin film BAW devices. Laser interferometric vibration measurements together with plat...
Modeling marine surface microplastic transport to assess optimal removal locations
Sherman, P; Van Sebille, E
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics floating on the ocean surface from 2015 to 2025, with the goal to assess the optimal marine microplastic removal locations for two scenarios: removing the most surface microplastic and reducing the ...
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Feipeng Guo; Qibei Lu
2013-01-01
With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic met...
Modeling and optimization of a hybrid solar combined cycle (HYCS)
Eter, Ahmad Adel
2011-12-01
The main objective of this thesis is to investigate the feasibility of integrating concentrated solar power (CSP) technology with the conventional combined cycle technology for electric generation in Saudi Arabia. The generated electricity can be used locally to meet the annual increasing demand. Specifically, it can be utilized to meet the demand during the hours 10 am-3 pm and prevent blackout hours, of some industrial sectors. The proposed CSP design gives flexibility in the operation system. Since, it works as a conventional combined cycle during night time and it switches to work as a hybrid solar combined cycle during day time. The first objective of the thesis is to develop a thermo-economical mathematical model that can simulate the performance of a hybrid solar-fossil fuel combined cycle. The second objective is to develop a computer simulation code that can solve the thermo-economical mathematical model using available software such as E.E.S. The developed simulation code is used to analyze the thermo-economic performance of different configurations of integrating the CSP with the conventional fossil fuel combined cycle to achieve the optimal integration configuration. This optimal integration configuration has been investigated further to achieve the optimal design of the solar field that gives the optimal solar share. Thermo-economical performance metrics which are available in the literature have been used in the present work to assess the thermo-economic performance of the investigated configurations. The economical and environmental impact of integration CSP with the conventional fossil fuel combined cycle are estimated and discussed. Finally, the optimal integration configuration is found to be solarization steam side in conventional combined cycle with solar multiple 0.38 which needs 29 hectare and LEC of HYCS is 63.17 $/MWh under Dhahran weather conditions.
Holz, Jasmin A.; Boerwinkel, David F.; Meijer, Sybren L.; Visser, Mike; van Leeuwen, Ton G.; Aalders, Maurice C. G.; Bergman, Jacques J. G. H. M.
2013-01-01
Fluorescence spectroscopy has the potential to detect early cellular changes in Barrett's oesophagus before these become visible. As the technique is based on varying concentrations of intrinsic fluorophores, each with its own optimal excitation wavelength, it is important to assess the optimal
Optimization of a semiconductor manufacturing process using a reentrant model
Directory of Open Access Journals (Sweden)
Sarah Abuhab Valente
2015-01-01
Full Text Available The scope of this work is the simulation of a semiconductor manufacturing model in Arena® software and subsequent optimization and sensitivity analysis of this model. The process is considered extremely complex given the amount of steps, machinery, parameters, and highly reentrant characteristics, which makes it difficult to reach stability of production process. The production model used was the Intel Five-Machine Six-Step Mini-fab developed by Karl Kempf (1994. It was programmed in Arena® and optimized by OptQuest®, an add-on. We concluded that variation in the number of machines and operators reflects on cycle time only if there is an increase of one unit of resource more than obtained in the optimization. As a result, we highlighted the scenario where a reduction in cycle time stood out, in which one extra unit was added in the second machine group, representing a 7.41% reduction in cycle time.
Linear versus quadratic portfolio optimization model with transaction cost
Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah
2014-06-01
Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.
International Nuclear Information System (INIS)
Silva, Marcio H.; Schirru, Roberto; Medeiros, Jose A.C.C.
2009-01-01
Using concepts and principles of the quantum computation, as the quantum bit and superposition of states, coupled with the biological metaphor of a colony of ants, used in the Ant Colony Optimization algorithm (ACO), Wang et al developed the Quantum Ant Colony Optimization (QACO). In this paper we present a modification of the algorithm proposed by Wang et al. While the original QACO was used just for simple benchmarks functions with, at the most, two dimensions, QACO A lfa was developed for application where the original QACO, due to its tendency to converge prematurely, does not obtain good results, as in complex multidimensional functions. Furthermore, to evaluate its behavior, both algorithms are applied to the real problem of identification of accidents in PWR nuclear power plants. (author)
International Nuclear Information System (INIS)
Chen Zhou; Qiu-Nan Tong; Zhang Cong-Cong; Hu Zhan
2015-01-01
Identification of acetone and its two isomers, and the control of their ionization and dissociation processes are performed using a dual-mass-spectrometer scheme. The scheme employs two sets of time of flight mass spectrometers to simultaneously acquire the mass spectra of two different molecules under the irradiation of identically shaped femtosecond laser pulses. The optimal laser pulses are found using closed-loop learning method based on a genetic algorithm. Compared with the mass spectra of the two isomers that are obtained with the transform limited pulse, those obtained under the irradiation of the optimal laser pulse show large differences and the various reaction pathways of the two molecules are selectively controlled. The experimental results demonstrate that the scheme is quite effective and useful in studies of two molecules having common mass peaks, which makes a traditional single mass spectrometer unfeasible. (paper)
Directory of Open Access Journals (Sweden)
Sean Ekins
Full Text Available High-throughput screening (HTS in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb for further development towards new tuberculosis (TB drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
Truong, Bui Ngoc Minh; Nam, Doan Ngoc Chi; Ahn, Kyoung Kwan
2013-09-01
Dielectric electro-active polymer (DEAP) materials are attractive since they are low cost, lightweight and have a large deformation capability. They have no operating noise, very low electric power consumption and higher performance and efficiency than competing technologies. However, DEAP materials generally have strong hysteresis as well as uncertain and nonlinear characteristics. These disadvantages can limit the efficiency in the use of DEAP materials. To address these limitations, this research will present the combination of the Preisach model and the dynamic nonlinear autoregressive exogenous (NARX) fuzzy model-based adaptive particle swarm optimization (APSO) identification algorithm for modeling and identification of the nonlinear behavior of one typical type of DEAP actuator. Firstly, open loop input signals are applied to obtain nonlinear features and to investigate the responses of the DEAP actuator system. Then, a Preisach model can be combined with a dynamic NARX fuzzy structure to estimate the tip displacement of a DEAP actuator. To optimize all unknown parameters of the designed combination, an identification scheme based on a least squares method and an APSO algorithm is carried out. Finally, experimental validation research is carefully completed, and the effectiveness of the proposed model is evaluated by employing various input signals.
Mathematical model of statistical identification of information support of road transport
Directory of Open Access Journals (Sweden)
V. G. Kozlov
2016-01-01
Full Text Available In this paper based on the statistical identification method using the theory of self-organizing systems, built multifactor model the relationship of road transport and training system. Background information for the model represented by a number of parameters of average annual road transport operations and information provision, including training complex system parameters (inputs, road management and output parameters. Ask two criteria: stability criterion model and test correlation. The program determines their minimum, and is the only model of optimal complexity. The predetermined number of parameters established mathematical relationship of each output parameter with the others. To improve the accuracy and regularity of the forecast of the interpolation nodes allocated in the test data sequence. Other data form the training sequence. Decision model based on the principle of selection. Running it with the gradual complication of the mathematical description and exhaustive search of all possible variants of the models on the specified criteria. Advantages of the proposed model: adequately reflects the actual process, allows you to enter any additional input parameters and determine their impact on the individual output parameters of the road transport, allows in turn change the values of key parameters in a certain ratio and to determine the appropriate changes the output parameters of the road transport, allows to predict the output parameters road transport operations.
Optimal experiment design for model selection in biochemical networks.
Vanlier, Joep; Tiemann, Christian A; Hilbers, Peter A J; van Riel, Natal A W
2014-02-20
Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors.
Capital Cost Optimization for Prefabrication: A Factor Analysis Evaluation Model
Directory of Open Access Journals (Sweden)
Hong Xue
2018-01-01
Full Text Available High capital cost is a significant hindrance to the promotion of prefabrication. In order to optimize cost management and reduce capital cost, this study aims to explore the latent factors and factor analysis evaluation model. Semi-structured interviews were conducted to explore potential variables and then questionnaire survey was employed to collect professionals’ views on their effects. After data collection, exploratory factor analysis was adopted to explore the latent factors. Seven latent factors were identified, including “Management Index”, “Construction Dissipation Index”, “Productivity Index”, “Design Efficiency Index”, “Transport Dissipation Index”, “Material increment Index” and “Depreciation amortization Index”. With these latent factors, a factor analysis evaluation model (FAEM, divided into factor analysis model (FAM and comprehensive evaluation model (CEM, was established. The FAM was used to explore the effect of observed variables on the high capital cost of prefabrication, while the CEM was used to evaluate comprehensive cost management level on prefabrication projects. Case studies were conducted to verify the models. The results revealed that collaborative management had a positive effect on capital cost of prefabrication. Material increment costs and labor costs had significant impacts on production cost. This study demonstrated the potential of on-site management and standardization design to reduce capital cost. Hence, collaborative management is necessary for cost management of prefabrication. Innovation and detailed design were needed to improve cost performance. The new form of precast component factories can be explored to reduce transportation cost. Meanwhile, targeted strategies can be adopted for different prefabrication projects. The findings optimized the capital cost and improved the cost performance through providing an evaluation and optimization model, which helps managers to
Fiorini, Rodolfo A.; Dacquino, Gianfranco
2005-03-01
GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous
Optimization of benzoxazinones as natural herbicide models by lipophilicity enhancement.
Macías, Francisco A; Marín, David; Oliveros-Bastidas, Alberto; Molinillo, José M G
2006-12-13
Benzoxazinones are plant allelochemicals well-known for their phytotoxic activity and for taking part in the defense strategies of Gramineae, Ranunculaceae, and Scrophulariceae plants. These properties, in addition to the recently optimized methodologies for their large-scale isolation and synthesis, have made some derivatives of natural products, 2,4-dihydroxy-(2H)-1,4-benzoxazin-3-(4H)-one (DIBOA) and its 7-methoxy analogue (DIMBOA), successful templates in the search for natural herbicide models. These new chemicals should be part of integrated methodologies for weed control. In ongoing research about the structure-activity relationships of benzoxazinones and the structural requirements for their phytotoxicity enhancement and after characterization of the optimal structural features, a new generation of chemicals with enhanced lipophilicity was developed. They were tested on selected standard target species and weeds in the search for the optimal aqueous solubility-lipophilicity rate for phytotoxicity. This physical parameter is known to be crucial in modern drug and agrochemical design strategies. The new compounds obtained in this way had interesting phytotoxicity profiles, empowering the phytotoxic effect of the starting benzoxazinone template in some cases. Quantitative structure-activity relationships were obtained by bioactivity-molecular parameters correlations. Because optimal lipophilicity values for phytotoxicity vary with the tested plant, these new derivatives constitute a more selective way to take advantage of benzoxazinone phytotoxic capabilities.
Identification of the 1PL Model with Guessing Parameter: Parametric and Semi-Parametric Results
San Martin, Ernesto; Rolin, Jean-Marie; Castro, Luis M.
2013-01-01
In this paper, we study the identification of a particular case of the 3PL model, namely when the discrimination parameters are all constant and equal to 1. We term this model, 1PL-G model. The identification analysis is performed under three different specifications. The first specification considers the abilities as unknown parameters. It is…
Deciphering the Crowd: Modeling and Identification of Pedestrian Group Motion
Directory of Open Access Journals (Sweden)
Norihiro Hagita
2013-01-01
Full Text Available Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.
Identification of the reduced order models of a BWR reactor
International Nuclear Information System (INIS)
Hernandez S, A.
2004-01-01
The present work has as objective to analyze the relative stability of a BWR type reactor. It is analyzed that so adaptive it turns out to identify the parameters of a model of reduced order so that this it reproduces a condition of given uncertainty. This will take of a real fact happened in the La Salle plant under certain operation conditions of power and flow of coolant. The parametric identification is carried out by means of an algorithm of recursive least square and an Output Error model (Output Error), measuring the output power of the reactor when the instability is present, and considering that it is produced by a change in the reactivity of the system in the same way that a sign of type step. Also it is carried out an analytic comparison of the relative stability, analyzing two types of answers: the original answer of the uncertainty of the reactor vs. the obtained response identifying the parameters of the model of reduced order, reaching the conclusion that it is very viable to adapt a model of reduced order to study the stability of a reactor, under the only condition to consider that the dynamics of the reactivity is of step type. (Author)
Deciphering the crowd: modeling and identification of pedestrian group motion.
Yücel, Zeynep; Zanlungo, Francesco; Ikeda, Tetsushi; Miyashita, Takahiro; Hagita, Norihiro
2013-01-14
Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.
Optimizing ZigBee Security using Stochastic Model Checking
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
ZigBee is a fairly new but promising wireless sensor network standard that offers the advantages of simple and low resource communication. Nevertheless, security is of great concern to ZigBee, and enhancements are prescribed in the latest ZigBee specication: ZigBee-2007. In this technical report......, we identify an important gap in the specification on key updates, and present a methodology for determining optimal key update policies and security parameters. We exploit the stochastic model checking approach using the probabilistic model checker PRISM, and assess the security needs for realistic...
Optimizing Markovian modeling of chaotic systems with recurrent neural networks
International Nuclear Information System (INIS)
Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de
2008-01-01
In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included
Error Modeling and Design Optimization of Parallel Manipulators
DEFF Research Database (Denmark)
Wu, Guanglei
challenges due to their highly nonlinear behaviors, thus, the parameter and performance analysis, especially the accuracy and stiness, are particularly important. Toward the requirements of robotic technology such as light weight, compactness, high accuracy and low energy consumption, utilizing optimization...... theory and virtual spring approach, a general kinetostatic model of the spherical parallel manipulators is developed and validated with Finite Element approach. This model is applied to the stiness analysis of a special spherical parallel manipulator with unlimited rolling motion and the obtained stiness...
Lu, Shi Jing; Salleh, Abdul Hakim Mohamed; Mohamad, Mohd Saberi; Deris, Safaai; Omatu, Sigeru; Yoshioka, Michifumi
2014-09-28
Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. Copyright © 2014 Elsevier Ltd. All rights reserved.
Transfer prices assignment with integrated production and marketing optimization models
Directory of Open Access Journals (Sweden)
Enrique Parra
2018-04-01
Full Text Available Purpose: In decentralized organizations (today a great majority of the large multinational groups, much of the decision-making power is in its individual business units-BUs-. In these cases, the management control system (MCS uses transfer prices to coordinate actions of the BUs and to evaluate their performance with the goal of guaranteeing the whole corporation optimum. The purpose of the investigation is to design transfer prices that suit this goal. Design/methodology/approach: Considering the results of the whole company supply chain optimization models (in the presence of seasonality of demand the question is to design a mechanism that creates optimal incentives for the managers of each business unit to drive the corporation to the optimal performance. Mathematical programming models are used as a start point. Findings: Different transfer prices computation methods are introduced in this paper for decentralised organizations with two divisions (production and marketing. The methods take into account the results of the solution of the whole company supply chain optimization model, if exists, and can be adapted to the type of information available in the company. It is mainly focused on transport costs assignment. Practical implications: Using the methods proposed in this paper a decentralized corporation can implement more accurate transfer prices to drive the whole organization to the global optimum performance. Originality/value: The methods proposed are a new contribution to the literature on transfer prices with special emphasis on the practical and easy implementation in a modern corporation with several business units and with high seasonality of demand. Also, the methods proposed are very flexible and can be tuned depending on the type of information available in the company.
Dos Santos, P Lopes; Deshpande, Sunil; Rivera, Daniel E; Azevedo-Perdicoúlis, T-P; Ramos, J A; Younger, Jarred
2013-12-31
There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.
Identification of Constitutive Parameters Using Inverse Strategy Coupled to an ANN Model
International Nuclear Information System (INIS)
Aguir, H.; Chamekh, A.; BelHadjSalah, H.; Hambli, R.
2007-01-01
This paper deals with the identification of material parameters using an inverse strategy. In the classical methods, the inverse technique is generally coupled with a finite element code which leads to a long computing time. In this work an inverse strategy coupled with an ANN procedure is proposed. This method has the advantage of being faster than the classical one. To validate this approach an experimental plane tensile and bulge tests are used in order to identify material behavior. The ANN model is trained from finite element simulations of the two tests. In order to reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure to identify material parameters for the AISI304. The identified material parameters are the hardening curve and the anisotropic coefficients
Optimal Retail Price Model for Partial Consignment to Multiple Retailers
Directory of Open Access Journals (Sweden)
Po-Yu Chen
2017-01-01
Full Text Available This paper investigates the product pricing decision-making problem under a consignment stock policy in a two-level supply chain composed of one supplier and multiple retailers. The effects of the supplier’s wholesale prices and its partial inventory cost absorption of the retail prices of retailers with different market shares are investigated. In the partial product consignment model this paper proposes, the seller and the retailers each absorb part of the inventory costs. This model also provides general solutions for the complete product consignment and the traditional policy that adopts no product consignment. In other words, both the complete consignment and nonconsignment models are extensions of the proposed model (i.e., special cases. Research results indicated that the optimal retail price must be between 1/2 (50% and 2/3 (66.67% times the upper limit of the gross profit. This study also explored the results and influence of parameter variations on optimal retail price in the model.
Roll levelling semi-analytical model for process optimization
Silvestre, E.; Garcia, D.; Galdos, L.; Saenz de Argandoña, E.; Mendiguren, J.
2016-08-01
Roll levelling is a primary manufacturing process used to remove residual stresses and imperfections of metal strips in order to make them suitable for subsequent forming operations. In the last years the importance of this process has been evidenced with the apparition of Ultra High Strength Steels with strength > 900 MPa. The optimal setting of the machine as well as a robust machine design has become critical for the correct processing of these materials. Finite Element Method (FEM) analysis is the widely used technique for both aspects. However, in this case, the FEM simulation times are above the admissible ones in both machine development and process optimization. In the present work, a semi-analytical model based on a discrete bending theory is presented. This model is able to calculate the critical levelling parameters i.e. force, plastification rate, residual stresses in a few seconds. First the semi-analytical model is presented. Next, some experimental industrial cases are analyzed by both the semi-analytical model and the conventional FEM model. Finally, results and computation times of both methods are compared.
van der Molen, Thys; van Boven, Job F. M.; Maguire, Terence; Goyal, Pankaj; Altman, Pablo
The aim of this paper was to propose key steps for community pharmacist integration into a patient care pathway for chronic obstructive pulmonary disease (COPD) management. A literature search was conducted to identify publications focusing on the role of the community pharmacist in identification
Transport Routes Optimization Model Through Application of Fuzzy Logic
Directory of Open Access Journals (Sweden)
Ivan Bortas
2018-03-01
Full Text Available The transport policy of the European Union is based on the mission of restructuring road traffic into other and energy-favourable transport modes which have not been sufficiently represented yet. Therefore, the development of the inland waterway and rail transport, and connectivity in the intermodal transport network are development planning priorities of the European transport strategy. The aim of this research study was to apply the scientific methodology and thus analyse the factors that affect the distribution of the goods flows and by using the fuzzy logic to make an optimization model, according to the criteria of minimizing the costs and negative impact on the environment, for the selection of the optimal transport route. Testing of the model by simulation, was performed on the basis of evaluating the criteria of the influential parameters with unprecise and indefinite input parameters. The testing results show that by the distribution of the goods flow from road transport network to inland waterways or rail transport, can be predicted in advance and determine the transport route with optimal characteristics. The results of the performed research study will be used to improve the process of planning the transport service, with the aim of reducing the transport costs and environmental pollution.
Routing and Scheduling Optimization Model of Sea Transportation
barus, Mika debora br; asyrafy, Habib; nababan, Esther; mawengkang, Herman
2018-01-01
This paper examines the routing and scheduling optimization model of sea transportation. One of the issues discussed is about the transportation of ships carrying crude oil (tankers) which is distributed to many islands. The consideration is the cost of transportation which consists of travel costs and the cost of layover at the port. Crude oil to be distributed consists of several types. This paper develops routing and scheduling model taking into consideration some objective functions and constraints. The formulation of the mathematical model analyzed is to minimize costs based on the total distance visited by the tanker and minimize the cost of the ports. In order for the model of the problem to be more realistic and the cost calculated to be more appropriate then added a parameter that states the multiplier factor of cost increases as the charge of crude oil is filled.
Optimization of arterial age prediction models based in pulse wave
International Nuclear Information System (INIS)
Scandurra, A G; Meschino, G J; Passoni, L I; Dai Pra, A L; Introzzi, A R; Clara, F M
2007-01-01
We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff
Optimization of arterial age prediction models based in pulse wave
Energy Technology Data Exchange (ETDEWEB)
Scandurra, A G [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Meschino, G J [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Passoni, L I [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Dai Pra, A L [Engineering Aplied Artificial Intelligence Group, Mathematics Department, Mar del Plata University (Argentina); Introzzi, A R [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Clara, F M [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina)
2007-11-15
We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff.
Distributionally Robust Return-Risk Optimization Models and Their Applications
Directory of Open Access Journals (Sweden)
Li Yang
2014-01-01
Full Text Available Based on the risk control of conditional value-at-risk, distributionally robust return-risk optimization models with box constraints of random vector are proposed. They describe uncertainty in both the distribution form and moments (mean and covariance matrix of random vector. It is difficult to solve them directly. Using the conic duality theory and the minimax theorem, the models are reformulated as semidefinite programming problems, which can be solved by interior point algorithms in polynomial time. An important theoretical basis is therefore provided for applications of the models. Moreover, an application of the models to a practical example of portfolio selection is considered, and the example is evaluated using a historical data set of four stocks. Numerical results show that proposed methods are robust and the investment strategy is safe.
Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model
Directory of Open Access Journals (Sweden)
Farzad Tahriri
2014-01-01
Full Text Available It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary goal is to minimize the make-span, setup time, and cost simultaneously in mixed-model assembly lines. Such conflicting goals arise when switching between different products. A genetic algorithm (GA approach is used to solve this problem, in which trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data.
Efficient Parameterization for Grey-box Model Identification of Complex Physical Systems
DEFF Research Database (Denmark)
Blanke, Mogens; Knudsen, Morten Haack
2006-01-01
Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations are the be......Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations...
Energy Technology Data Exchange (ETDEWEB)
Freeman, J.; Junk, T. [Fermi National Accelerator Laboratory, Batavia, IL 60510 (United States); Kirby, M., E-mail: kirby@fnal.gov [Fermi National Accelerator Laboratory, Batavia, IL 60510 (United States); Oksuzian, Y. [University of Virginia, Charlottesville, VA 22906 (United States); Phillips, T.J. [Duke University, Durham, NC 27708 (United States); Snider, F.D. [Fermi National Accelerator Laboratory, Batavia, IL 60510 (United States); Trovato, M. [Isituto Nazionale di Fisica Nucleare Pisa, Scuola Normale Superiore, I-56127 Pisa (Italy); Vizan, J. [Universite catholique de Louvain, Louvain la Neuve, B-1348 (Belgium); Yao, W.M. [Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)
2013-01-01
We present the development and validation of the Higgs Optimized b Identification Tagger (HOBIT), a multivariate b-jet identification algorithm optimized for Higgs boson searches at the CDF experiment at the Fermilab Tevatron. At collider experiments, b taggers allow one to distinguish particle jets containing B hadrons from other jets; these algorithms have been used for many years with great success at CDF. HOBIT has been designed specifically for use in searches for light Higgs bosons decaying via H{yields}bb{sup Macron }. This fact combined with the extent to which HOBIT synthesizes and extends the best ideas of previous taggers makes HOBIT unique among CDF b-tagging algorithms. Employing feed-forward neural network architectures, HOBIT provides an output value ranging from approximately -1 ('light-jet like') to 1 ('b-jet like'); this continuous output value has been tuned to provide maximum sensitivity in light Higgs boson search analyses. When tuned to the equivalent light jet rejection rate, HOBIT tags 54% of b jets in Monte Carlo simulated Higgs boson events (m{sub H}=120 GeV/c{sup 2}) compared to 39% for SecVtx, the most commonly used b tagger at CDF. We present features of the tagger as well as its characterization in the form of b-jet finding efficiencies and false (light-jet) tag rates.
Halimahtussadiyah, R.; Natsir, Muh.; Kurniawati, Desy; Utamy, Sukma Puspita
2017-03-01
Isolation and identification of chitinolytic bacteria from pohara river and optimation of chitinase enzyme production has been conducted. The aims of the study were isolation, characterize and optimaze of chitinase enzyme production. This study was carried out in three stages; isolation and selection of chitinolytic bacteria, characterization and identification of selected bacteria; optimization of the production of the enzyme (substrate concentration, temperature, and pH), and the determination of growth curve of T3 isolate. The chitinase activity assay was carried out using Schales method. The results of the screening obtained 6 isolates of potential bacteria of chitinolytic. The T3 isolate then was selected for the enzyme production, because it had the highest chitinolytic index of 22.31 mm. The morphological and biochemical observation showed that T3 isolate as a group of bacteria Aerobacter with Gram-negative nature, and shaped bacillus. The optimum condition for chitinase enzyme production was in chitin substrat concentration 0.06%, temperature of 30°C, and pH of 6.
Energy Technology Data Exchange (ETDEWEB)
Freeman, J.; Junk, T.; Kirby, M.; Oksuzian, Y.; Phillips, T. J.; Snider, F. D.; Trovato, M.; Vizan, J.; Yao, W. M.
2013-01-01
We present the development and validation of the Higgs Optimized b Identification Tagger (HOBIT), a multivariate b-jet identification algorithm optimized for Higgs boson searches at the CDF experiment at the Fermilab Tevatron. At collider experiments, b taggers allow one to distinguish particle jets containing B hadrons from other jets; these algorithms have been used for many years with great success at CDF. HOBIT has been designed specifically for use in searches for light Higgs bosons decaying via H ! b\\bar{b}. This fact combined with the extent to which HOBIT synthesizes and extends the best ideas of previous taggers makes HOBIT unique among CDF b-tagging algorithms. Employing feed-forward neural network architectures, HOBIT provides an output value ranging from approximately -1 ("light-jet like") to 1 ("b-jet like"); this continuous output value has been tuned to provide maximum sensitivity in light Higgs boson search analyses. When tuned to the equivalent light jet rejection rate, HOBIT tags 54% of b jets in simulated 120 GeV/c2 Higgs boson events compared to 39% for SecVtx, the most commonly used b tagger at CDF. We present features of the tagger as well as its characterization in the form of b-jet finding efficiencies and false (light-jet) tag rates.
Olaya-Abril, Alfonso; Gómez-Gascón, Lidia; Jiménez-Munguía, Irene; Obando, Ignacio; Rodríguez-Ortega, Manuel J
2012-06-27
Bacterial surface proteins are of outmost importance as they play critical roles in the interaction between cells and their environment. In addition, they can be targets of either vaccines or antibodies. Proteomic analysis through "shaving" live cells with proteases has become a successful approach for a fast and reliable identification of surface proteins. However, this protocol has not been able to reach the goal of excluding cytoplasmic contamination, as cell lysis is an inherent process during culture and experimental manipulation. In this work, we carried out the optimization of the "shaving" strategy for the Gram-positive human pathogen Streptococcus pneumoniae, a bacterium highly susceptible to autolysis, and set up the conditions for maximizing the identification of surface proteins containing sorting or exporting signals, and for minimizing cytoplasmic contamination. We also demonstrate that cell lysis is an inherent process during culture and experimental manipulation, and that a low level of lysis is enough to contaminate a "surfome" preparation with peptides derived from cytoplasmic proteins. When the optimized conditions were applied to several clinical isolates, we found the majority of the proteins described to induce protection against pneumococcal infection. In addition, we found other proteins whose protection capacity has not been yet tested. In addition, we show the utility of this approach for providing antigens that can be used in serological tests for the diagnosis of pneumococcal disease. Copyright © 2012 Elsevier B.V. All rights reserved.
Simulation platform to model, optimize and design wind turbines
Energy Technology Data Exchange (ETDEWEB)
Iov, F.; Hansen, A.D.; Soerensen, P.; Blaabjerg, F.
2004-03-01
This report is a general overview of the results obtained in the project 'Electrical Design and Control. Simulation Platform to Model, Optimize and Design Wind Turbines'. The motivation for this research project is the ever-increasing wind energy penetration into the power network. Therefore, the project has the main goal to create a model database in different simulation tools for a system optimization of the wind turbine systems. Using this model database a simultaneous optimization of the aerodynamic, mechanical, electrical and control systems over the whole range of wind speeds and grid characteristics can be achieved. The report is structured in six chapters. First, the background of this project and the main goals as well as the structure of the simulation platform is given. The main topologies for wind turbines, which have been taken into account during the project, are briefly presented. Then, the considered simulation tools namely: HAWC, DIgSILENT, Saber and Matlab/Simulink have been used in this simulation platform are described. The focus here is on the modelling and simulation time scale aspects. The abilities of these tools are complementary and they can together cover all the modelling aspects of the wind turbines e.g. mechanical loads, power quality, switching, control and grid faults. However, other simulation packages e.g PSCAD/EMTDC can easily be added in the simulation platform. New models and new control algorithms for wind turbine systems have been developed and tested in these tools. All these models are collected in dedicated libraries in Matlab/Simulink as well as in Saber. Some simulation results from the considered tools are presented for MW wind turbines. These simulation results focuses on fixed-speed and variable speed/pitch wind turbines. A good agreement with the real behaviour of these systems is obtained for each simulation tool. These models can easily be extended to model different kinds of wind turbines or large wind
Ramin, Pedram; Valverde-Pérez, Borja; Polesel, Fabio; Locatelli, Luca; Plósz, Benedek Gy
2017-08-24
This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology.
2012-06-01
The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...
2012-11-01
The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...
Kalicka, Renata; Pietrenko-Dabrowska, Anna
2007-03-01
In the paper MRI measurements are used for assessment of brain tissue perfusion and other features and functions of the brain (cerebral blood flow - CBF, cerebral blood volume - CBV, mean transit time - MTT). Perfusion is an important indicator of tissue viability and functioning as in pathological tissue blood flow, vascular and tissue structure are altered with respect to normal tissue. MRI enables diagnosing diseases at an early stage of their course. The parametric and non-parametric approaches to the identification of MRI models are presented and compared. The non-parametric modeling adopts gamma variate functions. The parametric three-compartmental catenary model, based on the general kinetic model, is also proposed. The parameters of the models are estimated on the basis of experimental data. The goodness of fit of the gamma variate and the three-compartmental models to the data and the accuracy of the parameter estimates are compared. Kalman filtering, smoothing the measurements, was adopted to improve the estimate accuracy of the parametric model. Parametric modeling gives a better fit and better parameter estimates than non-parametric and allows an insight into the functioning of the system. To improve the accuracy optimal experiment design related to the input signal was performed.
Vehicle Propulsion Systems Introduction to Modeling and Optimization
Guzzella, Lino
2013-01-01
This text provides an introduction to the mathematical modeling and subsequent optimization of vehicle propulsion systems and their supervisory control algorithms. Automobiles are responsible for a substantial part of the world's consumption of primary energy, mostly fossil liquid hydrocarbons and the reduction of the fuel consumption of these vehicles has become a top priority. Increasing concerns over fossil fuel consumption and the associated environmental impacts have motivated many groups in industry and academia to propose new propulsion systems and to explore new optimization methodologies. This third edition has been prepared to include many of these developments. In the third edition, exercises are included at the end of each chapter and the solutions are available on the web.
Using Cotton Model Simulations to Estimate Optimally Profitable Irrigation Strategies
Mauget, S. A.; Leiker, G.; Sapkota, P.; Johnson, J.; Maas, S.
2011-12-01
In recent decades irrigation pumping from the Ogallala Aquifer has led to declines in saturated thickness that have not been compensated for by natural recharge, which has led to questions about the long-term viability of agriculture in the cotton producing areas of west Texas. Adopting irrigation management strategies that optimize profitability while reducing irrigation waste is one way of conserving the aquifer's water resource. Here, a database of modeled cotton yields generated under drip and center pivot irrigated and dryland production scenarios is used in a stochastic dominance analysis that identifies such strategies under varying commodity price and pumping cost conditions. This database and analysis approach will serve as the foundation for a web-based decision support tool that will help producers identify optimal irrigation treatments under specified cotton price, electricity cost, and depth to water table conditions.
Optimal difference-based estimation for partially linear models
Zhou, Yuejin
2017-12-16
Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.
Optimal dividends in the Brownian motion risk model with interest
Fang, Ying; Wu, Rong
2009-07-01
In this paper, we consider a Brownian motion risk model, and in addition, the surplus earns investment income at a constant force of interest. The objective is to find a dividend policy so as to maximize the expected discounted value of dividend payments. It is well known that optimality is achieved by using a barrier strategy for unrestricted dividend rate. However, ultimate ruin of the company is certain if a barrier strategy is applied. In many circumstances this is not desirable. This consideration leads us to impose a restriction on the dividend stream. We assume that dividends are paid to the shareholders according to admissible strategies whose dividend rate is bounded by a constant. Under this additional constraint, we show that the optimal dividend strategy is formed by a threshold strategy.
Learning optimized features for hierarchical models of invariant object recognition.
Wersing, Heiko; Körner, Edgar
2003-07-01
There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares components like weight sharing, pooling stages, and competitive nonlinearities with earlier approaches but focuses on new methods for learning optimal feature-detecting cells in intermediate stages of the hierarchical network. We show that principles of sparse coding, which were previously mostly applied to the initial feature detection stages, can also be employed to obtain optimized intermediate complex features. We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy. The approach explicitly enforces symmetry constraints like translation invariance on the feature set. This leads to a dimension reduction in the search space of optimal features and allows determining more efficiently the basis representatives, which achieve a sparse decomposition of the input. We analyze the quality of the learned feature representation by investigating the recognition performance of the resulting hierarchical network on object and face databases. We show that a hierarchy with features learned on a single object data set can also be applied to face recognition without parameter changes and is competitive with other recent machine learning recognition approaches. To investigate the effect of the interplay between sparse coding and processing nonlinearities, we also consider alternative feedforward pooling nonlinearities such as presynaptic maximum selection and sum-of-squares integration. The comparison shows that a combination of strong competitive
MENENTUKAN PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL CONDITIONAL MEAN VARIANCE
Directory of Open Access Journals (Sweden)
I GEDE ERY NISCAHYANA
2016-08-01
Full Text Available When the returns of stock prices show the existence of autocorrelation and heteroscedasticity, then conditional mean variance models are suitable method to model the behavior of the stocks. In this thesis, the implementation of the conditional mean variance model to the autocorrelated and heteroscedastic return was discussed. The aim of this thesis was to assess the effect of the autocorrelated and heteroscedastic returns to the optimal solution of a portfolio. The margin of four stocks, Fortune Mate Indonesia Tbk (FMII.JK, Bank Permata Tbk (BNLI.JK, Suryamas Dutamakmur Tbk (SMDM.JK dan Semen Gresik Indonesia Tbk (SMGR.JK were estimated by GARCH(1,1 model with standard innovations following the standard normal distribution and the t-distribution. The estimations were used to construct a portfolio. The portfolio optimal was found when the standard innovation used was t-distribution with the standard deviation of 1.4532 and the mean of 0.8023 consisting of 0.9429 (94% of FMII stock, 0.0473 (5% of BNLI stock, 0% of SMDM stock, 1% of SMGR stock.
Maintenance modeling and optimization integrating human and material resources
Energy Technology Data Exchange (ETDEWEB)
Martorell, S., E-mail: smartore@iqn.upv.e [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Villamizar, M.; Carlos, S. [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Sanchez, A. [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia (Spain)
2010-12-15
Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization (MOP) problem where reliability, availability, maintainability and cost (RAM+C) act as decision criteria and maintenance strategies (i.e. maintenance tasks intervals) act as the only decision variables. However the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources (human and material) available to implement such strategies. Thus, the effect of the necessary resources on RAM+C needs to be modeled and accounted for in formulating the MOP affecting the set of objectives and constraints. In this paper RAM+C models to explicitly address the effect of human resources and material resources (spare parts) on RAM+C criteria are proposed. This extended model allows accounting for explicitly how the above decision criteria depends on the basic model parameters representing the type of strategies, maintenance intervals, durations, human resources and material resources. Finally, an application case is performed to optimize the maintenance plan of a motor-driven pump equipment considering as decision variables maintenance and test intervals and human and material resources.
Validation, Optimization and Simulation of a Solar Thermoelectric Generator Model
Madkhali, Hadi Ali; Hamil, Ali; Lee, HoSung
2017-12-01
This study explores thermoelectrics as a viable option for small-scale solar thermal applications. Thermoelectric technology is based on the Seebeck effect, which states that a voltage is induced when a temperature gradient is applied to the junctions of two differing materials. This research proposes to analyze, validate, simulate, and optimize a prototype solar thermoelectric generator (STEG) model in order to increase efficiency. The intent is to further develop STEGs as a viable and productive energy source that limits pollution and reduces the cost of energy production. An empirical study (Kraemer et al. in Nat Mater 10:532, 2011) on the solar thermoelectric generator reported a high efficiency performance of 4.6%. The system had a vacuum glass enclosure, a flat panel (absorber), thermoelectric generator and water circulation for the cold side. The theoretical and numerical approach of this current study validated the experimental results from Kraemer's study to a high degree. The numerical simulation process utilizes a two-stage approach in ANSYS software for Fluent and Thermal-Electric Systems. The solar load model technique uses solar radiation under AM 1.5G conditions in Fluent. This analytical model applies Dr. Ho Sung Lee's theory of optimal design to improve the performance of the STEG system by using dimensionless parameters. Applying this theory, using two cover glasses and radiation shields, the STEG model can achieve a highest efficiency of 7%.
Maintenance modeling and optimization integrating human and material resources
International Nuclear Information System (INIS)
Martorell, S.; Villamizar, M.; Carlos, S.; Sanchez, A.
2010-01-01
Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization (MOP) problem where reliability, availability, maintainability and cost (RAM+C) act as decision criteria and maintenance strategies (i.e. maintenance tasks intervals) act as the only decision variables. However the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources (human and material) available to implement such strategies. Thus, the effect of the necessary resources on RAM+C needs to be modeled and accounted for in formulating the MOP affecting the set of objectives and constraints. In this paper RAM+C models to explicitly address the effect of human resources and material resources (spare parts) on RAM+C criteria are proposed. This extended model allows accounting for explicitly how the above decision criteria depends on the basic model parameters representing the type of strategies, maintenance intervals, durations, human resources and material resources. Finally, an application case is performed to optimize the maintenance plan of a motor-driven pump equipment considering as decision variables maintenance and test intervals and human and material resources.
Optimization of Regional Geodynamic Models for Mantle Dynamics
Knepley, M.; Isaac, T.; Jadamec, M. A.
2016-12-01
The SubductionGenerator program is used to construct high resolution, 3D regional thermal structures for mantle convection simulations using a variety of data sources, including sea floor ages and geographically referenced 3D slab locations based on seismic observations. The initial bulk temperature field is constructed using a half-space cooling model or plate cooling model, and related smoothing functions based on a diffusion length-scale analysis. In this work, we seek to improve the 3D thermal model and test different model geometries and dynamically driven flow fields using constraints from observed seismic velocities and plate motions. Through a formal adjoint analysis, we construct the primal-dual version of the multi-objective PDE-constrained optimization problem for the plate motions and seismic misfit. We have efficient, scalable preconditioners for both the forward and adjoint problems based upon a block preconditioning strategy, and a simple gradient update is used to improve the control residual. The full optimal control problem is formulated on a nested hierarchy of grids, allowing a nonlinear multigrid method to accelerate the solution.
WE-D-BRE-04: Modeling Optimal Concurrent Chemotherapy Schedules
International Nuclear Information System (INIS)
Jeong, J; Deasy, J O
2014-01-01
Purpose: Concurrent chemo-radiation therapy (CCRT) has become a more common cancer treatment option with a better tumor control rate for several tumor sites, including head and neck and lung cancer. In this work, possible optimal chemotherapy schedules were investigated by implementing chemotherapy cell-kill into a tumor response model of RT. Methods: The chemotherapy effect has been added into a published model (Jeong et al., PMB (2013) 58:4897), in which the tumor response to RT can be simulated with the effects of hypoxia and proliferation. Based on the two-compartment pharmacokinetic model, the temporal concentration of chemotherapy agent was estimated. Log cell-kill was assumed and the cell-kill constant was estimated from the observed increase in local control due to concurrent chemotherapy. For a simplified two cycle CCRT regime, several different starting times and intervals were simulated with conventional RT regime (2Gy/fx, 5fx/wk). The effectiveness of CCRT was evaluated in terms of reduction in radiation dose required for 50% of control to find the optimal chemotherapy schedule. Results: Assuming the typical slope of dose response curve (γ50=2), the observed 10% increase in local control rate was evaluated to be equivalent to an extra RT dose of about 4 Gy, from which the cell-kill rate of chemotherapy was derived to be about 0.35. Best response was obtained when chemotherapy was started at about 3 weeks after RT began. As the interval between two cycles decreases, the efficacy of chemotherapy increases with broader range of optimal starting times. Conclusion: The effect of chemotherapy has been implemented into the resource-conservation tumor response model to investigate CCRT. The results suggest that the concurrent chemotherapy might be more effective when delayed for about 3 weeks, due to lower tumor burden and a larger fraction of proliferating cells after reoxygenation
Research on NC laser combined cutting optimization model of sheet metal parts
Wu, Z. Y.; Zhang, Y. L.; Li, L.; Wu, L. H.; Liu, N. B.
2017-09-01
The optimization problem for NC laser combined cutting of sheet metal parts was taken as the research object in this paper. The problem included two contents: combined packing optimization and combined cutting path optimization. In the problem of combined packing optimization, the method of “genetic algorithm + gravity center NFP + geometric transformation” was used to optimize the packing of sheet metal parts. In the problem of combined cutting path optimization, the mathematical model of cutting path optimization was established based on the parts cutting constraint rules of internal contour priority and cross cutting. The model played an important role in the optimization calculation of NC laser combined cutting.
Comparison of Two Identification Models Used in Adaptive Control of Continuous-Stirred Tank Reactor
Directory of Open Access Journals (Sweden)
Vojtesek Jiri
2016-01-01
Full Text Available The goal of this paper is to compare two identification methods – continuous-time and discrete-time. The continuous-time identification model is more accurate but not very suitable for on-line identification. This disadvantage was overcome with the use of differential filters. On the other hand, discrete-time identification model has is more suitable for identification but less accurate. Compromise can be found in the delta model as a special type of the discrete-time model parameters of which are related to the sampling period. The adaptive approach is based on the choice of the External Linear Model, parameters of which are identified recursively which satisfies the adaptivity of this system. Proposed control strategy was applied on the mathematical model of the Continuous Stirred-Tank reactor as a typical nonlinear lumped-parameters system used in the industry.
Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory
Dees, Patrick D.; Zwack, Matthew R.; Edwards, Stephen; Steffens, Michael
2016-01-01
It has been well documented that decisions made in the early stages of Conceptual and Pre-Conceptual design commit up to 80% of total Life-Cycle Cost (LCC) while engineers know the least about the product they are designing [1]. Once within Preliminary and Detailed design however, making changes to the design becomes far more difficult to enact in both cost and schedule. Primarily this has been due to a lack of detailed data usually uncovered later during the Preliminary and Detailed design phases. In our current budget-constrained environment, making decisions within Conceptual and Pre-Conceptual design which minimize LCC while meeting requirements is paramount to a program's success. Within the arena of launch vehicle design, optimizing the ascent trajectory is critical for minimizing the costs present within such concerns as propellant, aerodynamic, aeroheating, and acceleration loads while meeting requirements such as payload delivered to a desired orbit. In order to optimize the vehicle design its constraints and requirements must be known, however as the design cycle proceeds it is all but inevitable that the conditions will change. Upon that change, the previously optimized trajectory may no longer be optimal, or meet design requirements. The current paradigm for adjusting to these updates is generating point solutions for every change in the design's requirements [2]. This can be a tedious, time-consuming task as changes in virtually any piece of a launch vehicle's design can have a disproportionately large effect on the ascent trajectory, as the solution space of the trajectory optimization problem is both non-linear and multimodal [3]. In addition, an industry standard tool, Program to Optimize Simulated Trajectories (POST), requires an expert analyst to produce simulated trajectories that are feasible and optimal [4]. In a previous publication the authors presented a method for combatting these challenges [5]. In order to bring more detailed information
Optimal Filtering in Mass Transport Modeling From Satellite Gravimetry Data
Ditmar, P.; Hashemi Farahani, H.; Klees, R.
2011-12-01
Monitoring natural mass transport in the Earth's system, which has marked a new era in Earth observation, is largely based on the data collected by the GRACE satellite mission. Unfortunately, this mission is not free from certain limitations, two of which are especially critical. Firstly, its sensitivity is strongly anisotropic: it senses the north-south component of the mass re-distribution gradient much better than the east-west component. Secondly, it suffers from a trade-off between temporal and spatial resolution: a high (e.g., daily) temporal resolution is only possible if the spatial resolution is sacrificed. To make things even worse, the GRACE satellites enter occasionally a phase when their orbit is characterized by a short repeat period, which makes it impossible to reach a high spatial resolution at all. A way to mitigate limitations of GRACE measurements is to design optimal data processing procedures, so that all available information is fully exploited when modeling mass transport. This implies, in particular, that an unconstrained model directly derived from satellite gravimetry data needs to be optimally filtered. In principle, this can be realized with a Wiener filter, which is built on the basis of covariance matrices of noise and signal. In practice, however, a compilation of both matrices (and, therefore, of the filter itself) is not a trivial task. To build the covariance matrix of noise in a mass transport model, it is necessary to start from a realistic model of noise in the level-1B data. Furthermore, a routine satellite gravimetry data processing includes, in particular, the subtraction of nuisance signals (for instance, associated with atmosphere and ocean), for which appropriate background models are used. Such models are not error-free, which has to be taken into account when the noise covariance matrix is constructed. In addition, both signal and noise covariance matrices depend on the type of mass transport processes under
Fractional and multivariable calculus model building and optimization problems
Mathai, A M
2017-01-01
This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...
Energy Technology Data Exchange (ETDEWEB)
Na, Man Gyun [Department of Nuclear Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759 (Korea, Republic of)]. E-mail: magyna@chosun.ac.kr; Upadhyaya, Belle R. [Department of Nuclear Engineering, University of Tennessee, Knoxville, TN 37996-2300 (United States)
2006-11-15
In this work, a model predictive control method combined with fuzzy identification, is applied to the design of the thermoelectric (TE) power control in the SP-100 space reactor. The future TE power is predicted by using the fuzzy model identified by a subtractive clustering method of a fast and robust algorithm. The objectives of the proposed fuzzy model predictive controller are to minimize both the difference between the predicted TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives are subject to maximum and minimum control drum angle and maximum drum angle variation speed. The genetic algorithm that is effective in accomplishing multiple objectives is used to optimize the fuzzy model predictive controller. A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed controller. The results of numerical simulations to check the performance of the proposed controller show that the TE generator power level controlled by the proposed controller could track the target power level effectively, satisfying all control constraints.
Subspace identification of Hammer stein models using support vector machines
International Nuclear Information System (INIS)
Al-Dhaifallah, Mujahed
2011-01-01
System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.
Talent identification model for sprinter using discriminant factor
Kusnanik, N. W.; Hariyanto, A.; Herdyanto, Y.; Satia, A.
2018-01-01
The main purpose of this study was to identify young talented sprinter using discriminant factor. The research was conducted in 3 steps including item pool, screening of item pool, and trial of instruments at the small and big size of samples. 315 male elementary school students participated in this study with mean age of 11-13 years old. Data were collected by measuring anthropometry (standing height, sitting height, body mass, and leg length); testing physical fitness (40m sprint for speed, shuttle run for agility, standing broad jump for power, multistage fitness test for endurance). Data were analyzed using discriminant factor. The result of this study found that there were 5 items that selected as an instrument to identify young talented sprinter: sitting height, body mass, leg length, sprint 40m, and multistage fitness test. Model of Discriminant for talent identification in sprinter was D = -24,497 + (0,155 sitting height) + (0,080 body mass) + (0,148 leg length) + (-1,225 Sprint 40m) + (0,563 MFT). The conclusion of this study: instrument tests that have been selected and discriminant model that have been found can be applied to identify young talented as a sprinter.
Optimizing DNA assembly based on statistical language modelling.
Fang, Gang; Zhang, Shemin; Dong, Yafei
2017-12-15
By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selected. Based on statistical language model, which is a probability distribution P(s) over strings S that attempts to reflect how frequently a string S occurs as a sentence, the most commonly used parts will be selected. Then, a dynamic programming algorithm was designed to figure out the solution of maximum probability. The algorithm optimizes the results of a genetic design based on a grammatical model and finds an optimal solution. In this way, redundant operations can be reduced and the time and cost required for conducting biological experiments can be minimized. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Systemic Model for Optimal Regulation in Public Service
Directory of Open Access Journals (Sweden)
Lucica Matei
2006-05-01
Full Text Available The current paper inscribes within those approaching the issue of public services from the interdisciplinary perspective. Public service development and imposing standards of efficiency and effectiveness, as well as for citizens’ satisfaction bring in front line the systemic modelling and establishing optimal policies for organisation and functioning of public services. The issue under discussion imposes an interface with powerful determinations of social nature. Consequently, the most adequate modelling might be that with a probabilistic and statistic nature. The fundamental idea of this paper, that obviously can be broadly developed, starts with assimilating the way of organisation and functioning of a public service with a waiting thread, to which some hypotheses are associated concerning the order of provision, performance measurement through costs or waiting time in the system etc. We emphasise the openness and dynamics of the public service system, as well as modelling by turning into account the statistic knowledge and researches, and we do not make detailed remarks on the cybernetic characteristics of this system. The optimal adjustment is achieved through analysis on the feedback and its comparison with the current standards or good practices.
Optimization models using fuzzy sets and possibility theory
Orlovski, S
1987-01-01
Optimization is of central concern to a number of discip lines. Operations Research and Decision Theory are often consi dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been "hard" or "crisp", i. e. the solutions were considered to be either fea sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob lems. This is particularly true if the problem under considera tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na tural language has to be...
An optimal decision making model for supporting week hospital management.
Conforti, Domenico; Guerriero, Francesca; Guido, Rosita; Cerinic, Marco Matucci; Conforti, Maria Letizia
2011-03-01
Week Hospital is an innovative inpatient health care organization and management, by which hospital stay services are planned in advance and delivered on week-time basis to elective patients. In this context, a strategic decision is the optimal clinical management of patients, and, in particular, devising efficient and effective admission and scheduling procedures, by tackling different requirements such as beds' availability, diagnostic resources, and treatment capabilities. The main aim is to maximize the patient flow, by ensuring the delivery of all clinical services during the week. In this paper, the optimal management of Week Hospital patients is considered. We have developed and validated an innovative integer programming model, based on clinical resources allocation and beds utilization. In particular, the model aims at scheduling Week Hospital patients' admission/discharge, possibly reducing the length of stay on the basis of an available timetable of clinical services. The performance of the model has been evaluated, in terms of efficiency and robustness, by considering real data coming from a Week Hospital Rheumatology Division. The experimental results have been satisfactory and demonstrate the effectiveness of the proposed approach.
Directory of Open Access Journals (Sweden)
Roger Skjetne
2004-01-01
Full Text Available Complete nonlinear dynamic manoeuvering models of ships, with numerical values, are hard to find in the literature. This paper presents a modeling, identification, and control design where the objective is to manoeuver a ship along desired paths at different velocities. Material from a variety of references have been used to describe the ship model, its difficulties, limitations, and possible simplifications for the purpose of automatic control design. The numerical values of the parameters in the model is identified in towing tests and adaptive manoeuvering experiments for a small ship in a marine control laboratory.
An Efficient Framework Model for Optimizing Routing Performance in VANETs
Zulkarnain, Zuriati Ahmad; Subramaniam, Shamala
2018-01-01
Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED). PMID:29462884
Optimization of Forward Wave Modeling on Contemporary HPC Architectures
Energy Technology Data Exchange (ETDEWEB)
Krueger, Jens [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Micikevicius, Paulius [NVIDIA, Santa Clara, CA (United States); Williams, Samuel [Fraunhofer ITWM, Kaiserslautern (Germany)
2012-07-20
Reverse Time Migration (RTM) is one of the main approaches in the seismic processing industry for imaging the subsurface structure of the Earth. While RTM provides qualitative advantages over its predecessors, it has a high computational cost warranting implementation on HPC architectures. We focus on three progressively more complex kernels extracted from RTM: for isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) media. In this work, we examine performance optimization of forward wave modeling, which describes the computational kernels used in RTM, on emerging multi- and manycore processors and introduce a novel common subexpression elimination optimization for TTI kernels. We compare attained performance and energy efficiency in both the single-node and distributed memory environments in order to satisfy industry’s demands for fidelity, performance, and energy efficiency. Moreover, we discuss the interplay between architecture (chip and system) and optimizations (both on-node computation) highlighting the importance of NUMA-aware approaches to MPI communication. Ultimately, our results show we can improve CPU energy efficiency by more than 10× on Magny Cours nodes while acceleration via multiple GPUs can surpass the energy-efficient Intel Sandy Bridge by as much as 3.6×.
Kim, Ju Seok; Kang, Sun Hyung; Moon, Hee Seok; Lee, Eaum Seok; Kim, Seok Hyun; Sung, Jae Kyu; Lee, Byung Seok; Jeong, Hyun Yong; Chung, Woo Suk
2015-10-01
All present guidelines regarding surveillance intervals after index colonoscopy are based on optimal bowel preparation. However, the appropriate timing of repeat colonoscopy after suboptimal bowel preparation is not clear. To determine the appropriate timing of repeat colonoscopy following index colonoscopy with suboptimal bowel preparation. The medical records of patients who underwent colonoscopy over 5 years were retrospectively analyzed. Index colonoscopy was defined as the first colonoscopy in patients who underwent the procedure at least twice during the study period. Bowel preparation quality was classified as optimal, fair, or poor. The overall adenoma detection rate was 39.1% (95% confidence interval [CI], 38.0-40.1%), but the detection rate depended significantly on bowel preparation quality (p preparation (p 2.19-6.16) preparation relative to optimal preparation; however, no difference was observed at surveillance intervals >2 years. Bowel preparation quality significantly affects AMR. Colonoscopy should be repeated within 2 years in patients with suboptimal bowel preparation at index colonoscopy.
Off-road vehicle dynamics analysis, modelling and optimization
Taghavifar, Hamid
2017-01-01
This book deals with the analysis of off-road vehicle dynamics from kinetics and kinematics perspectives and the performance of vehicle traversing over rough and irregular terrain. The authors consider the wheel performance, soil-tire interactions and their interface, tractive performance of the vehicle, ride comfort, stability over maneuvering, transient and steady state conditions of the vehicle traversing, modeling the aforementioned aspects and optimization from energetic and vehicle mobility perspectives. This book brings novel figures for the transient dynamics and original wheel terrain dynamics at on-the-go condition.
Modeling and optimization of parallel and distributed embedded systems
Munir, Arslan; Ranka, Sanjay
2016-01-01
This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability.