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  • 1
    Electronic Resource
    Electronic Resource
    Predicting cumulative sediment depostition in large reservoirs (1990)
    Springer
    Stochastic environmental research and risk assessment 4 (1990), S. 241-251 
    Details
    ISSN: 1436-3259
    Keywords: Sedimentation ; Large Reservoirs ; Markov Chains
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Sediment deposition and its accumulation in a large resorvoir depends on the inflow and reservoir storage content, respectively. Because of this fact it is possible to model the cumulative deposition of sediment as an additive process defined on a bivariate Markov chain. Using the bivariate Markov chain model the mean and variance of the cumulative deposition of John Martin Reservoir, Colorado, U.S.A. are estimated and compared with observed sedimentation data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01543087
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  • 2
    Electronic Resource
    Electronic Resource
    Application of the distributed parameter filter to predict simulated tidal induced shallow water flow (1995)
    Springer
    Stochastic environmental research and risk assessment 9 (1995), S. 13-32 
    Details
    ISSN: 1436-3259
    Keywords: Distributed parameter filter ; shallow water equations ; distributed dynamical systems ; data assimilation ; white Gaussian noise
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Distributed parameter filtering theory is employed for estimating the state variables and associated error covariances of a dynamical distributed system under highly random tidal and meteorological influences. The stochastic-deterministic mathematical model of the physical system under study consists of the shallow water equations described by the momentum and continuity equations in which the external forces such as Coriolis force, wind friction, and atmospheric pressure are considered. White Gaussian noises in the system and measurement equations are used to account for the inherent stochasticity of the system. By using an optimal distributed parameter filter, the information provided by the stochastic dynamical model and the noisy measurements taken from the actual system are combined to obtain an optimal estimate of the state of the system, which in turn is used as the initial condition for the prediction procedure. The approach followed here has numerical approximation carried out at the end, which means that the numerical discretization is performed in the filtering equations, and not in the equations modelling the system. Therefore, the continuous distributed nature of the original system is maintained as long as possible and the propagation of modelling errors in the problem is minimized. The appropriateness of the distributed parameter filter is demonstrated in an application involving the prediction of storm surges in the North Sea. The results confirm excellent filter performance with considerable improvement with respect to the deterministic prediction.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01581756
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  • 3
    Electronic Resource
    Electronic Resource
    A new formulation for the stochastic control of systems with bounded state variables: An application to a single reservoir system (1996)
    Springer
    Stochastic environmental research and risk assessment 10 (1996), S. 167-186 
    Details
    ISSN: 1436-3259
    Keywords: Reservoir stochastic theory ; reliability ; mean ; variance ; indicator function ; storage bounds ; nonlinear programming ; simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract A new formulation is presented for the analysis of reservoir systems synthesizing concepts from the traditional stochastic theory of reservoir storage, moments analysis and reliability programming. The analysis is based on the development of the first and second moments for the stochastic storage state variable. These expressions include terms for the failure probabilities (probabilities of spill or deficit) and consider the storage bounds explicitly. Using this analysis, expected values of the storage state, variances of storage, optimal release policies and failure probabilities — useful information in the context of reservoir operations and design, can be obtained from a nonlinear programming solution. The solutions developed from studies of single reservoir operations on both an annual and monthly basis, compare favorably with those obtained from simulation. The presentation herein is directed to both traditional reservoir storage theorists who are interested in the design of a reservoir and modern reservoir analysts who are interested in the long term operation of reservoirs.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01581462
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  • 4
    Electronic Resource
    Electronic Resource
    An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization (1989)
    Springer
    Stochastic environmental research and risk assessment 3 (1989), S. 17-29 
    Details
    ISSN: 1436-3259
    Keywords: Stochastic optimization ; linear programming ; simplex method ; Karmarkar's method
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP): (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a Micro VAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01543425
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  • 5
    Electronic Resource
    Electronic Resource
    Stochastic partial differential equations in groundwater hydrology (1991)
    Springer
    Stochastic environmental research and risk assessment 5 (1991), S. 239-251 
    Details
    ISSN: 1436-3259
    Keywords: Parameter estimation ; maximum likelihood estimation ; stochastic partial differential equation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Notes: Abstract Part I of this series of two papers (Unny, 1989) dealt with the theoretical derivation of the moment equations for the stochastic partial differential equation in the water table depth forced by stochastic rainfall input. Part I also developed a maximum likelihood estimation procedure for parameter determination. The primary aim of the present manuscript is the application of the parameter estimation procedure to the Borden aquifer, an aquifer designated as an experimental site, where extensive field measurements have been carried out. Estimates of hydraulic conductivity and transmissivity for the Borden aquifer, derived from the maximum likelihood algorithm, have been compared with estimates obtained by “traditional” procedures. The paper also presents the simulated solution of the governing differential equation in the one dimensional problem applied to the Borden aquifer.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01544060
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  • 6
    Electronic Resource
    Electronic Resource
    Maximization of Manufacturing Yield of Systems with Arbitrary Distributions of Component Values (2000)
    Springer
    Annals of operations research 99 (2000), S. 373-383 
    Details
    ISSN: 1572-9338
    Keywords: yield maximization ; design optimization ; asymmetrical PDF
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract This paper presents a general method for maximizing manufacturing yield when the realizations of system components are independent random variables with arbitrary distributions. Design specifications define a feasible region which, in the nonlinear case, is linearized using a first-order approximation. The method attempts to place the given tolerance hypercube of the uncertain parameters such that the area with higher yield lies in the feasible region. The yield is estimated by using the joint cumulative density function over the portion of the tolerance hypercube that is contained in the feasible region. A double-bounded density function is used to approximate various bounded distributions for which optimal designs are demonstrated on a tutorial example. Monte Carlo simulation is used to evaluate the actual yields of optimal designs.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019288220413
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  • 7
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    A new formulation for the stochastic control of systems with bounded state variables: An application to a single reservoir system (1996)
    Springer
    Details
    Publication Date: 1996-08-01
    Print ISSN: 1436-3240
    Electronic ISSN: 1436-3259
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Published by Springer
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  • 8
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    Predicting cumulative sediment depostition in large reservoirs (1990)
    Springer
    Details
    Publication Date: 1990-09-01
    Print ISSN: 1436-3240
    Electronic ISSN: 1436-3259
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Published by Springer
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  • 9
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    Application of the distributed parameter filter to predict simulated tidal induced shallow water flow (1995)
    Springer
    Details
    Publication Date: 1995-03-01
    Print ISSN: 1436-3240
    Electronic ISSN: 1436-3259
    Topics: Architecture, Civil Engineering, Surveying , Energy, Environment Protection, Nuclear Power Engineering , Geography , Geosciences
    Published by Springer
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  • 10
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    Fabrication uncertainties and yield optimization in MEMS tunable capacitors (2008)
    Elsevier
    Details
    Publication Date: 2008-10-01
    Print ISSN: 0924-4247
    Electronic ISSN: 1873-3069
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Elsevier
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