The contribution deals with a deterministic-based structural optimisation (DBSO). The instroductory part of the paper covers a short overview of optimisation algorithms applicable to deterministic-based problems, general DBSO formulation and a target function(s) pattern for structural design. The following part gives attention to particular problem of general RC (reinforced concrete) cross-sectional design subjected to normal force and bending moments (ULS, i.e. ultimate limit state), where basic cross-sectional characteristics (cross-sectional dimensions, steel bars profiles and types of materials constitute an optimisation space with discrete attributes. The target function (including economical and ecological aspects) and principle problem solution(s) is defined and an illlustrative numerical example of a simple rectangular cross-section design is presented. The solution approach is further augmented to RC frame structures problems and a numerical example of a collector tube design is presented. and Obsahuje seznam literatury
Reliability-Based Structural Optimisation (RBSO) incorporates probabilistic structural reliability analysis into structural optimisation. A sample definition of an RBSO problem and its solution are presented for the optimisation of an RC cross-section, which is subjected to combinations of normal force and bending moments. The presented RBSO algorithm utilizes the LHS (Latin Hypercube Sampling) approximate simulation method for reliability computations. Numerical results for the particular data set are presented. and Obsahuje seznam literatury
This paper presents the entire formulation of longitudinal reinforcement minimisation in a concrete structure of known sections and shape under loading by normal force and bending moment. Constraint conditions are given by the conditions of structure reliability in accordance with the relevant codes for ultimate strength and applicability of the sections specified by a designer. Linearization of the non-linear formulation is described, and possibilities of applying linear programming algorithms are discussed. The functioning of the process described is demonstrated on a plane frame structure design. and Obsahuje seznam literatury
In this paper we consider the optimal control of both operators and parameters for uncertain systems. For the optimal control and identification problem, we show existence of an optimal solution and present necessary conditions of optimality.
The optimal operation of an electrical power supply subsystem results from respect for the effectiveness criterion for the control of an industrial enterprise. It is essential for the operation of the Váh Cascade hydro power plants - a significant Slovak hydro-energy system. The fundamental principle of this operation is the potential hydropower utilization of water courses in addition to planning the control of the whole electrical system. and Obsahuje seznam literatury
In the minimization of the number of subtours made by the insertion head of an SMD placement machine a variant of the network flow problem arose. In a network with <span class="tex">n</span> vertices and <span class="tex">m</span> arcs a set <span class="tex">F</span> of arcs (parametrized arcs) is given. The task is to find a flow of a given size such that the maximum of flow values along the arcs from <span class="tex">F</span> is minimized. This problem can be solved by a sequence of maximum flow computations in modified networks where the capacities of the parametrized arcs are successively set to an increasing sequence of target parameter values. We show that it suffices to consider at most <span class="tex">O(|F|)</span> different target values and so this approach leads to a strongly polynomial algorithm consisting of at most <span class="tex">O(|F|)</span> maximum flow computations.
A well known problem in unit selection speech synthesis is designing the join and target function sub-costs and optimizing their corresponding weights so that they reflect the human listeners' preferences. To achieve this we propose a procedure where an objective criterion for optimal speech unit selection is used. The objective criterion for tuning the cost function weights is based on automatic speech recognition results. In order to demonstrate the effectiveness of the proposed method listening tests with 31 naive listeners were performed. The experimental results have shown that the proposed method improves speech quality and intelligibility. In order to evaluate the quality of synthesized speech the unit selection speech synthesis system is compared with two other Croatian speech synthesis systems with voices built using the same recorded speech corpus. One of these voices was built with the Festival speech synthesis system using the statistical parametric method and the other is a diphone concatenation based text-to-speech system. The comparison is based on subjective tests using MOS (mean opinion score) evaluation. The system using the proposed method used for cost function weights optimization performs better than other compared systems according to the subjective tests.
The predictive performance of Echo State neural networks were optimized for electrical load forecasting and compared to the results achieved by competitors in the worldwide Eunite Competition #1. The test data used were the actual results of the competition, attached to a specific region. A regular adaptation of an \textit{Echo State }neural network was optimized by adapting the weights of the dynamic reservoir through Anti-Hebbian learning, and the weights from input and output neurons to the hidden neurons were optimized using the Metropolis algorithm. The results achieved with such an optimized Echo State neural network would gain a strong second place within the Eunite competition.
Combinatorial optimization problems are extensively solved by using neural networks. Hopfield-Tank model is used to solve Traveling Salesman Problem and many NP-Hard Problems. This paper describes a neural network optimizer/scheduler that optimizes a solution for a highly complicated version of N Queens Problem (NQP), i.e. N+1 non-threatening Queens on a N*N chessboard with an intermediate pawn on it. Both synchronous and asynchronous methods of updating of the neurons have been applied for optimization of N+1 Queens Problem. Computer simulations are used to confirm the results. The proposed neural network is attracted to optimized solution or finds the global minima in 90% of the trials. A new rule of initialization, i.e. the proximity rule of initialization has been proposed. Using the proximity rule of initialization the performance of the system is enhanced and the system converges to an optimal solution in much less time. Many novel applications like multiprocessor job scheduling, resource optimization, of the above mentioned algorithm have been proposed. N Queens Problem has been solved by many techniques but no other algorithm exists to solve N+1 QP in the literature. Consequently, the performance of the network is compared with full space search algorithm.
This paper deals with issues related to convenient monitoring of subsidence due to longtime mining activities of Czech Karviná Mine: Lazy plant, using satellite SAR interferometry (InSAR) techniques. It maintains approaches for optimizations of differential InSAR, especially including available filtering possibilities. It was realized that current SAR satellites were not able to appropriately evaluate deep subsidence as it occurs in Czech mining sites. Other issues are related to the presence of dense vegetation. Data from previous and only available L-band SAR satellite ALOS demonstrates its high potential in this area of interest. However, only a few acquisitions are available of the mining site disallowing usage for purposes of continuous monitoring of subsidence in the area. Processing results of InSAR techniques of the Lazy plant are presented in this paper., Milan Lazecký and Eva Jiránková., and Obsahuje bibliografii