A delicate differential game is formed by two aircraft performing a long-range missile duel. The duel starts for each aircraft with a semiagressive phase until the launch of the missile, when a pure evasion commences....
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The paper presents an optimization method based on optimality criterion for minimum weight design of structures with geometric nonlinear behavior. The nonlinear critical load is determined by finding the load level at...
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This article presents experimental results of the application of an electric field to the blowoff limits of a premixed methane-air flame. The results indicate that, under suitable electrode configuration, electric fie...
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This article presents experimental results of the application of an electric field to the blowoff limits of a premixed methane-air flame. The results indicate that, under suitable electrode configuration, electric fields can be used as a suitable technique in controlling the combustion process.
In conditions of monopolistic position of enterprises, they exhibit unrestrained tendency to increase the prices of their products, without taking care for full utilization of their production capacities. Such a situa...
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In conditions of monopolistic position of enterprises, they exhibit unrestrained tendency to increase the prices of their products, without taking care for full utilization of their production capacities. Such a situation may occur in the planned and as well as the free-market national economy. The decision center is able to counteract this tendency by introducing a proper system of income taxes, which results in the optimal prices providing the maximal profit. Simultaneously, due to the high level of the prime costs of small production series, there exists some threshold value in the relation between profit and production rate. If the production rate is less than this value, it is unprofitable. For reasons mentioned, the problem of enterprise profit optimization in the case of production capacities constrained, is a non-trivial mathematical programming problem. It is a mixed discrete-continuous optimization problem: the decision variables connected with selecting ranges of products are of discrete (zero-one) type: the decision variables associated with the choice of the optimal production rate are continuous. The paper presents an optimization algorithm which can be used to solve this mixed discrete-continuous decision problem. The worked out method can be also applied to other socio-economic decision problems of similar type.
This paper presents the results of a series of sampling experiments to evaluate the small sample properties of several variants of the nonlinear two stage and nonlinear three stage least squares estimators. The analys...
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The paper presents a new, modified Newton-Raphson technique for the fast, model-adaptive identification of delays between two unknown stochastic or deterministic signals. The method combines properties of the gradient...
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The paper presents a new, modified Newton-Raphson technique for the fast, model-adaptive identification of delays between two unknown stochastic or deterministic signals. The method combines properties of the gradient, as well as the Newton-Raphson technique. In contrast to the linear convergence of gradient techniques, it has in this special application the same cubic convergence rate as the Newton-Raphson method combined, however, with the larger range of stability of the gradient method. Further properties are a nearly uniform settling time, independent of the bandwidth and the amplitudes of the measured signals, and under certain assumptions a monotone decreasing error. The algorithm leads straightforwardly to a rather simple, discrete hardware implementation, adjusting a parametric model. Possible applications are in the field of velocity and distance measurement. Extensive simulations for the proposed scheme together with results of known methods are presented.
The useful heat gain of a linear single-axis concentrating solar collector is significantly affected bY the orientation of the collector. The temporal distribution of the heat output, both diurnally and annually, and ...
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The useful heat gain of a linear single-axis concentrating solar collector is significantly affected bY the orientation of the collector. The temporal distribution of the heat output, both diurnally and annually, and the integrated energy output varY with the direction of the collector axis. For a typical insolation sequence, a north to south orientation yields greater annual heat gain while an east to west orientation gives a flatter annual distribution. Selection of the best collector orientation depends on this distribution, the load profile, and the thermal capacitance of the system. The first two factors are considered in the definition of an economic criterion function which is then used to identify the optimum distribution of collectors among all possible orientations.
Lagrangian functions are the basis of many of the more successful methods for nonlinear constraints in optimization calculations. Sometimes they are used in conjunction with linear approximations to the constraints an...
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Lagrangian functions are the basis of many of the more successful methods for nonlinear constraints in optimization calculations. Sometimes they are used in conjunction with linear approximations to the constraints and sometimes penalty terms are included to allow the use of algorithms for unconstrained optimization. Much has been discovered about these techniques during the last eight years and this paper gives a view of the progress and understanding that has been achieved and its relevance to practical algorithms. A particular method is recommended that seems to be more powerful than the author believed to be possible at the beginning of 1976.
A study was conducted to assess the feasibility of performing computerized wing design by numerical optimization. The design program combined a full-potential, inviscid aerodynamic code with a conjugate gradient optim...
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A study was conducted to assess the feasibility of performing computerized wing design by numerical optimization. The design program combined a full-potential, inviscid aerodynamic code with a conjugate gradient optimization algorithm. Three design problems were selected to demonstrate the design technique. The first involved modifying the upper surface of the inboard 50% of a swept wing to reduce the shock drag subject to a constraint on wing volume. The second involved modifying the entire upper surface of the same swept wing (except the tip section) to increase the lift-drag ratio, subject to constraints on wing volume and lift coefficient. The final problem involved modifying the inboard 50% of a low-speed wing to achieve good stall progression. Results from the three cases indicate that the technique is sufficiently accurate to permit substantial improvement in the design objectives.
Federated Learning (FL) has been widely adopted as a distributed machine learning paradigm aiming to derive a global model without transferring local data to the server. In the context of heterogeneous environments ty...
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Federated Learning (FL) has been widely adopted as a distributed machine learning paradigm aiming to derive a global model without transferring local data to the server. In the context of heterogeneous environments typical of many FL deployments, our research has identified the performance oscillation problem in existing FL methods, resulting in slow convergence and severe performance drop. In this paper, we first investigate the global optimizing objective in FL and demonstrate that, due to data heterogeneity and partial client participation, the global updates in a single training epoch may diverge from the intended objectives of conventional FL methods. To address this problem, we introduce a triple-objective decomposition mechanism to decompose the overarching global objective into three distinct local objectives aimed at aligning client gradients. Subsequently, we propose a gradient trajectory smoothing technique known as FedGTS, which refines local updates by estimating a pseudo-gradient leveraging historical global update trajectories. This approach is designed to mitigate performance oscillations and enhance the stability of the learning process. We theoretically demonstrate that our approach reduces variance of local updates and achieves a guaranteed convergence rate. We experimentally show that the proposed method outperforms the baselines with faster convergence and higher accuracy. Extensive experiments validate the effectiveness of the proposed approach across various heterogeneity settings. Our codes are publicly available on GitHub.
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