Energy management of heating, ventilating and air-conditioning (HVAC systems is a primary concern in building projects, since the energy consumption in electricity has the highest percentage in HVAC among all building...
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Energy management of heating, ventilating and air-conditioning (HVAC systems is a primary concern in building projects, since the energy consumption in electricity has the highest percentage in HVAC among all building services installations and electric appliances. Without sacrifice of thermal comfort, to reset the suitable operating parameters, such as the chilled water temperature and supply air temperature, would have energy saving with immediate effect. For the typical commercial building projects, it is not difficult to acquire the reference settings for efficient operation. However, for some special projects, due to the specific design and control of the HVAC system, conventional settings may not be necessarily energy-efficient in daily operation. In this paper, the simulation-optimization approach was proposed for the effective energy management of HVAC system. Due to the complicated interrelationship of the entire HVAC system, which commonly includes the water side and air side systems, it is necessary to suggest optimum settings for different operations in response to the dynamic cooling loads and changing weather conditions throughout a year. A metaheuristic simulation-EP (evolutionary programming) coupling approach was developed using evolutionary programming, which can effectively handle the discrete, non-linear and highly constrained optimization problems, such as those related to HVAC systems. The effectiveness of this simulation-EP coupling suite was demonstrated through the establishment of a monthly optimum reset scheme for both the chilled water and supply air temperatures of the HVAC installations of a local project. This reset scheme would have a saving potential of about 7% as compared to the existing operational settings, without any extra cost. (C) 2005 Elsevier B.V. All rights reserved.
This essay begins with discussion of four relatively recent works which are representative of major themes and preoccupations in Artificial Life Art: 'Propagaciones' by Leo Nunez;'Sniff' by Karolina So...
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This essay begins with discussion of four relatively recent works which are representative of major themes and preoccupations in Artificial Life Art: 'Propagaciones' by Leo Nunez;'Sniff' by Karolina Sobecka and Jim George;'Universal Whistling Machine' by Marc Boehlen;and 'Performative Ecologies' by Ruari Glynn. This essay is an attempt to contextualise these works by providing an overview of the history and forms of Artificial Life Art as it has developed over two decades, along with some background in the ideas of the Artificial Life movement of the late 1980s and 1990s.1.
This paper proposes an improved evolutionary programming (EP) method with deterministic mutation factor for on line PID parameters optimization of hydro-turbine governing systems. The mutation factors are usually gene...
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This paper proposes an improved evolutionary programming (EP) method with deterministic mutation factor for on line PID parameters optimization of hydro-turbine governing systems. The mutation factors are usually generated with Gaussian or Cauchy random series in conventional evolutionary programming algorithms. Considering the difficulties of on line optimal parameters settings resulting from nonlinear time-variant hydro-turbine governing systems, this paper introduces deterministic chaos dynamics into the mutation operation of EP and provides a deterministic chaotic mutation evolutionary programming (DCMEP) method. The improved method develops the traditional concept that implements mutation operation with a fixed random distribution using a quasi-random deterministic way to generate the mutation factor. The test result of the two real hydro-turbine governing systems shows that the improved method can optimize the PID parameters efficiently, and the system has the characteristics of stability;low overshoot level and fast response. (c) 2005 Elsevier Ltd. All rights reserved.
Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing opera...
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Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing operations. To model consumer responses to direct marketing, this study proposes Bayesian networks learned by evolutionary programming. Using a large direct marketing data set, we tested the endogeneity bias in the recency, frequency, monetary value (RFM) variables using the control function approach;compared the results of Bayesian networks with those of neural networks, classification and regression tree (CART), and latent class regression;and applied a tenfold cross-validation. The results suggest that Bayesian networks have distinct advantages over the other methods in accuracy of prediction, transparency of procedures, interpretability of results, and explanatory insight. Our findings lend strong support to Bayesian networks as a robust tool for modeling consumer response and other marketing problems and for assisting management decision making.
The application of a powerful evolutionary optimization technique for the estimation of intrinsic formation constants describing geologically relevant adsorption reactions at mineral surfaces is introduced. We illustr...
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The application of a powerful evolutionary optimization technique for the estimation of intrinsic formation constants describing geologically relevant adsorption reactions at mineral surfaces is introduced. We illustrate the optimization power of a simple Genetic Algorithm (GA) for forward (aqueous chemical speciation calculations) and inverse (calibration of Surface Complexation Models, SCMs) modeling problems of varying degrees of complexity, including problems where conventional deterministic derivative-based root-finding techniques such as Newton-Raphson, implemented in popular programs such as FITEQL, fail to converge or yield poor data fits upon convergence. Subject to sound a priori physical-chemical constraints, adequate solution encoding schemes, and simple GA operators, the GA conducts an exhaustive probabilistic search in a broad solution space and finds a suitable solution regardless of the input values and without requiring sophisticated GA implementations (e.g., advanced GA operators, parallel genetic programming). The drawback of the GA approach is the large number of iterations that must be performed to obtain a satisfactory solution. Nevertheless, for computationally demanding problems, the efficiency of the optimization can be greatly improved by combining heuristic GA optimization with the Newton-Raphson approach to exploit the power of deterministic techniques after the evolutionary-driven set of potential solutions has reached a suitable level of numerical viability. Despite the computational requirements of the GA, its robustness, flexibility, and simplicity make it a very powerful, alternative tool for the calibration of SCMs, a critical step in the generation of a reliable thermodynamic database describing adsorption equilibria. The latter is fundamental to the forward modeling of the adsorption behavior of minerals and geologically based adsorbents in hydro-geological settings (e.g., aquifers, pore waters, water basins) and/or in engineered
This paper essentially aims to propose a new EP based algorithm for solving the ED problem. The ED problem is solved using EP with system lambda as decision variable and power mismatch as fitness function. The algorit...
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This paper essentially aims to propose a new EP based algorithm for solving the ED problem. The ED problem is solved using EP with system lambda as decision variable and power mismatch as fitness function. The algorithm is made fast through judicious modifications in initialization of the parent population, offspring generation and selection of the normal distribution curve. The proposed modifications reduce the search region progressively and generate only effective offsprings. The proposed algorithm is tested on a number of sample systems with quadratic cost function and also on a 10-unit system with piecewise quadratic cost function. The computational results reveal that the proposed algorithm has an excellent convergence characteristic and is superior to other EP based methods in many respects. Copyright (c) 2005 John Wiley & Sons, Ltd.
evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro...
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evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro-mutation (MM), local linear bisection search (LBS) and crossover operators for global optimization. The MM operator is designed to explore the whole search space and the LBS operator to exploit the neighborhood of the solution. Simulated annealing is adopted to prevent premature convergence. The performance of the proposed algorithm is assessed by numerical experiments on 12 benchmark problems. Combined with MM, the effectiveness of various local search operators is also studied. (c) 2004 Elsevier B.V. All rights reserved.
Resolution capability of any optical imaging system is limited by residual aberrations as well as diffraction effects. Overcoming this fundamental limit is called super-resolution. Several new paradigms for super-reso...
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Resolution capability of any optical imaging system is limited by residual aberrations as well as diffraction effects. Overcoming this fundamental limit is called super-resolution. Several new paradigms for super-resolution in optical systems use 'a posteriori' digital image processing. In these ventures the three-dimensional point spread function (PSF) of the lens plays a key role in image acquisition. A straightforward tailoring of the PSF can be performed by appropriate pupil plane filtering. With a brief review of the state-of-art in this research area, this paper dwells upon the inverse problem of global optimization of the pupil function by phase filtering in accordance with the desired PSF.
This paper presents a constrained active power reschedule using immune system for static security enhancement. The study aims to evaluate the static security when a power transmission system is subjected to multi-cont...
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ISBN:
(纸本)9780769539744
This paper presents a constrained active power reschedule using immune system for static security enhancement. The study aims to evaluate the static security when a power transmission system is subjected to multi-contingencies. Voltage stability improvement is achieved by performing active power rescheduling in which the optimal value is determined using artificial immune system (AIS). The method was test on IEEE 30-Bus RTS system and results have been compared with evolutionary programming (EP) indicating that AIS outperformed EP.
Resolution capability of any optical imaging system is limited by residual aberrations as well as diffraction effects. Overcoming this fundamental limit is called super-resolution. Several new paradigms for super-reso...
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Resolution capability of any optical imaging system is limited by residual aberrations as well as diffraction effects. Overcoming this fundamental limit is called super-resolution. Several new paradigms for super-resolution in optical systems use 'a posteriori' digital image processing. In these ventures the three-dimensional point spread function (PSF) of the lens plays a key role in image acquisition. A straightforward tailoring of the PSF can be performed by appropriate pupil plane filtering. With a brief review of the state-of-art in this research area, this paper dwells upon the inverse problem of global optimization of the pupil function by phase filtering in accordance with the desired PSF.
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