Quality of indoor air (IAQ) is one of considerable concerns of today. Its evaluation through measurement is highly requested, but difficult. The reason is the numerosity of influencing factors involved as well as sign...
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(纸本)9789897582844
Quality of indoor air (IAQ) is one of considerable concerns of today. Its evaluation through measurement is highly requested, but difficult. The reason is the numerosity of influencing factors involved as well as significant temporal and spatial variability of IAQ. In this work, we proposed a sensor system with multi-point sampling for this purpose. It is based on semiconductor gas sensor. The measurements were performed in a concert hall. The measurement procedure included sensor exposure to gas samples delivered from four samplingpoints, interchangeably with purified air for sensor regeneration. The obtained results show that the sensor system with multi-point sampling is a promising concept for indoor air monitoring. It was demonstrated that the system is applicable to determine the influence of occupants on IAQ. It is possible, because human beings release VOCs, which are measurable by semiconductor gas sensors. Sensor regeneration plays crucial role in the system operation. For achieving valuable results it is necessary to apply sensor signal pre-processing, which consist in baseline correction.
In general, sampling strategy plays a very important role in metamodel based design optimization, especially when computationally expensive simulations are involved in the optimization process. The research on new opt...
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In general, sampling strategy plays a very important role in metamodel based design optimization, especially when computationally expensive simulations are involved in the optimization process. The research on new optimization methods with less samplingpoints and higher convergence speed receives great attention in recent years. In this paper, a multi-point sampling method based on kriging (MPSK) is proposed for improving the efficiency of global optimization. The sampling strategy of this method is based on a probabilistic distribution function converted from the expected improvement (EI) function. It can intelligently draw appropriate new samples in an area with certain probability according to corresponding EI values. Besides, three strategies are also proposed to speed up the sequential sampling process and the corresponding convergence criterions are put forward to stop the searching process reasonably. In order to validate the efficiency of this method, it is tested by several numerical benchmark problems and applied in two engineering design optimization problems. Moreover, an overall comparison between the proposed method and several other typical global optimization methods has been made. Results show that the higher global optimization efficiency of this method makes it particularly suitable for design optimization problems involving computationally expensive simulations.
The computational efficiency and accuracy of the global solution are the main performance indicators of an optimization algorithm to solve the structural and multidisciplinary optimization problems. The Kriging-based ...
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The computational efficiency and accuracy of the global solution are the main performance indicators of an optimization algorithm to solve the structural and multidisciplinary optimization problems. The Kriging-based optimization algorithm can satisfy certain engineering requirements by applying the single-point sequence sampling method. However, this conventional algorithm does not efficiently apply the parallel performance of high-performance multi-core computers. This study aims to propose a global optimization strategy based on the Kriging surrogate model and parallel computing depending on the multi-peak characteristics of the expect improvement (EI) function. The proposed method searches out the locations of multiple peaks of the EI function by introducing a so-calledP-EI function;it then simultaneously includes multiple samplingpoints located nearby these peaks. Furthermore, an efficient design domain reduction technique is applied to improve the accuracy of the global solution. When compared with the traditional Kriging-based methods, the proposed method can effectively obtain multiple peaks of the EI function without solving complex expressions of the high-dimensional EI function exhibiting a joint probability density distribution. The parallel computation ability, global performance, and solution accuracy of our method are validated via typical test functions and structural optimization problems.
In this paper, we present a new global optimization algorithm SGOP for computationally intensive black-box problems. Considering that multiple surrogates concurrently used in an optimization process can have more robu...
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In this paper, we present a new global optimization algorithm SGOP for computationally intensive black-box problems. Considering that multiple surrogates concurrently used in an optimization process can have more robust performance in most cases, a Pareto-based multi-point sampling strategy is presented to improve iterative efficiency. Ideally, a group of samples having best predictive values on all the surrogates and meanwhile keeping better space-filling feature are most appropriate to be selected in each cycle. Therefore, a four-objective optimization formula is presented, where Kriging, radial basis function, quadratic response surface and a sampling density function are defined as objective functions, respectively. The non-dominated sorting strategy is used to capture the Pareto solutions of the multi-objective problem and the new promising samples are adaptively chosen from their Pareto solutions set to drive the optimization cycle. Moreover, a dynamic monitor is presented to check the premature convergence. Once the trigger is activated, the search will focus on unexplored area. SGOP can not only build a reasonable balance between global exploration and local exploitation, but also has remarkable advantages in sampling efficiency. Finally, the new algorithm is tested on 17 benchmark cases and compared with several existing algorithms. The results show SGOP's superior performance and strong robustness. Besides, SGOP is used for the shape optimization of a blended-wing-body underwater glider (BWBUG), and the lift-drag-ratio gets remarkable improvement. (C) 2021 Elsevier B.V. All rights reserved.
Because different tree parameters are of differing importance, and have different variability, efficiency in sampling would suggest that some of the principle variables be subsampled. One convenient way to do this is ...
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Because different tree parameters are of differing importance, and have different variability, efficiency in sampling would suggest that some of the principle variables be subsampled. One convenient way to do this is to sample different numbers of items at the same sample locations. This paper is a review of some current techniques in subsampling for measured values, especially with Variable Plot sampling, but including Fixed Plot and 3P sampling as well.
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