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Multi-objective optimization of elastomeric bearings to improve seismic performance of old bridges using eigen analysis and genetic algorithms

100%

Abbadi M. S., Lamdouar N.

Przegląd Naukowy. Inżynieria i Kształtowanie Środowiska

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2021

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tom 30

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nr 4[94]

2

A new morphometrical method for measuring the diameter, length and volume of vessels

100%

Gielecki J., Cytowski J., Gacek W.

Folia Morphologica

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1996

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tom 55

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nr 4

3

Optimization of the impact multi-mass vibration absorbers

75%

Kernytskyy I., Diveyev B., Horbaj O., Hlobchak M., Kopytko M., Zachek O.

Przegląd Naukowy. Inżynieria i Kształtowanie Środowiska

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2017

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tom 26

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nr 3[77]

Optimization of the impact multi-mass vibration absorbers. The problem of attaching dynamic vibration absorber (DVA) to a discrete multi-degree-of-freedom or continuous structure has been outlined in many papers and monographs. An impact damping system can overcome some limitations by impact as the damping medium and impact mass interaction as the damping mechanism. The paper contemplates the provision of DVA with the several of the impact masses. Such originally designed absorbers reduce vibration selectively in maximum vibration mode without introducing vibration in other modes. An impact damper is a passive control device which takes the form of a freely moving mass, constrained by stops attached to the structure under control, i.e. the primary structure. The damping results from the exchange of momentum during impacts between the mass and the stops as the structure vibrates. The paper contemplates the provision of the impact multi-mass DVA’s with masses collisions for additional damping. For some cases of DVA optimization such a design seems more effective than conventional multi-mass DVA with independent mass moving. A technique is developed to give the optimal DVA’s for the elimination of excessive vibration in harmonic stochastic and impact loaded systems.

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Application of genetic algorithms in designing of fuzzy logic controllers

75%

Wierzbicki D.

Annals of Warsaw Agricultural University. Agriculture

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1999

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nr 34 Agric.Eng.

63-69

5

New hardware engine for new operating systems

75%

Ahmadi N., Kaveh M.

International Letters of Natural Sciences

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2013

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tom 05

Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only don’t this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.

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Species distribution models to estimate the deforested area of Picea crassifolia in arid region recently protected: Qilian Mts. National Natural Reserve (China)

63%

Xu Z., Zhao C., Feng Z.

Polish Journal of Ecology

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2012

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tom 60

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nr 3

The earth is now facing the land degradation due to human disturbance, natural habitats were converted to rural and agricultural areas in order to fulfill the increasing demand of human population. The deforestation of Picea crassifolia (Qinghai spruce) forest at Qilian Mts is an example of such disturbance. P. crassifolia is an ecologically and hydrologically important plant species in the northwestern arid area of China. However, the forests have been intensively and extensively deforested. In order to restore the human-disturbed ecosystems, the spatial distribution of P. crassifolia needs to be delineated. This study employed Genetic Algorithm for Rule-set Prediction model (GARP) and Maximum entropy model (Maxent) and four environmental variables (mean temperature of the warmest quarter, precipitation of the wettest quarter, annual solar radiation, topographic wetness index) to predict the potential distribution of P. crassifolia in Qilian Mts. Genetic Algorithm for Rule-set Prediction model (GARP) produces a model of species niches in geographic space based on heterogeneous rule-sets. Maximum entropy model (Maxent) focuses on fitting a probability distribution for occurrence based on the idea that the best explanation to unknown phenomena will maximize the entropy of the probability distribution, subject to the appropriate constraints. The environmental variables were spatially interpolated throughout the entire study area. We used sensitivity-specificity sum maximum approach to select the threshold value. The projected niche space for the mean temperature of the warmest quarter is between 8.5 and 18.1°C; the space for the precipitation of the wettest quarter is between 149 and 245 mm; the space for annual solar radiation is 118–1100×103 wh m–2 and the space for topographic wetness index is between –0.4 and 5.1. The results show that both GARP and Maxent’s models produce acceptable predictions, but the overall comparison shows that GARP prediction is better than Maxent’s; the comparison between the observed distribution and the predicted distribution suggests that 61% (2869 km2) of P. crassifolia forests have been deforested.

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A genetic algorithm how to solve a puzzle and its using in cartography

63%

Bartonek D.

Acta Scientiarum Polonorum. Geodesia et Descriptio Terrarum

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2005

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tom 04

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nr 2

8

Hybrid intelligent system for analysis of project document

63%

Kushnarov O., Lytvynchenko G.

Teka Komisji Motoryzacji i Energetyki Rolnictwa

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2010

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tom 10A

9

A genetic algorithm for automatic map symbols placement

63%

Bartonek D.

Electronic Journal of Polish Agricultural Universities. Series: Geodesy and Cartography

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2003

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tom 06

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nr 1

Genetic algorithms represent an up-to-date method of process optimalization, where other solutions have failed or havent given any satisfactory results. One of these processes is automatic placement of map symbols in such a way so that no symbols should mutually overlay. A genetic algorithm solving this task including an exact formulation and a definition of the initial conditions has been described in this paper. The algorithm efficiency will be tested in diploma works in Institute of Geodesy, Faculty of Civil Engineering, Brno University of Technology.

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Toward the application of AI methods in marine SAR operations

63%

Bednarczyk M., Piotrowski M., Pyrchla J.

Polish Journal of Environmental Studies

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2005

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tom 14

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nr Suppl.1

It is argued that there is the need as well as resources necessary to provide support for the SAR planning and execution process based on Artificial Intelligence methods. In particular, an idea to use evolutionary programming to generate sub-optimal search planning patterns is presented.

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Wykorzystanie algorytmów genetycznych do prognozowania stanów przestrzeni miejskiej w procesie proaktywnego przeciwdziałania zagrożeniom

51%

Bilozor A., Czyza S., Szuniewicz K.

Acta Scientiarum Polonorum. Administratio Locorum

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2013

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tom 12

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nr 1

12

Optimization algorithm for de novo analysis of tandem mass spectrometry data

51%

Kistowski M., Gambin A.

BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology

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2011

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tom 92

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