Computational Design of a nature-inspired architectural structure using the concepts of self-similar and random fractals

Rian, Iasef Md; Asayama, Shuichi

doi:10.1016/j.autcon.2016.03.010

Cited by 46 publications

(24 citation statements)

References 22 publications

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“…The random fractal images obtained by the Diamond-Square algorithm are similar to the texture images of clouds, thus, the fractal dictionary constructed from this image can efficiently represent cirrus. Next, the Diamond-Square algorithm [43] is introduced to generate random fractal images.…”

Section: Random Fractalmentioning

confidence: 99%

“…The construction of fractal dictionary can be generated according to random fractal images. The fractal dictionary can be constructed from the random fractal image obtained by diamond square algorithm [43]. Then, the fractal dictionary D s was studied and sparsely coded by the k-clustering with singular value decomposition (KSVD) method, and the sparsely represented images were obtained.…”

Section: Introductionmentioning

confidence: 99%

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Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

et al. 2020

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In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.

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Section: Random Fractalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

et al. 2020

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“…The basic concept of SFS technique is to explore an area based on the property of an entity (object or quantity). The fractal theories resembles self‐similarity of the patterns in nature, such as trees, snowflakes, animal coloration patterns, crystals, Romanesco broccoli, lungs, river networks, blood vessels, and DNA . SFS attempts to exchange information among all participants, which increases the chances of finding the global minima in the group to speed up convergence and accuracy.…”

Section: Stochastic Fractal Search Techniquementioning

confidence: 99%

“…The fractal theories resembles self-similarity of the patterns in nature, such as trees, snowflakes, animal coloration patterns, crystals, Romanesco broccoli, lungs, river networks, blood vessels, and DNA. 30,31 SFS attempts to exchange information among all participants, which increases the chances of finding the global minima in the group to speed up convergence and accuracy. SFS algorithm 29 constitutes of diffusing process and updating process.…”

Section: Stochastic Fractal Search Techniquementioning

confidence: 99%

“…A new metaheuristic algorithm presented by Hamid Salimi uses a mathematical concept called the fractal inspired by the natural phenomenon of growth, known as stochastic fractal search (SFS). SFS offers least error compared with the globally optimum solution within a minimal number of iterations, thus offering an improvement in terms of accuracy, convergence time, and simplicity of operations . So far, it is rare to find any literature that dealt with the concept of random fractals for optimization of controller gains used in multi‐units with diverse sources.…”

Section: Introductionmentioning

confidence: 99%

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Automatic generation control of an interconnected CCGT-thermal system using stochastic fractal search optimized classical controllers

Saha

Saikia

2018

Int Trans Electr Energ Syst

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Summary During the past few decades, frequency regulation of combined cycle gas turbine (CCGT) has become an interesting issue of research. A larger power system model experiences many challenges when base‐loaded CCGT unit is incorporated in it. This article presents the load frequency regulation of CCGT model in conjunction with thermal units in an unequal 3‐area interconnected system. The control strategy of CCGT plant includes temperature and air flow controllers that are designed on the basis of minimization of area control error. The main drive of proposed control strategy is to successfully tackle the system dynamics during step and realistic load disturbances. The eigenvalues of proposed system commensurate with dynamic stability. A new metaheuristic algorithm known as stochastic fractal search has been applied for simultaneous optimization of classical controller (proportional [PI], proportional‐integral [PI] and proportional‐integral‐derivative [PID] gains, and other parameters such as governor speed regulation parameter [Ri], frequency bias [Bi]). Comprehensive simulations are performed to ensure stable system dynamics with easier and cheaper realization of governor, and higher values of Bi in respective areas and robustness of stochastic fractal search optimized PID controller.

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FracShell: From Fractal Surface to a Lattice Shell Structure

Rian

2019

Lecture Notes in Civil Engineering

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Computational Design of a nature-inspired architectural structure using the concepts of self-similar and random fractals

Cited by 46 publications

References 22 publications

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

Automatic generation control of an interconnected CCGT-thermal system using stochastic fractal search optimized classical controllers

FracShell: From Fractal Surface to a Lattice Shell Structure

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