Highly efficient density-based topology optimization using DCT-based digital image compression

Zhou, Pingzhang; Du, Jiuyu; Li, Zhenhua

doi:10.1007/s00158-017-1840-z

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…Pingzhang Zhou and colleges displayed the reducing of density based topology optimization using DCT 23 . They discussed the information stored in an image is composed of high and low-frequency components and only low-frequency components are needed for the optimization in order to attain high resolution.…”

Section: Discussionmentioning

confidence: 99%

“…They discussed the information stored in an image is composed of high and low-frequency components and only low-frequency components are needed for the optimization in order to attain high resolution. By using such technique they suggested that there is no need to apply additional filters such as density or sensitivity filters as the high-frequency components are already reduced to zero by the application of DCT 23 .…”

Section: Discussionmentioning

confidence: 99%

“…Adaptive Huffman coding for lossless compression was introduced in order to store image information without using the concept of quantization which may lead to a distorted or corrupted image if over compressed 18 . Compression is a process of reducing information and has been used in the field of medicine for many years [19][20][21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

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Computed tomogram image encoding for internet of things based systems

Siddique

Qadri

Mohy-Ud-Din

2018

Int. j. endorsing health sci. res.

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Background: In recent times, transmission of information over a wireless channel is increasing at an exponential rate. Applications based on IOT will exceed all form of data available over the internet, it can be in the form of data based on videos and images which alone size up to 70% of the global traffic. Methodology: In this paper, an encoding technique based on discrete cosine transform (DCT) have been utilized to reduce information of a CT image that is unwanted or a human eye is unable to perceive. For medical images, preserving maximum information in order to properly diagnose any disease is important and for such application, the amount of quantization needed to gain an image with minimum error is to observe the Correlation index (CI). Results: CI at 50% Quality Quantization Table (QQT) for image 1 was found to be 0.9981 and its size reduced to 0.403 MB, it means that the compressed image is 99.8% similar to the original image. But its similarity reduces with the increase in QQT, its size will also decrease by at this point quality will degrade too. Conclusion: In order to store images on the drive, it is imperative to apply a compression technique. In this paper, a compression technique is proposed utilizing a DCT algorithm to reduce an image size with different QQT provided for quality assessment. Each image has a different set of information and to cater their differences this QQT can be manipulated for best possible quality with acceptable size on disk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computed tomogram image encoding for internet of things based systems

Siddique

Qadri

Mohy-Ud-Din

2018

Int. j. endorsing health sci. res.

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“…Transforming design variables of topology optimization into wavelet basis have been explored in [22]. Zhou et al [23] presents a reduced order topology optimization approach using discrete cosine transform and demonstrate its efficiency on 2D problems. Similarly, a topology optimization method has been developed using fourier representations in the form of discrete cosine transforms for compliance minimization problems in [24].…”

Section: Reduced Order Design Optimizationmentioning

confidence: 99%

Sliding Basis Optimization for Heterogeneous Material Design

Ulu¹,

Korneev²,

Ulu³

et al. 2020

Preprint

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We present the sliding basis computational framework to automatically synthesize heterogeneous (graded or discrete) material fields for parts designed using constrained optimization. Our framework uses the fact that any spatially varying material field over a given domain may be parameterized as a weighted sum of the Laplacian eigenfunctions. We bound the parameterization of all material fields using a small set of weights to truncate the Laplacian eigenfunction expansion, which enables efficient design space exploration with the weights as a small set of design variables. We further improve computational efficiency by using the property that the Laplacian eigenfunctions form a spectrum and may be ordered from lower to higher frequencies. Starting the optimization with a small set of weighted lower frequency basis functions we iteratively include higher frequency bases by sliding a window over the space of ordered basis functions as the optimization progresses. This approach allows greater localized control of the material distribution as the sliding window moves through higher frequencies. The approach also reduces the number of optimization variables per iteration, thus the design optimization process speeds up independent of the domain resolution without sacrificing analysis quality. While our method is useful for problems where analytical gradients are available, it is most beneficial when the gradients may not be computed easily (i.e., optimization problems coupled with external black-box analysis) thereby enabling optimization of otherwise intractable design problems. The sliding basis framework is independent of any particular physics analysis, objective and constraints, providing a versatile and powerful design optimization tool for various applications. We demonstrate our approach on graded solid rocket fuel design and multi-material topology optimization applications and evaluate its performance.

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“…This approach renders the use of different meshes by the density methods easier for topological design variables and finite element analysis. Then, similar projection schemes were used to achieve multiresolution topology optimization and the topological design of complex structures . Matsui and Terada, Matsui et al, Rahmatalla and Swan, and Paulino and Le considered the material density field as a continuous distribution interpolated within the finite elements by design variables associated with the element nodes and bilinear shape functions.…”

Section: Introductionmentioning

confidence: 99%

Velocity field level‐set method for topological shape optimization using freely distributed design variables

Wang

Kang

Liu

2019

Numerical Meth Engineering

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Summary The velocity field level‐set topological shape optimization method combines the implicit representation in the standard level‐set method and the capabilities of general mathematical programming algorithms in handling multiple constraints and additional design variables. The key concept is to construct the normal velocity field using basis functions and the velocity design variables at specified points (referred to as velocity knots) in the entire design domain. In this study, the velocity design variables are decoupled from the level‐set grid points. Making use of this property, we can adaptively change the arrangement of the velocity knots as the structural boundary evolves. This provides more design freedom in the optimization and allows for a significant reduction in the number of design variables. Several numerical examples in two‐ and three‐dimensional design domains are presented to demonstrate the robustness and efficiency of the proposed method. We also show that changing the number of velocity knots may implicitly exert certain control on topological complexity and length scale.

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Highly efficient density-based topology optimization using DCT-based digital image compression

Cited by 12 publications

References 7 publications

Computed tomogram image encoding for internet of things based systems

Computed tomogram image encoding for internet of things based systems

Sliding Basis Optimization for Heterogeneous Material Design

Velocity field level‐set method for topological shape optimization using freely distributed design variables

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