Low‐cost data‐driven modelling of microwave components using domain confinement and PCA‐based dimensionality reduction

Kozieł, Sławomir; Pietrenko‐Dabrowska, Anna

doi:10.1049/iet-map.2020.0101

Cited by 8 publications

(4 citation statements)

References 37 publications

(67 reference statements)

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“…e principle of PCA is to realize the projection of correlated variables in the principal component space, and the projection Q ′ of Q is obtained by PCA mapping in the PCA subspace to complete the dimensionality reduction of specific sports training images [8].…”

Section: Image Dimensionalitymentioning

confidence: 99%

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[Retracted] Research on Sports Training Action Image Recognition Based on SDN

Wang

Shen

2022

Journal of Mathematics

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Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.

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Section: Image Dimensionalitymentioning

confidence: 99%

“…According to relevant regulations, the angles are 0 ∘ , 45 ∘ , 90 ∘ , and 135 ∘ . When using formula (8) to calculate the image edge response, it is necessary to determine the value of φ in G x′ and G y′ . ere is a close correlation between Gaussian distribution and Gaussian function.…”

Section: Select Adaptivementioning

confidence: 99%

[Retracted] Research on Sports Training Action Image Recognition Based on SDN

Wang

Shen

2022

Journal of Mathematics

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“…In order to improve the efficiency of cloud computing storage data access control, high-dimensional cloud computing is required. Data are stored for dimensionality reduction processing [11,12]. is article mainly uses the principal component analysis method to reduce the dimensionality of cloud computing storage data.…”

Section: Cloud Computing Storage Data Dimensionalitymentioning

confidence: 99%

Cloud Computing Storage Data Access Control Method Based on Dynamic Re-Encryption

Chen

Zeng

Pang

et al. 2021

Security and Communication Networks

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In order to improve data security, ensure user privacy, and solve the problems of low data access control accuracy, long time consumption, and high energy consumption in traditional methods, a cloud computing storage data access control method based on dynamic re-encryption is proposed. The principal component analysis method is used to reduce the dimension of the cloud computing storage data, and the random forest algorithm is further used to classify and process the cloud computing storage data according to the processing results. On the basis of data preprocessing, an access control tree is established to obtain the correlation of data nodes. Finally, the dynamic re-encryption method is used for data security state transformation, and the data access control of cloud computing storage is realized through key generation, encryption, re-encryption key generation, and decryption. The experimental results show that the data access control accuracy of the method in this paper is high, time consumption is small, and energy consumption is small, and it is more suitable for cloud computing systems with huge data and information.

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“…The said designs (also referred to as reference points) have to be optimized beforehand according to the pre-selected values of the design specifications, incurring hefty computational cost. Several attempts to improve the efficacy of the nested kriging modeling technique have been made starting from enhanced sampling [83], through variable-fidelity setup [84], dimensionality reduction [85], and adopting variable-thickness domain [86], up to gradient-enhanced nested kriging [87]. In the latter, sensitivity data derived from the reference designs have been exploited, which made it possible to cut down the number of the database designs by half.…”

Section: Introductionmentioning

confidence: 99%

Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement

Pietrenko-Dabrowska,

Koziel,

Zhang

2023

Electronics

Self Cite

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A variety of surrogate modeling techniques has been utilized in high-frequency design over the last two decades. Yet, the curse of dimensionality still poses a serious challenge in setting up reliable design-ready surrogates of modern microwave components. The difficulty of the modeling task is only aggravated by nonlinearity of circuit responses. Consequently, constructing a practically usable surrogate model, valid across extended ranges of material, geometry, and operational parameters, is far from easy. As a matter of fact, conventional modeling techniques are merely capable of building models for microwave structures featuring a relatively small number of designable parameters within reduced ranges thereof. One possible way of mitigating these obstacles may be the employment of the recently proposed two-stage performance-driven modeling approach. Therein, the surrogate model domain is narrowed down to the section of the space where the vectors of adequate quality are located, thereby permitting significantly reducing the cost of acquiring the training data. Seeking even further cost reduction, this work introduces a novel modeling framework, which exploits problem-specific knowledge extracted from the circuit responses to achieve substantial cost-savings of training data acquisition. In our methodology, the modeling procedure targets response features instead of the complete responses. The response features are the characteristic locations of the circuit response, such as relevant minima or maxima over selected frequency bands. The dependency of the coordinates of the said features on circuit dimensions is considerably less nonlinear than is observed for the complete characteristics, which enables sizable reduction of the data acquisition cost. Numerical validation of our procedure involving three microwave structures corroborates its remarkable efficiency, which allows for setting design-ready surrogates using only a handful of samples.

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Low‐cost data‐driven modelling of microwave components using domain confinement and PCA‐based dimensionality reduction

Cited by 8 publications

References 37 publications

[Retracted] Research on Sports Training Action Image Recognition Based on SDN

[Retracted] Research on Sports Training Action Image Recognition Based on SDN

Cloud Computing Storage Data Access Control Method Based on Dynamic Re-Encryption

Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement

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