Efficient design of SIW filters with knowledge‐embedded space mapping technique

This article presents an optimization technique for the design of substrate‐integrated waveguide (SIW) filters using knowledge‐embedded space mapping. An effective coarse model is proposed to represent the SIW filter. The proposed coarse model can be analyzed in the available commercial software ADS. The embedded knowledge includes not only formulas but also extracted design curves, which help to build the mapping between the coarse and fine models. The effectiveness of the proposed method is demonstrated through a design example of a six‐pole SIW filter. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Optimization of friction stir welding using space mapping and manifold mapping—an initial study of thermal aspects

The aim of this paper is to optimize a thermal model of a friction stir welding process by finding optimal welding parameters. The optimization is performed using space mapping and manifold mapping techniques in which a coarse model is used along with the fine model to be optimized. Different coarse models are applied and the results and computation time are compared to gradient based optimization using the full model. It is found that the use of space and manifold mapping reduces the computational cost significantly due to the fact that fewer function evaluations and no fine model gradient information is required.     

Manifold mapping optimization with or without true gradients

This paper deals with the space mapping optimization algorithms in general and with the manifold mapping technique in particular. The idea of such algorithms is to optimize a model with a minimum number of each objective function evaluations using a less accurate but faster model. In this optimization procedure, fine and coarse models interact at each iteration in order to adjust themselves in order to converge to the real optimum. The manifold mapping technique guarantees mathematically this convergence but requires gradients of both fine and coarse model. Approximated gradients can be used for some cases but are subject to divergence. True gradients can be obtained for many numerical model using adjoint techniques, symbolic or automatic differentiation. In this context, we have tested several manifold mapping variants and compared their convergence in the case of real magnetic device optimization.     

An image-based texture mapping technique for apparel products exhibition and interior design

In this paper, we propose an image-based texture mapping technique for textile products exhibition that does not require geometric representation of 3D models. Under this technique, a texture grid is built interactively for a target area in an original image. This grid acts as the intermediate space between planar space and texture space. The texture coordinate for each pixel in the target area can be calculated based on this grid, and the 3D effect can be successfully realized by further fine adjustment of the grid. This technique can be applied in apparel products exhibition and interior design.     

基于响应面和空间映射技术的模面设计优化

阐述了响应面近似模型和空间映射技术用于快速模面设计优化的基本原理．为避免数值噪声干扰和求解隐函数敏度,提出通过多项式响应面方法构造真实目标和约束的逼近曲面,以光滑响应进行全局最优;并利用空间映射技术的代理模型确定新的响应面及其梯度,经精细模型修正优化方向和设计子域,使计算成本降低．算法结合有限元模拟实施,通过对金钣拉延成形和回弹补偿的分析证明,该算法具有很高的效率和鲁棒性,适用于模面设计优化．     

Design and optimization of cross‐coupled substrate integrated waveguide filters using space mapping method

This work presents an efficient method for the design of substrate integrated waveguide (SIW) filters. The proposed design approach is based on a combined use of equivalent circuit model of a filter and a space mapping technique. A reduced number of full‐wave evaluations are needed, leading to a reduced optimization time. A novel SIW filter with improved stop‐band characteristic using cross‐coupling has been proposed. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:360–366, 2014.     

基于字典学习的非线性降维方法

目前,众多的数据降维（Dimensionality reduction, DR）方法（如经典的PCA（Principle component analysis）, ISOMAP（Isometric mapping））能够使降维后的数据保留原始信号的重要特征,但是从降维后的数据中很好地恢复出原始信号仍旧是一个挑战.近年来,稀疏表示（Sparse representation, SR）在信号重构研究中受到广泛关注,信号可以利用过完备字典中少数原子的线性组合来描述.本文提出一种基于字典学习的非线性降维方法.从高维输入信号到低维特征的降维过程中,期望一些重要的几何特征（内积、距离和夹角）得以保留,同时又能够从低维数据中恢复出原始信号.为达此目的,本文采用CDL（Concentrated dictionary learning）算法训练一个字典对（高维字典D和低维字典P）,使高维原始信号的能量能够聚集于低维子空间中.字典D用来获取稀疏表示系数,字典P是D的直接降维采样,CDL算法能够保证P聚集D中的大部分能量.这样,信号的降维与恢复问题就转变为字典对的训练问题,信号的降维即为从D到P的能量保留过程.实验表明:CDL可在RIP（Restricted isomery property）条件的限制之外具有一定的信号重建能力,能在更低的维度条件下恢复图像,优于传统的压缩感知方法.此外,在噪声较大的情况下,CDL图像压缩效果优于JPEG2000.     

基于C-MKE 模型的交互界面设计

为了满足用户对交互界面设计的感性需求,提出了基于C-MKE 模型的交互界面 设计方法。首先应用感性工学(KE)理论构架中的类目层次法(CC)推论交互界面设计项目,并在 与现有交互界面对比分析的基础上建立设计变量空间。其次,通过感性意象认知实验构建感性 意象认知空间,运用数学模型方程法(MM)实现交互界面设计变量与感性意象的映射模型,进一 步通过T 检验法验证了该模型的有效性。最后依据C-MKE 模型推导出苗族银饰文化传播APP 首页界面设计的参考性结论,并进行了界面的创新设计。     

Dual‐band dielectric resonator bandstop filters

This article presents the design and implementation of a new asymmetric dual‐band bandstop filter using TE01δ mode dielectric resonator (DR) technology. The coupling matrix is generated by frequency transformation technique applied to advance filtering functions for direct‐coupled asymmetric dual‐band bandstop filter in cul‐de‐sac configuration. The proposed approach provides control of all the major parameters such as center frequencies, intercavity couplings, and input/output couplings of filter independently in both the designated bands. The dual‐band DR filters (2 × 2) pole, with return loss = 15 dB and percentage rejection bandwidth of 1.6 and 0.6% in two bands, at 9.96 and 10.15 GHz, respectively, are designed, built, and tested. The measured and simulated results are in good agreement over the desired band. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:282–288, 2015.     

Separable soft shadow mapping

We propose an efficient technique for rendering visually plausible real-time soft shadows in screen space. First, we propose a novel blocker estimation technique based on a separable filter. Second, our technique performs a separable Gaussian blur in screen space over the hard shadows produced by the standard shadow mapping technique. Although blurring the hard shadows with a separable filter was done before in the literature using bilateral filtering, we use an alternative approach that minimizes artifacts. Since separated calculation is not possible for all cases, we provide data reutilization criteria based on two user-defined error thresholds called \(\alpha \) and \(\beta \). As a consequence of using separable approaches for both stages of the light visibility estimation, our technique is able to improve rendering performance, especially when high-resolution shadow maps and filtering kernels are used.     

Space mapping optimization of handset antennas considering EM effects of mobile phone components and human body

To meet antenna design specifications under realistic conditions, electromagnetic coupling effects between the antenna and its environment must be considered. In this work, an efficient antenna design optimization methodology that considers the influence of the human head and main mobile handset components on the antenna performance is presented. The computational optimization time is dramatically reduced by exploiting a Broyden‐based input space mapping (SM) algorithm. Both coarse and fine models required for the SM algorithm are based on the finite‐element method and are implemented in the same simulator; simplifying the modeling process. However, our coarse model does not consider any object of the actual operational environment. In spite of that and other simplifications applied to the coarse model, the proposed optimization scheme is able to find a solution that meets the specifications in a realistic environment by performing an extremely small number of expensive fine model simulations. Our practical illustration opens up the feasibility of using this CAD methodology to optimize other RF devices that operate in close proximity to objects that affect its desired response, as it is the case for many wearable devices. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:121–128, 2016.     

Space mapping optimization of waveguide filters using finite element and mode‐matching electromagnetic simulators

For the first time in design optimization of microwave circuits, the aggressive space mapping (SM) optimization technique is applied to automatically align electromagnetic (EM) models based on hybrid mode‐matching/network theory simulations with models based on finite‐element (FEM) simulations. SM optimization of an H‐plane resonator filter with rounded corners illustrates the advantages as well as the challenges of the approach. The parameter extraction phase of SM is given special attention. The impact of selecting responses and error functions on the convergence and uniqueness of parameter extraction is discussed. A statistical approach to parameter extraction involving 𝓁₁ and penalty concepts facilitates a key requirement by SM for uniqueness and consistency. A multipoint parameter extraction approach to sharpening the solution uniqueness and improving the SM convergence is also introduced. Once the mapping is established, the effects of manufacturing tolerances are rapidly estimated with the FEM accuracy. SM has also been successfully applied to optimize waveguide transformers using two hybrid mode‐matching/network theory models: a coarse model using very few modes and a fine model using many modes to represent discontinuities. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 54–70, 1999.     

Neural inverse space mapping (NISM) optimization for EM‐based microwave design

We present neural inverse space mapping (NISM) optimization for electromagnetics‐based design of microwave structures. The inverse of the mapping from the fine to the coarse model parameter spaces is exploited for the first time in a space mapping algorithm. NISM optimization does not require up‐front EM simulations, multipoint parameter extraction, or frequency mapping. It employs a simple statistical parameter extraction procedure. The inverse of the mapping is approximated by a neural network whose generalization performance is controlled through a network growing strategy. We contrast our new algorithm with neural space mapping (NSM) optimization. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 136–147, 2003.     

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.     

Optimization of the new Saab 9-3 exposed to impact load using a space mapping technique

The aim of this work is to illustrate how a space mapping technique using surrogate models together with response surfaces can be used for structural optimization of crashworthiness problems. To determine the response surfaces, several functional evaluations must be performed and each evaluation can be computationally demanding. The space mapping technique uses surrogate models, i.e. less costly models, to determine these surfaces and their associated gradients. The full model is used to correct the gradients from the surrogate model for the next iteration. Thus, the space mapping technique makes it possible to reduce the total computing time needed to find the optimal solution. First, two analytical functions and one analytical structural optimization problem are presented to exemplify the idea of space mapping and to compare the efficiency of space mapping to traditional response surface optimization. Secondly, a sub-model of a complete vehicle finite element (FE) model is used to study different objective functions in vehicle crashworthiness optimization. Finally, the space mapping technique is applied to a structural optimization problem of a large industrial FE vehicle model, consisting of 350.000 shell elements and a computing time of 100 h. In this problem the intrusion in the passenger compartment area was reduced by 32% without compromising other crashworthiness parameters.     

Space mapping algorithm with improved convergence properties for microwave design optimization

Space mapping is one of the most efficient techniques for microwave design optimization. Still, it is well known that space mapping algorithms may suffer from convergence issues, which are consequences of certain fundamental features. In many cases, the space mapping algorithm quickly finds an acceptable solution, but then falls into oscillations with respect to the design variables and/or the objective function value; as a consequence, there are no clear criteria for terminating the optimization process. In this article, we investigate some techniques for improving the convergence properties of the space mapping algorithm, which are based on the general convergence results for such algorithms. Our approach is verified using several microwave design optimization problems. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Reanalysis-based space mapping method,an alternative optimization way for expensive simulation-based problems

The computational cost of evaluation in the design procedure is an essential bottleneck for simulation-based applications. To handle this problem, space mapping(SM) algorithm and reanalysis method are integrated to improve the efficiency of optimization without loss of accuracy. In the suggested method, the SM algorithm is used to construct the projection between coarse and fine spaces. In the coarse space, the reanalysis method is used to analyze finite element (FE) models. Compared with surrogate assisted evaluation, the accuracy of reanalysis method is significantly improved due to introduction of equilibrium equations. Generally, compared with other SM algorithms, coarse and fine spaces are based on solvers instead of models. Due to the high accurate reanalysis method, the reanalysis-based SM algorithm is easier to converge. Moreover, to integrate geometry and finite element models easily, a B-rep based reanalysis method is also introduced. To verify the performance of the suggested method, two examples have been carried out by using reanalysis based space mapping method. According to the results, the efficiency of optimization procedure is improved significantly.     

基于局部敏感布隆过滤器的工业物联网隐性异常检测

工业物联网（IIoT）系统中的传感器由于持续使用和正常磨损出现损坏,导致收集和记录的传感数据出现隐性异常。为解决该问题,提出一种基于局部敏感Bloom Filter（LSBF）模型的异常检测算法LSBFAD。首先利用基于空间划分的快速Johnson-Lindenstrauss变换（SP-FJLT）对数据进行哈希映射,然后采用相互竞争（MC）策略进行除噪,最后利用0-1编码构建Bloom Filter。在SIFT、MNIST和FMA三个基准数据集上进行的仿真实验中,LSBFAD算法的误报率（FAR）均低于10%。实验结果表明,基于LSBF的异常检测算法与当前主流的异常检测算法相比,具有较高的检测率（RD）和较低的误报率,可有效应用于IIoT数据的异常检测。     

Space exploration and global optimization for computationally intensive design problems: a rough set based approach

Modern engineering design problems often involve computation-intensive analysis and simulation processes. Design optimization based on such processes is desired to be efficient, informative and transparent. This work proposes a rough set based approach that can identify multiple sub-regions in a design space, within which all of the design points are expected to have a performance value equal to or less than a given level. The rough set method is applied iteratively on a growing sample set. A novel termination criterion is also developed to ensure a modest number of total expensive function evaluations to identify these sub-regions and search for the global optimum. The significance of the proposed method is twofold. First, it provides an intuitive method to establish the mapping from the performance space to the design space, i.e. given a performance level, its corresponding design region(s) can be identified. Such a mapping could be potentially used to explore and visualize the entire design space. Second, it can be naturally extended to a global optimization method. It also bears potential for more broad application to problems such as metamodeling-based design and robust design optimization. The proposed method was tested with a number of test problems and compared with a few well-known global optimization algorithms.