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Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Strategic Study of CAE 2010, Volume 12, Issue 3,   Pages 77-81

Abstract: The experiments show that the method gets higher recognition rate than other linear and kernel-based nonlineardimensionality reduction algorithm, and is robust to data aliasing.

Keywords： manifold learning     Laplacian eigenmaps     nonlinear dimensionality reduction     low dimensional manifold     MMW    

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A MATLAB code for the material-field series-expansion topology optimization method

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 3,   Pages 607-622 doi: 10.1007/s11465-021-0637-3

Abstract: description and the finite element discretization, and greatly reduces the number of design variables after dimensionalityreduction.

Keywords： implementation     topology optimization     material-field series-expansion method     bounded material field     dimensionalityreduction    

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Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 224-238 doi: 10.1007/s11709-022-0812-6

Abstract: The consolidation coefficient of soil (Cv) is a crucial parameter used for the design of structures leaned on soft soi. In general, the Cv is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine Cv via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the Cv of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R2 > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy ( R2 = 0.9869), the difference compared with other models was negligible (i.e., R2 = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict Cv based on the basic soil parameters.

Keywords： soil consolidation coefficient     machine learning     random forest     Relief    

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Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 569-575 doi: 10.1007/s11709-021-0735-7

Abstract: ., the urban development is moving toward “pan-dimensionality” and “individual ubiquity”.

Keywords： digital age     urban design     multiple objectives     human-computer interaction     pan-dimensionality     individual-ubiquity    

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Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research Article

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Engineering 2021, Volume 7, Issue 12,   Pages 1725-1731 doi: 10.1016/j.eng.2020.05.028

Abstract:

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods. Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas, including investment analysis, image identification, and population genetic structure analysis. However, these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency. Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparse reduced rank regression and subspace assisted regression with row sparsity, which hold potential to meet the above demands and thus improve the interpretability of regression models. We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods. For different application scenarios, we also provide selection suggestions based on predictive ability and variable selection accuracy. Finally, to demonstrate the practical value of these methods in the field of microbiome research, we applied our chosen method to real population-level microbiome data, the results of which validated our method. Our method extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression, and could pave the way for novel discoveries in microbiome and related research fields.

Keywords： Multivariate regression methods     Reduced rank regression     Sparsity     Dimensionality reduction     Variable    

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A new feature selection method for handling redundant information in text classification None

You-wei WANG, Li-zhou FENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2,   Pages 221-234 doi: 10.1631/FITEE.1601761

Abstract: Feature selection is an important approach to dimensionality reduction in the field of text classification

Keywords： Feature selection     Dimensionality reduction     Text classification     Redundant features     Support vector machine    

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The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Strategic Study of CAE 2009, Volume 11, Issue 2,   Pages 82-87

Abstract:

The data dimensionality reduction is the main method that can enhanceLocal Linear Embedding algorithm (LLE) is an effective technique for nonlinear dimensionality reductionCompared with other dimensionality reduction algorithms, the advantage of the local Linear EmbeddingEmbedding, the algorithm can select optimal parameter and regulate the distance among data set after data dimensionalityreduction, so as to improve efficiency of detection of outliers.

Keywords： manifold learning     detection of outliers     high dimensional data     dimensionality reduction     outliers    

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Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation None

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1352-1361 doi: 10.1631/FITEE.1601512

Abstract: As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original featuresDDMA method consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality

Keywords： Tool condition monitoring     Manifold learning     Dimensionality reduction     Diffusion mapping analysis     Intrinsic    

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New nonlinear stiffness actuator with predefined torque‒deflection profile

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 1, doi: 10.1007/s11465-022-0721-3

Abstract: A nonlinear stiffness actuator (NSA) could achieve high torque/force resolution in low stiffness range

Keywords： compliant actuator     nonlinear stiffness actuator     nonlinear spring     predefined torque−deflection profile    

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Fuel optimal control of parallel hybrid electric vehicles

PU Jinhuan, YIN Chenliang, ZHANG Jianwu

Frontiers of Mechanical Engineering 2008, Volume 3, Issue 3,   Pages 337-342 doi: 10.1007/s11465-008-0057-7

Abstract: To overcome the problem of numerical DP dimensionality, an algorithm to restrict the exploring region

Keywords： mathematical     Comparison     computational complexity     dimensionality     corresponding    

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Outliers detection algorithm based on nonlinear data transformation

Xu Xuesong,Zhang Xu,Song Dongming,Zhang Hong,Liu Fnegyu

Strategic Study of CAE 2008, Volume 10, Issue 9,   Pages 74-78

Abstract:

The data dimension reduction is the main method that can enhance thedisadvantages of the classical outlier mining algorithm in the paper.In this paper, we can transform nonlinearlarge-scale data into linear data in the feature space,and introduce a nonlinear data transformationthat the algorithm is not only used to detect linear separable outlier data,but also used to detect nonlinear

Keywords： dimension reduction     kernel function     principal component     outliers    

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Mean wind load induced incompatibility in nonlinear aeroelastic simulations of bridge spans

Zhitian ZHANG

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3,   Pages 605-617 doi: 10.1007/s11709-018-0499-x

Abstract: Mean wind response induced incompatibility and nonlinearity in bridge aerodynamics is discussed, where the mean wind and aeroelastic loads are applied simultaneously in time domain. A kind of incompatibility is found during the simultaneous simulation of the mean wind and aeroelastic loads, which leads to incorrect mean wind structural responses. It is found that the mathematic expectations (or limiting characteristics) of the aeroelastic models are fundamental to this kind of incompatibility. In this paper, two aeroelastic models are presented and discussed, one of indicial-function-denoted (IF-denoted) and another of rational-function-denoted (RF-denoted). It is shown that, in cases of low wind speeds, the IF-denoted model reflects correctly the mean wind load properties, and results in correct mean structural responses; in contrast, the RF-denoted model leads to incorrect mean responses due to its nonphysical mean properties. At very high wind speeds, however, even the IF-denoted model can lead to significant deviation from the correct response due to steady aerodynamic nonlinearity. To solve the incompatibility at high wind speeds, a methodology of subtraction of pseudo-steady effects from the aeroelastic model is put forward in this work. Finally, with the method presented, aeroelastic nonlinearity resulted from the mean wind response is investigated at both moderate and high wind speeds.

Keywords： bridge     aerodynamics     nonlinear     aeroelastic model     Pseudo-steady     mean wind loads    

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General expression for linear and nonlinear time series models

Ren HUANG, Feiyun XU, Ruwen CHEN

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 1,   Pages 15-24 doi: 10.1007/s11465-009-0015-z

Abstract: However, some nonlinear factors are within the practical system; thus, it is difficult to fit the modelThis paper proposes a general expression for linear and nonlinear auto-regressive time series modelsexperiments show that the GNAR model can accurately approximate to the dynamic characteristics of the most nonlinear

Keywords： linear and nonlinear     autoregressive model     system identification     time series analysis    

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Evaluation of transmissibility for a class of nonlinear passive vibration isolators

Z. K. PENG, Z. Q. LANG, G. MENG

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 4,   Pages 401-409 doi: 10.1007/s11465-012-0349-9

Abstract: concept of Output Frequency Response Functions (OFRFs) is applied to represent the transmissibility of nonlinearcharacteristic parameters is derived for a wide class of nonlinear isolators that have nonlinear anti-symmetricdamping characteristics and a comprehensive pattern about how the nonlinear damping characteristic parametersThese conclusions are of significant importance in the analysis and design of the nonlinear vibrationisolators with nonlinear anti-symmetric damping.

Keywords： nonlinear vibration     volterra series     Output Frequency Response Functions (OFRFs)     nonlinear damping     vibration    

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Numerical analysis of nonlinear dynamic behavior of earth dams

Babak EBRAHIMIAN

Frontiers of Structural and Civil Engineering 2011, Volume 5, Issue 1,   Pages 24-40 doi: 10.1007/s11709-010-0082-6

Abstract: The numerical investigation employs a fully nonlinear dynamic finite difference analysis incorporating

Keywords： earth dam     numerical     nonlinear response     dynamic analysis     earthquake     dam height    

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Title Author Date Type Operation

Passive millimeter-wave target recognition based on Laplacian eigenmaps

Luo Lei,Li Yuehua,Luan Yinghong

Journal Article

A MATLAB code for the material-field series-expansion topology optimization method

Journal Article

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

Journal Article

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Journal Article

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Journal Article

A new feature selection method for handling redundant information in text classification

You-wei WANG, Li-zhou FENG

Journal Article

The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Journal Article

Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation

Yi-xiang HUANG, Xiao LIU, Cheng-liang LIU, Yan-ming LI

Journal Article

New nonlinear stiffness actuator with predefined torque‒deflection profile

Journal Article

Fuel optimal control of parallel hybrid electric vehicles

PU Jinhuan, YIN Chenliang, ZHANG Jianwu

Journal Article

Outliers detection algorithm based on nonlinear data transformation

Xu Xuesong,Zhang Xu,Song Dongming,Zhang Hong,Liu Fnegyu

Journal Article

Mean wind load induced incompatibility in nonlinear aeroelastic simulations of bridge spans

Zhitian ZHANG

Journal Article

General expression for linear and nonlinear time series models

Ren HUANG, Feiyun XU, Ruwen CHEN

Journal Article

Evaluation of transmissibility for a class of nonlinear passive vibration isolators

Z. K. PENG, Z. Q. LANG, G. MENG

Journal Article

Numerical analysis of nonlinear dynamic behavior of earth dams

Babak EBRAHIMIAN

Journal Article