Unified Parametrization for the Solutions to the Polynomial Diophantine Matrix Equation and the Generalized Sylvester Matrix Equation

Bin Zhou,Zhi-Bin Yan,Guang-Ren Duan 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.1

The polynomial Diophantine matrix equation and the generalized Sylvester matrix equation are important for controller design in frequency domain linear system theory and time domain linear system theory, respectively. By using the so-called generalized Sylvester mapping, right coprime factorization and Bezout identity associated with certain polynomial matrices, we present in this note a unified parametrization for the solutions to both of these two classes of matrix equations. Moreover, it is shown that solutions to the generalized Sylvester matrix equation can be obtained if solutions to the Diophantine matrix equation are available. The results disclose a relationship between the polynomial Diophantine matrix equation and generalized Sylvester matrix equation that are respectively studied and used in frequency domain linear system theory and time domain linear system theory.

Magnetic biochar from alkali-activated rice straw for removal of rhodamine B from aqueous solution

Zhaogang Ren,Fang Chen,Bin Wang,Zhongxian Song,Ziyu Zhou,Dong Ren 대한환경공학회 2020 Environmental Engineering Research Vol.25 No.4

To address organic dye wastewater, economic and effective adsorbents are required. Here, magnetic biochar from alkali-activated rice straw (AMBC) was successfully synthesized using one-step magnetization and carbonization method. The alkaline activation caused the large specific surface area, high pore volume and abundant oxygen-containing groups of the AMBC, and the magnetization gave the AMBC a certain degree of electropositivity and fast equilibrium characteristics. These characteristics collectively contributed to a relative high adsorption capacity of 53.66 mg g<SUP>−1</SUP> for this adsorbent towards rhodamine B (RhB). In brief, RhB can spontaneously adsorb onto the heterogeneous surface of the AMBC and reach the equilibrium in 60 min. Although the initial pH, ionic strength and other substances of the solution affected the adsorption performance of the AMBC, it could be easily regenerated and reused with considerable adsorption content. Based on the results, H-bonds, π–π stacking and electrostatic interactions were speculated as the primary mechanisms for RhB adsorption onto the AMBC, which was also demonstrated by the FTIR analysis. With the advantageous features of low cost, easy separation, considerable adsorption capacity and favorable stability and reusability, the AMBC would be a potential adsorbent for removing organic dyes from wastewater.

Parallel Multi-task Cascade Convolution Neural Network Optimization Algorithm for Real-time Dynamic Face Recognition

( Bin Jiang ),( Qiang Ren ),( Fei Dai ),( Tian Zhou ),( Guan Gui ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.10

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

A Numerically Efficient Multi-Objective Optimization Algorithm: Combination of Dynamic Taylor Kriging and Differential Evolution

Bin Xia,Baatar, Nyambayar,Ziyan Ren,Chang-Seop Koh IEEE 2015 IEEE transactions on magnetics Vol.51 No.3

<P>A dynamic Taylor Kriging (DTK) is newly developed and combined with a multi-objective differential evolution algorithm to get a numerically efficient multi-objective optimization strategy. In the DTK, basis functions are not predefined but optimally selected so that the fitting error with the given sampling data may be minimized. In the developed multi-objective optimization algorithm, the DTK provides predicted objective function values as an alternative to direct finite-element analysis. The effectiveness of the proposed DTK and multi-objective optimization strategy are verified through applications to analytic example and TEAM 22.</P>

The effects of deletion of cellobiohydrolase genes on carbon source-dependent growth and enzymatic lignocellulose hydrolysis in Trichoderma reesei

Ren Meibin,Wang Yifan,Liu Guoxin,Zuo Bin,Zhang Yuancheng,Wang Yunhe,Liu Weifeng,Liu Xiangmei,Zhong Yaohua 한국미생물학회 2020 The journal of microbiology Vol.58 No.8

The saprophytic fungus Trichoderma reesei has long been used as a model to study microbial degradation of lignocellulosic biomass. The major cellulolytic enzymes of T. reesei are the cellobiohydrolases CBH1 and CBH2, which constitute more than 70% of total proteins secreted by the fungus. However, their physiological functions and effects on enzymatic hydrolysis of cellulose substrates are not sufficiently elucidated. Here, the cellobiohydrolase-encoding genes cbh1 and cbh2 were deleted, individually or combinatively, by using an auxotrophic marker-recycling technique in T. reesei. When cultured on media with different soluble carbon sources, all three deletion strains (Δcbh1, Δcbh2, and Δcbh1Δcbh2) exhibited no dramatic variation in morphological phenotypes, but their growth rates increased apparently when cultured on soluble cellulase-inducing carbon sources. In addition, Δcbh1 showed dramatically reduced growth and Δcbh1Δcbh2 could hardly grew on microcrystalline cellulose (MCC), whereas all strains grew equally on sodium carboxymethyl cellulose (CMC-Na), suggesting that the influence of the CBHs on growth was carbon source-dependent. Moreover, five representative cellulose substrates were used to analyse the influence of the absence of CBHs on saccharification efficiency. CBH1 deficiency significantly affected the enzymatic hydrolysis rates of various cellulose substrates, where acid pre-treated corn stover (PCS) was influenced the least. CBH2 deficiency reduced the hydrolysis of MCC, PCS, and acid pre-treated and delignified corncob but improved the hydrolysis ability of filter paper. These results demonstrate the specific contributions of CBHs to the hydrolysis of different types of biomass, which could facilitate the development of tailor-made strains with highly efficient hydrolysis enzymes for certain biomass types in the biofuel industry.

An Adaptive Optimization Algorithm Based on Kriging Interpolation with Spherical Model and its Application to Optimal Design of Switched Reluctance Motor

Bin Xia,Ziyan Ren,Yanli Zhang,Chang-Seop Koh 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.5

In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.

An analytical analysis of the pullout behaviour of reinforcements of MSE structures

Ren, Feifan,Wang, Guan,Ye, Bin Techno-Press 2018 Geomechanics & engineering Vol.14 No.3

Pullout tests are usually employed to determine the ultimate bearing capacity of reinforced soil, and the load-displacement curve can be obtained easily. This paper presents an analytical solution for predicting the full-range mechanical behavior of a buried planar reinforcement subjected to pullout based on a bi-linear bond-slip model. The full-range behavior consists of three consecutive stages: elastic stage, elastic-plastic stage and debonding stage. For each stage, closed-form solutions for the load-displacement relationship, the interfacial slip distribution, the interfacial shear stress distribution and the axial stress distribution along the planar reinforcement were derived. The ultimate load and the effective bond length were also obtained. Then the analytical model was calibrated and validated against three pullout experimental tests. The predicted load-displacement curves as well as the internal displacement distribution are in closed agreement with test results. Moreover, a parametric study on the effect of anchorage length, reinforcement axial stiffness, interfacial shear stiffness and interfacial shear strength is also presented, providing insights into the pullout behaviour of planar reinforcements of MSE structures.

A Single- and Multi-objective Optimization Algorithm for Electromagnetic Devices Assisted by Adaptive Kriging Based on Parallel Infilling Strategy

Bin Xia,Ren Liu,Zhiwei He,Chang Seop Koh 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1

A computationally effi cient surrogate model is suggested to approximate the objective and constraint function values, which replace expensive evaluation of the objective and constraint function values in numerical simulation-based optimization. Kriging surrogate model has been widely used in surrogate-based design optimization (SBDO) to replace the highly nonlinear black-box functions. In this paper, a novel adaptive Kriging model based on parallel infi lling strategy is proposed to improve both the numerical accuracy and effi ciency of the SBDO methods. The parallel infi lling strategy consists of two parts: local sampling and globaluthor sampling. In the local sampling, new additional sampling points are generated only within a limited region that is determined according to the optimal point at the last iteration, while in global sampling they are generated based on the fi tting error estimation in the whole region. The eff ectiveness of the proposed algorithm is verifi ed through applications to analytical functions. Then the algorithm is applied to the multi-objective optimal design of an ironless permanent magnet synchronous linear motor.

Global existence and asymptotic behavior in a three-dimensional two-species chemotaxis-Stokes system with tensor-valued sensitivity

Bin Liu,Guoqiang Ren 대한수학회 2020 대한수학회지 Vol.57 No.1

In this paper, we deal with a two-species chemotaxis-Stokes system with Lotka-Volterra competitive kinetics under homogeneous Neumann boundary conditions in a general three-dimensional bounded domain with smooth boundary. Under appropriate regularity assumptions on the initial data, by some $L^p$-estimate techniques, we show that the system possesses at least one global and bounded weak solution, in addition to discussing the asymptotic behavior of the solutions. Our results generalizes and improves partial previously known ones.

Identifying Typhoon Targeted Observations Sensitive Areas Using the Gradient Definition Based Method

Bin Mu,Juhui Ren,Shi-Jin Yuan,Fei-Fan Zhou 한국기상학회 2019 Asia-Pacific Journal of Atmospheric Sciences Vol.55 No.2

Increasing additional observation in the typhoon targeted observation sensitive area can help providing more accurate initial field for numerical models, further to improve the typhoon forecast skill. The critical problem is how to identify the typhoon targeted observation sensitive area. Conditional nonlinear optimal perturbation (CNOP) has been proved to be an effective method. Generally, the CNOP is solved using adjoint-based method, which needs to utilize the adjoint models of the numerical models. However, the adjoint models for some numerical models have not been developed or only for some modules. The gradient definition based method is an adjoint-free method, which has been applied to solve the CNOP of Zebiak-Cane (ZC) model with 1080-dimensional solution space to study the optimal precursors of El Nino-Southern Oscillation (ENSO) event. It is very easy to realize, but the time efficiency will go down dramatically along with the rapidly increasing dimensions. In this paper, the gradient definition based method is applied to solve the CNOP ofMM5 model with more than 105-dimensional solution space to identify the typhoon targeted observation sensitive area. Compared to the adjoint-based method, the identified sensitive area and the benefits of the CNOPs are very similar for typhoon Matmo (2014) and Fitow (2013), and higher time efficiency can be achieved. Furthermore, the OSSEs’ results show that the sensitive area identified can be used to improve the forecast skill of typhoon Matmo and Fitow to different extent.