Rongwei Yu, Xiya Guo^*, Peihao Zhang, Kaijuan Zhang

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2643-2664, 2024, DOI:10.32604/cmc.2024.056165 - 18 November 2024

Abstract Encrypted traffic plays a crucial role in safeguarding network security and user privacy. However, encrypting malicious traffic can lead to numerous security issues, making the effective classification of encrypted traffic essential. Existing methods for detecting encrypted traffic face two significant challenges. First, relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic. Second, machine learning and convolutional neural network methods lack sufficient network expression capabilities, hindering the full exploration of traffic’s potential characteristics. To address these limitations, this study introduces a traffic classification method that utilizes… More >

Hailong Chen^1,*, Wailam Chan¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.09835

Abstract The conventional peridynamic material correspondence model suffers from the well-known issue of material instability. The material instability in peridynamics can be understood as the existence of nonunique mapping between deformation states and the resultant force state. This instability poses practical difficulties while using the correspondence model in computational modeling. One consequence of this instability is the oscillation in the predicted displacement field, i.e., existence of zero-energy modes. Bondassociated correspondence formulations have been proposed to inherently remove the material instability. Different from the conventional formulation, bond-associated formulations are developed based on the concept of bond-associated deformation… More >

Yu-Fu Ko^1,* , Jiann-Wen Woody Ju²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1305-1328, 2021, DOI:10.32604/cmes.2021.017589 - 25 November 2021

Abstract Higher-order multiscale structures are proposed to predict the effective elastic properties of 3-phase particle reinforced composites by considering the probabilistic spherical particles spatial distribution, the particle interactions, and utilizing homogenization with ensemble volume average approach. The matrix material, spherical particles with radius a₁, and spherical particles with radius a₂, are denoted as the 0^th phase, the 1^st phase, and the 2^nd phase, respectively. Particularly, the two inhomogeneity phases are different particle sizes and the same elastic material properties. Improved higher-order (in ratio of spherical particle sizes to the distance between the centers of spherical particles) bounds on effective More >

Arash Mehraban¹, Henry Tufo¹, Stein Sture², Richard Regueiro^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1283-1303, 2021, DOI:10.32604/cmes.2021.017476 - 25 November 2021

Abstract Higher-order displacement-based finite element methods are useful for simulating bending problems and potentially addressing mesh-locking associated with nearly-incompressible elasticity, yet are computationally expensive. To address the computational expense, the paper presents a matrix-free, displacement-based, higher-order, hexahedral finite element implementation of compressible and nearly-compressible (ν → 0.5) linear isotropic elasticity at small strain with p-multigrid preconditioning. The cost, solve time, and scalability of the implementation with respect to strain energy error are investigated for polynomial order p = 1, 2, 3, 4 for compressible elasticity, and p = 2, 3, 4 for nearly-incompressible elasticity, on different number More >

Taekyun Kim^1,*, Dae San Kim², Dmitry V. Dolgy³, Si-Hyeon Lee¹, Jongkyum Kwon^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1121-1132, 2021, DOI:10.32604/cmes.2021.016532 - 11 August 2021

Abstract We introduce the higher-order type 2 Bernoulli numbers and polynomials of the second kind. In this paper, we investigate some identities and properties for them in connection with central factorial numbers of the second kind and the higher-order type 2 Bernoulli polynomials. We give some relations between the higher-order type 2 Bernoulli numbers of the second kind and their conjugates. More >

K. K. Thyagharajan, I. Kiruba Raji^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2021.017591 - 21 July 2021

Abstract This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew,… More >

Yi Ji^1,2, Yufeng Xing^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 549-575, 2021, DOI:10.32604/cmes.2021.014244 - 21 January 2021

Abstract Based on the weighted residual method, a single-step time integration algorithm with higher-order accuracy and unconditional stability has been proposed, which is superior to the second-order accurate algorithms in tracking long-term dynamics. For improving such a higher-order accurate algorithm, this paper proposes a two sub-step higher-order algorithm with unconditional stability and controllable dissipation. In the proposed algorithm, a time step interval [tk, tk + h] where h stands for the size of a time step is divided into two sub-steps [tk, tk + γh] and [tk + γh, tk + h]. A non-dissipative fourth-order algorithm is used in the rst… More >

Qiping Chen^*, Haoyu Sun, Ning Wang, Zhi Niu, Rui Wan

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.3, pp. 513-524, 2020, DOI:10.32604/fdmp.2020.09375 - 25 May 2020

Abstract The possibility to enhance the stability and robustness of electrohydraulic brake (EHB) systems is considered a subject of great importance in the automotive field. In such a context, the present study focuses on an actuator with a four-way sliding valve and a hydraulic cylinder. A 4-order nonlinear mathematical model is introduced accordingly. Through the linearization of the feedback law of the high order EHB model, a sliding mode control method is proposed for the hydraulic pressure. The hydraulic pressure tracking controls are simulated and analyzed by MATLAB/Simulink soft considering separately different conditions, i.e., a sine More >

Yuzhou Sun¹, Yuchao Mu²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.1, pp. 115-115, 2019, DOI:10.32604/icces.2019.05304

Abstract Due to its double-structure property, the higher-order continuum theory is adopted to study the constitutive behavior of expansive soil. The higher-order strain and scale factor are considered to describe the effect of the microscale structural property on the macroscale behavior, and a higher-order multiscale constitutive model is developed for expansive soil. The effect of the microscale structural property is investigated through the theoretical and experimental studies based on the developed model. In virtue of a representative elementary volume, the double-structure property is better studied for expansive soil. A variational equation is developed with the contribution… More >

Hongwei Guo³, Xiaoying Zhuang^3,4,5, Timon Rabczuk^1,2,*

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 433-456, 2019, DOI:10.32604/cmc.2019.06660

Abstract In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed. This method takes advantage of computational graphs and backpropagation algorithms involved in deep learning. Besides, the proposed DCM is based on a feedforward deep neural network (DNN) and differs from most previous applications of deep learning for mechanical problems. First, batches of randomly distributed collocation points are initially generated inside the domain and along the boundaries. A loss function is built with the aim that the governing partial differential equations (PDEs) of Kirchhoff plate bending problems, and the boundary/initial conditions More >