Qi Zhou^1,2, Jinghua Li^1,3, Ruipu Dong^1,*, Qinghua Zhou^3,*, Boxin Yang³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1263-1281, 2023, DOI:10.32604/cmes.2022.020744 - 31 August 2022

Abstract Offshore engineering construction projects are large and complex, having the characteristics of multiple execution modes and multiple resource constraints. Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems (RCPSPs). To solve RCPSP problems in offshore engineering construction more rapidly, a hybrid genetic algorithm was established. To solve the defects of genetic algorithms, which easily fall into the local optimal solution, a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation. Then, an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions, More >

Xin Wang, Zhilin Zhu, Zhen Hua^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 893-908, 2023, DOI:10.32604/cmes.2022.021287 - 31 August 2022

Abstract Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute More >

Tingshun Li¹, Jiaohui Xu^1,*, Zesan Liu², Dadi Wang², Wen Tan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 767-782, 2023, DOI:10.32604/cmes.2022.020702 - 31 August 2022

Abstract The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is, they are… More >

Huiwen Xue¹, Jiwei Wen^1,*, Akshya Kumar Swain¹, Xiaoli Luan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 857-871, 2023, DOI:10.32604/cmes.2022.020127 - 31 August 2022

Abstract In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because More >

Francesco Tornabene^*, Matteo Viscoti, Rossana Dimitri

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1393-1468, 2023, DOI:10.32604/cmes.2022.022237 - 31 August 2022

Abstract In the present manuscript, a Layer-Wise (LW) generalized model is proposed for the linear static analysis of doublycurved shells constrained with general boundary conditions under the influence of concentrated and surface loads. The unknown field variable is modelled employing polynomials of various orders, each of them defined within each layer of the structure. As a particular case of the LW model, an Equivalent Single Layer (ESL) formulation is derived too. Different approaches are outlined for the assessment of external forces, as well as for non-conventional constraints. The doubly-curved shell is composed by superimposed generally anisotropic… More >

Hao Han, Wei Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494 - 31 August 2022

Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction More >

Mohammad Sadegh Barkhordari¹, Danial Jahed Armaghani^2,*, Panagiotis G. Asteris³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840 - 31 August 2022

Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of More >

Passent El-kafrawy^1,2, Maie Aboghazalah^2,*, Abdelmoty M. Ahmed³, Hanaa Torkey⁴, Ayman El-Sayed⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 909-926, 2023, DOI:10.32604/cmes.2022.021713 - 31 August 2022

Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the… More >

Noor Zanib¹, Munira Batool¹, Saleem Riaz², Farkhanda Afzal³, Sufian Munawar⁴, Ibtisam Daqqa⁵, Najma Saleem^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1105-1136, 2023, DOI:10.32604/cmes.2022.021676 - 31 August 2022

Abstract This paper presents a comprehensive study that includes the sizing and power flow by series and parallel inverters in a distributed generation system (DGs) that integrates the system of hybrid wind photovoltaic with a unified power quality conditioner (UPQC). In addition to supplying active power to the utility grid, the system of hybrid wind photovoltaic functions as a UPQC, compensating reactive power and suppressing the harmonic load currents. Additionally, the load is supplied with harmonic-free, balanced and regulated output voltages. Since PVWind-UPQC is established on a dual compensation scheme, the series inverter works like a… More >

Kamal Shah^1,2, Hafsa Naz², Thabet Abdeljawad^1,3,*, Aziz Khan¹, Manar A. Alqudah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 941-955, 2023, DOI:10.32604/cmes.2022.021483 - 31 August 2022

Abstract In this manuscript, an algorithm for the computation of numerical solutions to some variable order fractional differential equations (FDEs) subject to the boundary and initial conditions is developed. We use shifted Legendre polynomials for the required numerical algorithm to develop some operational matrices. Further, operational matrices are constructed using variable order differentiation and integration. We are finding the operational matrices of variable order differentiation and integration by omitting the discretization of data. With the help of aforesaid matrices, considered FDEs are converted to algebraic equations of Sylvester type. Finally, the algebraic equations we get are More >