Chan Hyeong Cho, Hyun Min Song^*, Taek-Young Youn^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1911-1931, 2024, DOI:10.32604/cmes.2024.053430 - 31 October 2024

Abstract With the advancement of video recording devices and network infrastructure, we use surveillance cameras to protect our valuable assets. This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security. The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest (ROIs) within the video, linking these ROIs to generate unique IDs. These IDs are then combined with a master key to create entity-specific keys, which are used to encrypt the ROIs within the video. This system supports selective decryption, effectively protecting personal information More >

Chentong Zhao¹, Jiming Zhou^1,2,*, Xujiang Chao^1,3, Su Wang¹, Lehua Qi^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2453-2469, 2024, DOI:10.32604/cmes.2024.052723 - 31 October 2024

Abstract The mechanical properties of graphene reinforced composites are often hampered by challenges related to the dispersion and aggregation of graphene within the matrix. This paper explores the mechanism of cooling rate, process temperature, and process pressure’s influence on the agglomeration behavior of graphene and the tensile response of composites from a computer simulation technology, namely molecular dynamics. Our findings reveal that the cooling rate exerts minimal influence on the tensile response of composites. Conversely, processing temperature significantly affects the degree of graphene aggregation, with higher temperatures leading to the formation of larger-sized graphene clusters. In More >

Qingrong Zeng, Xiaochen Liu, Xuefeng Zhu^*, Xiangkui Zhang, Ping Hu

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2065-2085, 2024, DOI:10.32604/cmes.2024.052620 - 31 October 2024

Abstract Traditional topology optimization methods often suffer from the “dimension curse” problem, wherein the computation time increases exponentially with the degrees of freedom in the background grid. Overcoming this challenge, we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty (CGAN-GP). This innovative method allows for nearly instantaneous prediction of optimized structures. Given a specific boundary condition, the network can produce a unique optimized structure in a one-to-one manner. The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization (SIMP) method. Subsequently, we More >

Danial Jahed Armaghani^1,*, Zida Liu², Hadi Khabbaz¹, Hadi Fattahi³, Diyuan Li², Mohammad Afrazi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2421-2451, 2024, DOI:10.32604/cmes.2024.052210 - 31 October 2024

Abstract Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s)… More >

Md Naeem Hossain¹, Md Mustafizur Rahman^1,2,*, Devarajan Ramasamy¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 951-996, 2024, DOI:10.32604/cmes.2024.056022 - 27 September 2024

Abstract Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically. Hence, there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns. Due to vehicles’ increasingly complex and autonomous nature, there is a growing urgency to investigate novel diagnosis methodologies for improving safety, reliability, and maintainability. While Artificial Intelligence (AI) has provided a great opportunity in this area, a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis (VFD)… More > Graphic Abstract

Pan Su¹, Jiaxing Chen², Ronggang Yang², Jiawei Xiang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1883-1905, 2024, DOI:10.32604/cmes.2024.055942 - 27 September 2024

Abstract High-performance finite element research has always been a major focus of finite element method studies. This article introduces isogeometric analysis into the finite element method and proposes a new isogeometric finite element method. Firstly, the physical field is approximated by uniform B-spline interpolation, while geometry is represented by non-uniform rational B-spline interpolation. By introducing a transformation matrix, elements of types C⁰ and C¹ are constructed in the isogeometric finite element method. Subsequently, the corresponding calculation formats for one-dimensional bars, beams, and two-dimensional linear elasticity in the isogeometric finite element method are derived through variational principles and… More >

Haris Alam Zuberi¹, Madan Lal¹, Shivangi Verma¹, Nurul Amira Zainal^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1137-1163, 2024, DOI:10.32604/cmes.2024.055493 - 27 September 2024

Abstract Motivated by the widespread applications of nanofluids, a nanofluid model is proposed which focuses on uniform magnetohydrodynamic (MHD) boundary layer flow over a non-linear stretching sheet, incorporating the Casson model for blood-based nanofluid while accounting for viscous and Ohmic dissipation effects under the cases of Constant Surface Temperature (CST) and Prescribed Surface Temperature (PST). The study employs a two-phase model for the nanofluid, coupled with thermophoresis and Brownian motion, to analyze the effects of key fluid parameters such as thermophoresis, Brownian motion, slip velocity, Schmidt number, Eckert number, magnetic parameter, and non-linear stretching parameter on… More > Graphic Abstract

Tong Zu, Fengyong Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1395-1417, 2024, DOI:10.32604/cmes.2024.055442 - 27 September 2024

Abstract False data injection attack (FDIA) can affect the state estimation of the power grid by tampering with the measured value of the power grid data, and then destroying the stable operation of the smart grid. Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams. Data-driven features, however, cannot effectively capture the differences between noisy data and attack samples. As a result, slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks. To address this problem, this paper designs a… More >

Zhenjiang Zhao¹, Lei Jiang¹, Ling Bai^2,*, Bo Pan³, Ling Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1111-1136, 2024, DOI:10.32604/cmes.2024.055399 - 27 September 2024

Abstract The fluid’s viscosity significantly affects the performance of a centrifugal pump. The entropy production method and leakage are employed to analyze the performance changes under various viscosities by numerical simulation and validated by experiments. The results showed that increasing viscosity reduces both the pump head and efficiency. In addition, the optimal operating point shifts to the left. Leakage is influenced by vortex distribution in the front chamber and boundary layer thickness in wear-ring clearance, leading to an initial increase and subsequent decrease in leakage with increasing viscosity. The total entropy production inside the pump rises More >

Fehaid Alqahtani^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1709-1724, 2024, DOI:10.32604/cmes.2024.054976 - 27 September 2024

Abstract With the rapid advancement in artificial intelligence (AI) and its application in the Internet of Things (IoT), intelligent technologies are being introduced in the medical field, giving rise to smart healthcare systems. The medical imaging data contains sensitive information, which can easily be stolen or tampered with, necessitating secure encryption schemes designed specifically to protect these images. This paper introduces an artificial intelligence-driven novel encryption scheme tailored for the secure transmission and storage of high-resolution medical images. The proposed scheme utilizes an artificial intelligence-based autoencoder to compress high-resolution medical images and to facilitate fast encryption… More >