Haojie Lian¹, Jiaqi Wang¹, Leilei Chen^2,*, Shengze Li³, Ruochen Cao⁴, Qingyuan Hu⁵, Peiyun Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549

Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations… More >

Xue-Qin Li¹, Lu-Kai Song^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 665-684, 2024, DOI:10.32604/cmes.2024.048445

Abstract Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function, leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy. In this case, by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory, a random forest (RF) model is presented to enhance the computing efficiency of reliability degree; moreover, by embedding the RF model into multilevel optimization model, an efficient RF-assisted fatigue reliability-based design optimization framework is developed. Regarding the low-cycle fatigue reliability-based design optimization of… More >

Bo Liu^1,2, Li Feng^1,*, Yufeng Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 845-862, 2024, DOI:10.32604/cmes.2024.047695

Abstract This paper focuses on the effective utilization of data augmentation techniques for 3D lidar point clouds to enhance the performance of neural network models. These point clouds, which represent spatial information through a collection of 3D coordinates, have found wide-ranging applications. Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities. Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds. However, there has been a lack of focus on making the most of the numerous existing… More >

R.M. MISHRAA, R. VIJAYVARGIYAA, K.N. PANDEYA, J.S.P. RAIB

Journal of Polymer Materials, Vol.37, No.3-4, pp. 179-188, 2020, DOI:10.32381/JPM.2020.37.3-4.5

Abstract The present investigation is targeted to prepare nanocomposites based on binary blends of polyetherimide (PEI)-silicone rubber incorporated with varied loadings of nanotitanium dioxide particles. Nanocomposites have been prepared by melt blending process using twin screw extruder. Thermal properties of the developed nanocomposites have been investigated with the help of thermogravimetric analyzer (TGA) and dynamic mechanical analyzer (DMA). Scanning electron microscopy (SEM) is used to analyze the morphological properties of the nanocomposites. Mechanical properties (tensile strength, tensile modulus, elongation at break, impact strength) of the nanocomposites have been evaluated by universal testing machine (UTM). Mechanical testing results reveal that there is… More >

ANIL KUMAR VEERAPANENI¹, CHANDRASEKAR KUPPAN^2,*, MURTHY CHAVALI^3,*

Journal of Polymer Materials, Vol.37, No.3-4, pp. 121-130, 2020, DOI:10.32381/JPM.2020.37.3-4.1

Abstract The mechanical properties of hybrid nanocomposites made of epoxy/glass fibre dispersed with nano-α-Al₂ O₃ powder at different weight percentages were studied.The effect of nano-α- Al₂O₃ size and wt% on mechanical properties like tensile, flexural, interlaminar shear stress and hardness enhanced because of their higher surface area and interfacial polymer-metal interaction. The nanoparticle embedded laminates have shown improvement in flexural strength,and hardness when compared to laminate without nano-α-Al₂ O₃. The properties varied with the loading and size of the nanoparticles. The tensile strength was highest for 0.5 wt% of 200nm nano-α-Al₂O₃, which is 167.80 N/m₂.The highest flexural strength was observed for… More >

HADIMANI SHIVAKUMAR¹, GURUMURTHY G. D.¹, BOMMEGOWDA K. B.², S. PARAMESHWARA³

Journal of Polymer Materials, Vol.40, No.1-2, pp. 93-103, 2023, DOI:10.32381/JPM.2023.40.1-2.8

Abstract Polymer composites are increasing in demand in energy storage applications including in the electronic as well as electrical industries due to the ease of processing of these materials with associated advantages like light weight, corrosion resistance, and high mechanical strength. In this investigation, efforts are made to enhance the charging and discharging properties of epoxy/carbon fabric composite by the addition of graphene nanoplatelets (GNPs) into the epoxy/ carbon matrix. The performance of the composites with graphene platelets of 0.5 to 5 wt. % in epoxy were characterized and 1wt.% percolation threshold was observed poor performance in gravimetric charge and discharge… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity,… More >

Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760

Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships. Firstly, a dataset for… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of these techniques is evaluated using… More >

Qazi Mazhar ul Haq¹, Fahim Arif^2,3, Khursheed Aurangzeb⁴, Noor ul Ain³, Javed Ali Khan⁵, Saddaf Rubab⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172

Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to… More >