Lyuchao Liao^1,2, Hankun Xiao^2,*, Pengqi Xing², Zhenhua Gan^1,2, Youpeng He², Jiajun Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational… More >

ABHIJEET SURYAWANSHI¹, NIRANJANA BEHERA^2,*

Journal of Polymer Materials, Vol.40, No.3-4, pp. 305-316, 2023, DOI:10.32381/JPM.2023.40.3-4.11

Abstract Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient Boosting and Random Forest… More >

Karim Choubani^1,2,*, Ons Ghriss³, Nashmi H. Alrasheedi¹, Sirin Dhaoui², Abdallah Bouabidi²

Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 341-358, 2024, DOI:10.32604/fhmt.2024.047016

Abstract Faced with the world’s environmental and energy-related challenges, researchers are turning to innovative, sustainable and intelligent solutions to produce, store, and distribute energy. This work explores the trend of using a smart sensor to monitor the stability and efficiency of a salt-gradient solar pond. Several studies have been conducted to improve the thermal efficiency of salt-gradient solar ponds by introducing other materials. This study investigates the thermal and salinity behaviors of a pilot of smart salt-gradient solar ponds with (SGSP) and without (SGSPP) paraffin wax (PW) as a phase-change material (PCM). Temperature and salinity were measured experimentally using a smart… More >

Chengsheng Yuan^1,2, Baojie Cui^1,2, Zhili Zhou³, Xinting Li^4,*, Qingming Jonathan Wu⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854

Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of… More >

Weida Wu, Yiqiang Wang, Zhonghao Gao, Pai Liu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2001-2026, 2024, DOI:10.32604/cmes.2023.046670

Abstract Negative Poisson’s ratio (NPR) metamaterials are attractive for their unique mechanical behaviors and potential applications in deformation control and energy absorption. However, when subjected to significant stretching, NPR metamaterials designed under small strain assumption may experience a rapid degradation in NPR performance. To address this issue, this study aims to design metamaterials maintaining a targeted NPR under large deformation by taking advantage of the geometry nonlinearity mechanism. A representative periodic unit cell is modeled considering geometry nonlinearity, and its topology is designed using a gradient-free method. The unit cell microstructural topologies are described with the material-field series-expansion (MFSE) method. The… More >

Jiangshuai Wang^1,*, Chuchu Cai¹, Pan Fu^2,3, Jun Li^4,5, Hongwei Yang⁴, Song Deng¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 537-546, 2024, DOI:10.32604/fdmp.2023.030430

Abstract Multi-gradient drilling is a new offshore drilling method. The accurate calculation of the related wellbore temperature is of great significance for the prediction of the gas hydrate formation area and the precise control of the wellbore pressure. In this study, a new heat transfer model is proposed by which the variable mass flow is properly taken into account. Using this model, the effects of the main factors influencing the wellbore temperature are analyzed. The results indicate that at the position where the separation injection device is installed, the temperature increase of the fluid in the drill pipe is mitigated due… More >

Sheng Qian¹, Qi Tong^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.010116

Abstract Applications such as aircrafts and electronic devices require the noise and vibration reduction without much extra burden, such as extra damping systems. High damping metallic materials that exhibit the ability to dissipate mechanical energy are potential candidates in these application via directly being part of the functional components, such as the frame materials. The energy damping in polycrystalline metals depends on the activities of defects such as dislocation and grain boundary. However, operating defects has the opposite effect on strength and damping capacity. In the quest for high damping metals, maintaining the level of strength is desirable in practice. In… More >

Jiansheng Peng^1,2,*, Jinsong Guo¹, Fengbo Bao¹, Chengjun Yang², Yong Xu², Yong Qin²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2971-2994, 2023, DOI:10.32604/cmc.2023.041897

Abstract The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019. With the development of sensors and smart devices, factories and enterprises have accumulated a large amount of data in their daily production, which creates extremely favorable conditions for robots to perform machine learning. However, in recent years, people’s awareness of data privacy has been increasing, leading to the inability to circulate data between different enterprises, resulting in the emergence of data silos. The emergence of federated learning provides a feasible solution to this problem, and the combination of federated learning and multi-robot systems… More >

Lianghao Hua^1,2, Jianfeng Zhang^1,*, Dejie Li³, Xiaobo Xi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2129-2157, 2024, DOI:10.32604/cmes.2023.030535

Abstract With the increasing prevalence of high-order systems in engineering applications, these systems often exhibit significant disturbances and can be challenging to model accurately. As a result, the active disturbance rejection controller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmanned aerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances and the possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address these issues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neural network (RBFNN) with a second-order ADRC and leverages a… More > Graphic Abstract

Mohamed Zarouan¹, Ibrahim M. Mehedi^1,2,*, Shaikh Abdul Latif³, Md. Masud Rana⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1341-1364, 2024, DOI:10.32604/cmes.2023.030037

Abstract Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamless operation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0. Specifically, various modernized industrial processes have been equipped with quite a few sensors to collect process-based data to find faults arising or prevailing in processes along with monitoring the status of processes. Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Due to the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experience and human knowledge, intellectual… More >