Pedro Montenegro^1,*, Raphael Heleno², Hermes Carvalho², Diogo Ribeiro³, Rui Calçada¹, Chris Baker⁴

The International Conference on Computational & Experimental Engineering and Sciences, Vol.24, No.1, pp. 1-2, 2022, DOI:10.32604/icces.2022.08678

Abstract This work consists of evaluating the impact of the most relevant characteristics of railway vehicles, namely geometric, mechanical and aerodynamic properties, in their runnability in the presence of strong winds, more precisely in the risk of derailment. Such objective is achieved by performing several dynamic with a non-linear vehicle-structure interaction model developed by the authors [1,2] and used in other works in this field [3,4], which allows the evaluation of the wheel-rail contact forces and, consequently, the unloading index, as suggested by the European Norm EN 14067-6 [5]. The calculations are carried out for several scenarios characterized by different train… More >

Christopher C. Rosser, Wilbur L. Walters, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed^*

Journal on Artificial Intelligence, Vol.4, No.4, pp. 261-277, 2022, DOI:10.32604/jai.2022.039703

Abstract Intrinsic motivation helps autonomous exploring agents traverse a larger portion of their environments. However, simulations of different learning environments in previous research show that after millions of timesteps of successful training, an intrinsically motivated agent may learn to act in ways unintended by the designer. This potential for unintended actions of autonomous exploring agents poses threats to the environment and humans if operated in the real world. We investigated this topic by using Unity’s Machine Learning Agent Toolkit (ML-Agents) implementation of the Proximal Policy Optimization (PPO) algorithm with the Intrinsic Curiosity Module (ICM) to train autonomous exploring agents in three… More >

Yuchen Zhao, Shulei Wu^*, Yaoru Wang, Huandong Chen^*, Xianyao Zhang, Hongwei Zhao

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5639-5657, 2023, DOI:10.32604/cmc.2023.037829

Abstract Due to the rapid growth and spread of fire, it poses a major threat to human life and property. Timely use of fire detection technology can reduce disaster losses. The traditional threshold segmentation method is unstable, and the flame recognition methods of deep learning require a large amount of labeled data for training. In order to solve these problems, this paper proposes a new method combining You Only Look Once version 5 (YOLOv5) network model and improved flame segmentation algorithm. On the basis of the traditional color space threshold segmentation method, the original segmentation threshold is replaced by the proportion… More >

Jianming Wang, Qiguang Wang^*, Xiaotang Sheng, Jingsong Geng, Jiawang Xiao, Xianyang Zhu^*

Congenital Heart Disease, Vol.18, No.2, pp. 183-195, 2023, DOI:10.32604/chd.2022.021855

Abstract Objective: This study was designed to determine the long-term safety and efficacy of using the Amplatzer Duct Occluder II (ADO II) for the closure of various ventricular septal defects (VSDs). Methods: From January 2011 to December 2019, selected VSD patients were treated through transcatheter intervention using ADO II occluders. The closure results and complications from 188 patients, involving 167 perimembranous ventricular septal defects (pmVSDs), 9 intracristal VSDs, 11 post surgery residual shunts and 1 post closure residual shunt with the mean outlet diameter3.1 ± 0.8 mm under angiography, were enrolled in this study. Results: The success rate was 98.9% for… More >

Feisha Hu¹, Qi Wang^1,*, Haijian Shao^1,2, Shang Gao¹, Hualong Yu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732

Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of… More > Graphic Abstract

Fereydoon Laal^1,2, Amir Hossein Khoshakhlagh³, Esmaeil Zarei^4,5, Rohollah Fallah Madvari⁶, Somayeh Farhang Dehghan^7,*

Sound & Vibration, Vol.56, No.4, pp. 297-306, 2022, DOI:10.32604/sv.2022.028255

Abstract After controls, including engineering and management, the final way to control noise is to use hearing protection devices. Due to the lack of a standardized questionnaire regarding investigating workers’ use of hearing protection devices on the basis of the BASNEF behavioral model, the present study was conducted to investigate the effect of health education based on the BASNEF model on the use of hearing protection devices in workers of an automobile manufacturing plant in Iran. This quasi-experimental and prospective intervention study was performed on 80 workers at an automobile manufacturing plant who are exposed to noise levels above 85 decibels… More >

Yingxue Sang¹, Fengxia Han^1,2,*, Qing Liu^1,2, Liang Qiao³, Shouxi Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1973-1998, 2023, DOI:10.32604/cmes.2023.026218

Abstract Rapid urbanization has led to a surge in the number of towering structures, and overturning is widely used because it can better accommodate the construction of shaped structures such as variable sections. The complexity of the construction process makes the construction risk have certain randomness, so this paper proposes a cloud-based coupled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning construction of towering structures. In the pretended model, the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element, and calculate the cloud correlation of the… More >

Yue Su¹, Jingfa Li^2,*, Bo Yu², Yanlin Zhao¹, Dongxu Han², Dongliang Sun²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1315-1337, 2023, DOI:10.32604/cmes.2023.026035

Abstract With the introduction of various carbon reduction policies around the world, hydrogen energy, as a kind of clean energy with zero carbon emission, has attracted much attention. The safe and economical transportation of hydrogen is of great significance to the development of hydrogen energy industries. Utilizing natural gas pipelines to transport hydrogen is considered to be an efficient and economical way. However, hydrogen has a higher risk of leakage due to its strong diffusion capacity and lower explosive limit than conventional natural gas. Therefore, it is of great significance to study the leakage and diffusion law of hydrogen-enriched natural gas… More > Graphic Abstract

Jiazheng Du^*, Xue Cong, Ying Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1091-1120, 2023, DOI:10.32604/cmes.2023.025585

Abstract Spacecraft in the aerospace field and military equipment in the military field are at risk of being impacted by external objects, which can cause local damage to the structure. The randomness of local damage is a new challenge for structural design, and it is essential to take random damage into account in the conceptual design phase for the purpose of improving structure’s resistance to external shocks. In this article, a random damaged structure is assumed to have damages of the same size and shape at random locations, and the random damage is considered as multiple damage conditions of the structure.… More > Graphic Abstract

NaeJoung Kwak¹, DongJu Kim^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1671-1686, 2023, DOI:10.32604/cmc.2023.035762

Abstract This paper proposes a method for detecting a helmet for the safety of workers from risk factors and a mask worn indoors and verifying a worker’s identity while wearing a helmet and mask for security. The proposed method consists of a part for detecting the worker’s helmet and mask and a part for verifying the worker’s identity. An algorithm for helmet and mask detection is generated by transfer learning of Yolov5’s s-model and m-model. Both models are trained by changing the learning rate, batch size, and epoch. The model with the best performance is selected as the model for detecting… More >