Heba S. Mohammed¹, Mazen Nassar^2,3, Refah Alotaibi¹, Ahmed Elshahhat^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.028826

Abstract A new extended exponential lifetime model called Harris extended-exponential (HEE) distribution for data modelling with increasing and decreasing hazard rate shapes has been considered. In the reliability context, researchers prefer to use censoring plans to collect data in order to achieve a compromise between total test time and/or test sample size. So, this study considers both maximum likelihood and Bayesian estimates of the Harris extended-exponential distribution parameters and some of its reliability indices using a progressive Type-II censoring strategy. Under the premise of independent gamma priors, the Bayesian estimation is created using the squared-error and general entropy loss functions. Due… More >

Chong Xu¹, Hao Zhu¹, Haotian Zhu², Jirong Wang¹, Qinghai Zhao^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.029152

Abstract A new and improved RRT^∗ algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm. This algorithm enables the robot to avoid obstacles, find the optimal path, and complete automatic charging docking. It maintains the global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality of generated paths in both 2D and 3D path planning. After finding the optimal path, the B-sample curve is used to optimize the rough path to create a smoother and more optimal path.… More >

Jiepeng Yao^1,2, Zhanjia Peng^1,2, Jingjing Liu^1,2, Chengxiao Fan^1,2, Zhongyi Wang^1,2,3, Lan Huang^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.028101

Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this paper, taking the electrophysiological differential… More >

Lilan Zou¹, Bo Liang¹, Xu Cheng², Shufa Li^1,*, Cong Lin^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.028037

Abstract Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment. In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment, we proposed a more effective and robust target detection framework based on deep learning, which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection. Firstly, the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with… More >

Nosheen Fatima¹, Nabeela Kousar¹, Khalil Ur Rehman^2,3,*, Wasfi Shatanawi^2,4,5

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.026994

Abstract A computational analysis of magnetized hybrid Darcy-Forchheimer nanofluid flow across a flat surface is presented in this work. For the study of heat and mass transfer aspects viscous dissipation, activation energy, Joule heating, thermal radiation, and heat generation effects are considered. The suspension of nanoparticles singlewalled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) are created by hybrid nanofluids. However, single-walled carbon nanotubes (SWCNTs) produce nanofluids, with water acting as conventional fluid, respectively. Nonlinear partial differential equations (PDEs) that describe the ultimate flow are converted to nonlinear ordinary differential equations (ODEs) using appropriate similarity transformation. The ODEs are dealt with… More >

B. Ramesh, Kuruva Lakshmanna^*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.028944

Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Type 2… More >

Fei Li¹, Haci Mehmet Baskonus^2,*, S. Kumbinarasaiah³, G. Manohara³, Wei Gao⁴, Esin Ilhan⁵

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.028069

Abstract This article considers three types of biological systems: the dengue fever disease model, the COVID-19 virus model, and the transmission of Tuberculosis model. The new technique of creating the integration matrix for the Bernoulli wavelets is applied. Also, the novel method proposed in this paper is called the Bernoulli wavelet collocation scheme (BWCM). All three models are in the form system of coupled ordinary differential equations without an exact solution. These systems are converted into a system of algebraic equations using the Bernoulli wavelet collocation scheme. The numerical wave distributions of these governing models are obtained by solving the algebraic… More >

Wangpeng He^1,*, Yue Zhou¹, Xiaoya Guo², Deshun Hu¹, Junjie Ye³

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.027896

Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel… More >

Rongrong Ren^1,2, Luyang Su^1,2, Xinyu Meng^1,2, Jianfang Wang³, Meng Zhao^1,2,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.027310

Abstract With the development of big data and social computing, large-scale group decision making (LGDM) is now merging with social networks. Using social network analysis (SNA), this study proposes an LGDM consensus model that considers the trust relationship among decision makers (DMs). In the process of consensus measurement: the social network is constructed according to the social relationship among DMs, and the Louvain method is introduced to classify social networks to form subgroups. In this study, the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights. In the process of consensus improvement: A… More >

Yuexin Huang^1,2, Suihuai Yu¹, Jianjie Chu^1,*, Zhaojing Su^1,3, Yangfan Cong¹, Hanyu Wang¹, Hao Fan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.028268

Abstract The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design. This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph. Specifically, the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data, and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design. Moreover, the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module, and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity… More >