Shigeki Kaneko¹, Naoto Mitsume², Shinobu Yoshimura^1,*

Digital Engineering and Digital Twin, Vol.2, pp. 33-48, 2024, DOI:10.32604/dedt.2023.044279

Abstract In an integrated coal gasification combined cycle plant, cooling pipes are installed in the gasifier reactor and water cooling is executed to avoid reaching an excessively high temperature. To accelerate the design, it is necessary to develop an analysis system that can simulate the cooling operation within the practical computational time. In the present study, we assumed the temperature fields of the cooled object and the cooling water to be governed by the three-dimensional (3D) heat equation and the one-dimensional (1D) convection-diffusion equation, respectively. Although some existing studies have employed similar modeling, the applications have been limited to simple-shaped structures.… More >

Dinesh Mavaluru^1,*, Chettupally Anil Carie², Ahmed I. Alutaibi³, Satish Anamalamudi², Bayapa Reddy Narapureddy⁴, Murali Krishna Enduri², Md Ezaz Ahmed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277

Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management… More >

Ahmed Barnawi^1,*, Krishan Kumar², Neeraj Kumar¹, Bander Alzahrani¹, Amal Almansour¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2117-2137, 2024, DOI:10.32604/cmes.2023.044184

Abstract Landmines continue to pose an ongoing threat in various regions around the world, with countless buried landmines affecting numerous human lives. The detonation of these landmines results in thousands of casualties reported worldwide annually. Therefore, there is a pressing need to employ diverse landmine detection techniques for their removal. One effective approach for landmine detection is UAV (Unmanned Aerial Vehicle) based Airborne Magnetometry, which identifies magnetic anomalies in the local terrestrial magnetic field. It can generate a contour plot or heat map that visually represents the magnetic field strength. Despite the effectiveness of this approach, landmine removal remains a challenging… More >

Tianming Zhang¹, Zebin Chen¹, Haonan Guo², Bojun Ren¹, Quanmin Xie^3,*, Mengke Tian^4,*, Yong Wang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2139-2154, 2024, DOI:10.32604/cmes.2023.043822

Abstract The data analysis of blasting sites has always been the research goal of relevant researchers. The rise of mobile blasting robots has aroused many researchers’ interest in machine learning methods for target detection in the field of blasting. Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience, which has aroused people’s interest in how to use it in the field of machine learning. In this paper, we design a distributed machine learning training application based on the AWS Lambda platform. Based on data parallelism, the data aggregation and training synchronization… More >

Basma Mohamed^1,*, Linda Mohaisen², Mohammed Amin¹

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 19-34, 2023, DOI:10.32604/iasc.2023.031947

Abstract In this paper, we consider the NP-hard problem of finding the minimum dominant resolving set of graphs. A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B. A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B. The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set. The dominant metric dimension is computed by a binary version of the Archimedes optimization… More >

Hayder Makki Shakir, Jaber Karimpour^*, Jafar Razmara

Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545

Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource accessibility, and security. In this… More >

Changtao Wang, Honghua Dai^*, Wenchuan Yang

Digital Engineering and Digital Twin, Vol.1, pp. 3-13, 2023, DOI:10.32604/dedt.2023.044210

Abstract A novel Adaptive Parallel Feedback-Accelerated Picard Iteration (AP-FAPI) method is proposed to meet the requirements of various aerospace missions for fast and accurate orbit propagation. The Parallel Feedback-Accelerated Picard Iteration (P-FAPI) method is an advanced iterative collocation method. With large-step computing and parallel acceleration, the P-FAPI method outperforms the traditional finite-difference-based methods, which require small-step and serial integration to ensure accuracy. Although efficient and accurate, the P-FAPI method suffers extensive trials in tuning method parameters, strongly influencing its performance. To overcome this problem, we propose the AP-FAPI method based on the relationship between the parameters and the convergence speed leveraging… More >

Salam Al-E’mari¹, Yousef Sanjalawe^2,*, Ahmad Al-Daraiseh³, Mohammad Bany Taha⁴, Mohammad Aladaileh²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.043627

Abstract Amid the landscape of Cloud Computing (CC), the Cloud Datacenter (DC) stands as a conglomerate of physical servers, whose performance can be hindered by bottlenecks within the realm of proliferating CC services. A linchpin in CC’s performance, the Cloud Service Broker (CSB), orchestrates DC selection. Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck, endangering service quality. To tackle this, deploying an efficient CSB policy becomes imperative, optimizing DC selection to meet stringent Quality-of-Service (QoS) demands. Amidst numerous CSB policies, their implementation grapples with challenges like costs and availability. This article undertakes a holistic… More >

Ming Gao^1,#, Weiwei Cai^1,#, Yizhang Jiang¹, Wenjun Hu³, Jian Yao², Pengjiang Qian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 259-277, 2024, DOI:10.32604/cmes.2023.029015

Abstract Currently, applications accessing remote computing resources through cloud data centers is the main mode of operation, but this mode of operation greatly increases communication latency and reduces overall quality of service (QoS) and quality of experience (QoE). Edge computing technology extends cloud service functionality to the edge of the mobile network, closer to the task execution end, and can effectively mitigate the communication latency problem. However, the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management, and the booming development of artificial neural networks provides us with more powerful methods… More >

Xiangmin Guo^1,2, Guangjun Liang^1,2,*, Jiayin Liu^1,2, Xianyi Chen^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3305-3323, 2023, DOI:10.32604/cmc.2023.044529

Abstract Cloud storage is widely used by large companies to store vast amounts of data and files, offering flexibility, financial savings, and security. However, information shoplifting poses significant threats, potentially leading to poor performance and privacy breaches. Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms, ensuring businesses can focus on business development. To ensure data security in cloud platforms, this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing (HD2C) model. However, the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things (IoT) in the cloud.… More >