Abdulraqeb Alhammadi^1,*, Zaid Ahmed Shamsan², Arijit De³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1889-1905, 2024, DOI:10.32604/cmc.2024.051487 - 15 August 2024

Abstract Indoor localization systems are crucial in addressing the limitations of traditional global positioning system (GPS) in indoor environments due to signal attenuation issues. As complex indoor spaces become more sophisticated, indoor localization systems become essential for improving user experience, safety, and operational efficiency. Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database, but this can increase the computational burden in the online phase. Bayesian networks, which integrate prior knowledge or domain expertise, are an effective solution for accurately determining indoor user locations. These networks use probabilistic reasoning to model relationships among… More >

Mansour F. Yassen^1,2,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1691-1706, 2022, DOI:10.32604/cmc.2022.029701 - 18 May 2022

Abstract Quantifying the tool–tissue interaction forces in surgery can be utilized in the training of inexperienced surgeons, assist them better use surgical tools and avoid applying excessive pressures. The voltages read from strain gauges are used to approximate the unknown values of implemented forces. To this objective, the force-voltage connection must be quantified in order to evaluate the interaction forces during surgery. The progress of appropriate statistical learning approaches to describe the link between the genuine force applied on the tissue and numerous outputs obtained from sensors installed on surgical equipment is a key problem. In More >

W. Zhang¹, X. Han^1,2, J. Liu¹, R. Chen¹

CMES-Computer Modeling in Engineering & Sciences, Vol.86, No.5, pp. 385-408, 2012, DOI:10.3970/cmes.2012.086.385

Abstract A novel inverse technique is presented for quantifying the uncertainty of the identified the results in an encounter condition identification problem. In this technique, the polynomial response surface method based on the structure-selection technique is first adopted to construct the approximation model of the projectile/target system, so as to reduce the computational cost. The Markov Chain Monte Carlo method is then used to identify the encounter condition parameters and their confidence intervals based on this cheap approximation model with Bayesian perspective. The results are demonstrated through the simulated test cases, which show the utility and More >