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RISS 인기검색어
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- 저자 : Chenghao Lu (검색결과 3 건)
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A New Variational Bayesian-based Kalman Filter with Random Measurement Delay and Non-Gaussian Noises
Chenghao Shan,Weidong Zhou,Hanyu Shan,Lu Liu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.8
To improve the estimation accuracy of the Kalman filter in the scenario of random measurement delay and non-Gaussian process and measurement noises, a new variational Bayesian (VB)-based Kalman filter is proposed in this paper. First, the state expansion method and Bernoulli random variable (BRV) are utilized to characterize random measurement delay. Second, the one-step predicted probability density function (PDF) and measurement noise vectors are modeled as Student’s t (ST) distributions. Third, the likelihood function of two ST distributions is converted from a weighted sum to an exponential product to establish a hierarchical Gaussian state space model (HGSSM). Finally, the system state, BRV and intermediate random variables (IRV) are simultaneously estimated using the variational Bayesian (VB) method. Simulation experiment results indicate that the proposed filter has superior estimation performance to current filters to address the filtering problem of random measurement delay and non-Gaussian process and measurement noises.
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Jia Heming,Lu Chenghao,Wu Di,Wen Changsheng,Rao Honghua,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4
In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.
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Zheyu Yang,Yu Heng,Jianwei Lin,Chenghao Lu,Dingye Yu,Lei Tao,Wei Cai 대한암학회 2020 Cancer Research and Treatment Vol.52 No.4
Purpose Central lymph node metastasis (CNM) are highly prevalent but hard to detect preoperatively in papillary thyroid carcinoma (PTC) patients, while the significance of prophylactic compartment central lymph node dissection (CLND) remains controversial as a treatment option. We aim to establish a nomogram assessing risks of CNM in PTC patients, and explore whether prophylactic CLND should be recommended. Materials and Methods One thousand four hundred thirty-eight patients from two clinical centers that underwent thyroidectomy with CLND for PTC within the period 2016-2019 were retrospectively analyzed. Univariate and multivariate analysis were performed to examine risk factors associated with CNM. A nomogram for predicting CNM was established, thereafter internally and externally validated. Results Seven variables were found to be significantly associated with CNM and were used to construct the model. These were as follows: thyroid capsular invasion, multifocality, creatinine > 70 μmol/L, age < 40, tumor size > 1 cm, body mass index < 22, and carcinoembryonic antigen > 1 ng/mL. The nomogram had good discrimination with a concordance index of 0.854 (95% confidence interval [CI], 0.843 to 0.867), supported by an external validation point estimate of 0.825 (95% CI, 0.793 to 0.857). A decision curve analysis was made to evaluate nomogram and ultrasonography for predicting CNM. Conclusion A validated nomogram utilizing readily available preoperative variables was developed to predict the probability of central lymph node metastases in patients presenting with PTC. This nomogram may help surgeons make appropriate surgical decisions in the management of PTC, especially in terms of whether prophylactic CLND is warranted.
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