A review of Kalman filter with artificial intelligence techniques

Kalman filter (KF) is a widely used estimation algorithm for many applications. However, in many cases, it is not easy to estimate the exact state of the system due to many reasons such as an imperfect mathematical model, dynamic environments, or inaccurate parameters of KF. Artificial intelligence (AI) techniques have been applied to many estimation algorithms thanks to the advantage of AI techniques that have the ability of mapping between the input and the output, the so-called "black box". In this paper, we found and reviewed 55 papers that proposed KF with AI techniques to improve its performance. Based on the review, we categorised papers into four groups according to the role of AI as follows: 1) Methods tuning parameters of KF, 2) Methods compensating errors in KF, 3) Methods updating state vector or measurements of KF, and 4) Methods estimating pseudo-measurements of KF. In the concluding section of this paper, we pointed out the directions for future research that suggestion to focus on more research for combining the categorised groups. In addition, we presented the suggestion of beneficial approaches for representative applications

Augmented-Reality Visualization of Aerodynamics Simulation in Sustainable Cloud Computing

This paper proposes visualization based on augmented reality (AR) for aerodynamics simulation in a sustainable cloud computing environment that allows the Son of Grid Engine different types of computers to perform concurrent job requests. A simulation of an indoor air-purification system is performed using OpenFOAM computational fluid dynamics solver in the cloud computing environment. Post-processing converts the results to a form that is suitable for AR visualization. Simulation results can be displayed on devices, such as smart phones, tablets, and Microsoft HoloLens. This AR visualization allows for users to monitor purification of indoor air in real time

Clinical Implications of Bifurcation Angles in Left Main Bifurcation Intervention Using a Two-Stent Technique

Objectives. The aim of this study was to assess the clinical impact of 3 bifurcation angles in left main (LM) bifurcation treated with the 2-stent technique. Background. Data are limited regarding the impact of bifurcation angles after LM percutaneous coronary intervention (PCI). Methods. Using patient-level 4 multicenter registries in Korea, 462 patients undergoing LM bifurcation PCI with the 2-stent technique were identified (181 crush, 167 T-stenting; 63% 1st generation drug-eluting stent (DES), 37% 2nd generation DES). Three bifurcation angles, between the LM and left anterior descending (LAD), the LM and left circumflex (LCX), and the LAD and LCX, were measured. The primary outcome was target lesion failure (TLF), a composite of cardiac death, myocardial infarction, and target lesion revascularization (TLR). Results. In patients treated with the crush technique, the best cutoff value (BCV) to predict TLF was 152° of the LM-LAD angle. In the crush group, a significantly higher TLF rate, mostly driven by TLR, was observed in the LM-LAD angle ≥152° group compared with the <152° group (35.7% vs. 14.6%; adjusted hazard ratio 3.476; 95% confidence interval 1.612–7.492). An LM-LAD angle ≥152° was an independent predictor of TLF. In the T-stenting, no bifurcation angle affected the clinical outcomes. Conclusions. In LM bifurcation PCI using the 2-stent technique, wide LM-LAD angle (≥152°) was associated with a greater risk of TLF in the crush, whereas none of the bifurcation angles affected T-stenting outcomes