Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design