In this paper, we propose new separated decision variables that are derived by directly using the normal distribution of each measurement error and allowing substitution of the chi-square variable of the conventional method. In the derivation of the proposed decision variables, we considered not only the related mathematical model, but also the additional unmodelled properties of GPS measurements. Using the sequential pseudo-moving-average technique, we developed a method that easily obtains the combined results of multiple epochs. To verify our proposed algorithm, we analysed its performance using real data and compared the results with those of the conventional method. Our proposed approach performs better than the conventional approach, and effectively reduces computational effort by approximately 60%. Our results demonstrate that our method achieves a solution that is as reliable as the conventional technique, while reducing the time required to only 15% of that required by the conventional technique.
An inexpensive and effective technique based on machine learning (ML) algorithms with impedance characterization to sense and classify mixed gases is presented. Specifically, this method demonstrates that ML algorithms can distinguish hidden and valuable feature information such as different gas molecules from surface‐charged activated carbon fibers. The feature information used for ML is obtained by measuring the impedance and fitting the measured values to an equivalent circuit model. The mixed gases are classified using such feature information to train various automatic classifiers. The collected data consist of the resistances and capacitances extracted from best fitting results in Cole–Cole plots, and they are 5D vectors. The data processed with unsupervised learning are clustered, evaluated with Silhouette scores, and then the unique hidden patterns of individual gases in the mixed gases are obtained. When the supervised ML algorithm, k‐nearest neighbor classifier, is used for the analytical features, all combinations of gases have 94% classification accuracy, demonstrating the superiority of the proposed technique.
This paper proposes an airborne transceiver system as an alternative navigation system and a triangulation method using bidirectional range measurements as a method of transceiver position determination. We suggest several system arrays that can estimate each mobile transceiver position in real time. We found that our suggested alternative navigation system working in a 700 kmr900 km region is feasible using only 10 transceivers at an altitude of 42 km, furthermore its performance can compete with that of Galileo's Open Service. This paper will contribute to the establishment of an alternative or backup navigation system with modest expenditure and a short development period, and which is independent of GPS.K E Y W O R D S 1. Transceiver. 2. Regional navigation system. 3. Alternative navigation system.
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