GPS/VIO integrated navigation system based on factor graph and fuzzy logic.

In today's technologically advanced landscape, precision in navigation and positioning holds paramount importance across various applications, from robotics to autonomous vehicles. A common predicament in location-based systems is the reliance on Global Positioning System (GPS) signals, which may exhibit diminished accuracy and reliability under certain conditions. Moreover, when integrated with the Inertial Navigation System (INS), the GPS/INS system could not provide a long-term solution for outage problems due to its accumulated errors.

Employing a two stage process with resampling, decision rules, and fine acquisition to optimize the acquisition stage of GNSS receiver performance.

The optimal design of Global Navigation Satellite System (GNSS) software receivers should enable the accurate estimation of the receiver's position, velocity, and time under various environmental conditions. The software receivers consist of three sections: acquisition, tracking, and navigation. This paper specifically centers on the acquisition sections of the Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou, and Galileo satellite navigation systems.

Analysis of effective area and mass transfer in a structure packing column using machine learning and response surface methodology.

The study examined mass transfer coefficients in a structured CO absorption column using machine learning (ML) and response surface methodology (RSM). Three correlations for the fractional effective area (a), gas phase mass transfer coefficient (k), and liquid phase mass transfer coefficient (k) were derived with coefficient of determination (R) values of 0.9717, 0.

Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor.

Flue gas desulfurization (FGD) is a critical process for reducing sulfur dioxide (SO) emissions from industrial sources, particularly power plants. This research uses calcium silicate absorbent in combination with machine learning (ML) to predict SO concentration within an FGD process. The collected dataset encompasses four input parameters, specifically relative humidity, absorbent weight, temperature, and time, and incorporates one output parameter, which pertains to the concentration of SO.