A centering correction method for GNSS antenna diversity theory and implementation using a software receiver

GPS is performing well in open sky situation. However, severe attenuation or blockage of signals by high buildings may leads to an insufficient number of received satellites. Antenna diversity scheme is viewed as a method to alleviate signal attenuation and enhance the performance of GNSS positioning in the harsh environments. This paper introduces an antenna diversity system, composed of two spatially separated antennas. If relative geometry of two antennas is known, the carrier phase measurement outputs from these two antennas can be combined with Centering Correction Method (CCM). Even each antenna may not able to acquire more than four satellites this antenna diversity system can still precisely estimate each antenna’s location with centimeter-level accuracy, as long as the sum of the captured satellites by two separate antennas is no less than four

Biogas Production Potential and Kinetics of Microwave and Conventional Thermal Pretreatment of Grass

Pretreatment methods play an important role in the improvement of biogas production from the anaerobic digestion of energy grass. In this study, conventional thermal and microwave methods were performed on raw material, namely, Pennisetum hybrid, to analyze the effect of pretreatment on anaerobic digestion by the calculation of performance parameters using Logistic function, modified Gompertz equation, and transference function. Results indicated that thermal pretreatment improved the biogas production of Pennisetum hybrid, whereas microwave method had an adverse effect on the performance. All the models fit the experimental data with R-2&gt;0.980, and the Reaction Curve presented the best agreement in the fitting process. Conventional thermal pretreatment showed an increasing effect on maximum production rate and total methane produced, with an improvement of around 7% and 8%, respectively. With regard to microwave pretreatment, maximum production rate and total methane produced decreased by 18% and 12%, respectively.</p

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences attracts much attention and shows great improvements in the CTR field. Existing works mainly exploit attention mechanism based on embedding product when considering relations between user behaviors and target item. However, this methodology lacks of concrete semantics and overlooks the underlying reasons driving a user to click on a target item. In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction. Multiplex relations consist of meaningful semantics, which can bring a better understanding on users' interests from different perspectives. To explore and model multiplex relations, we propose to incorporate various graphs (e.g., knowledge graph and item-item similarity graph) to construct multiple relational paths between user behaviors and target item. Then Bi-LSTM is applied to encode each path in the path extractor layer. A path fusion network and a path activation network are devised to adaptively aggregate and finally learn the representation of all paths for CTR prediction. Extensive offline and online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202