The recently proposed factor graph optimization (FGO) is adopted to integrate GNSS/INS which attracted lots of attention and improved the performance over the existing EKF-based GNSS/INS integrations. However, a comprehensive comparison of those two GNSS/INS integration schemes in the urban canyon ...
However, the potential of high-precision positioning by using carrier phase observations is not fully explored. A factor graph optimization-based multi-GNSS real-time kinematic (FGO-RTK) framework is proposed to fill this gap, aiming to realize robust and precise positioning in urban canyons. In ...
The process of implementing a belief propagation network in software and/or hardware can begin with a factor-graph-designer who designs a factor graph that implements that network. A development system provides a user with a way to specify a factor graph at a high or abstract level, and then...
Graph theory has proved a powerful and elegant tool that has extensively been used in optimization and computational theory. This chapter is the first of two chapters dedicated to probabilistic graphical models. Bayesian networks are introduced and the Markov condition is discussed. The d-separation ...
Resilient navigationIndoor positioningFactor graph optimizationUltra-wide band(UWB)Based on the high positioning accuracy,low cost and low-power consumption,the... Q Meng,Y Song,SY Li,... - 《Defence Technology》 被引量: 0发表: 2023年 Neural Network Aided Factor Graph Optimization for Collaborati...
In contrast, inertial navigation can easily handle high-speed navigation and provides pose estimates at a world scale. By combining the advantages of both camera and IMU data through tightly coupled factor graph optimization, one can achieve better accuracy. For efficient execution time performance, ...
The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatm
SupirFactor combines the power of DNN optimization with prior structure constraints for inferring GRNs and explicit estimation of TFA. These TFA estimates are bounded by a ReLU activation function and are directly quantifiable and interpretable on a per-observation basis. Additionally, in this work, ...
Selecting an optimal group of hyper-parameters from candidates is an optimization problem. Overall, a model that enables efficient representation learning to high-dimensional and incomplete data, as well as hyper-parameters self-adaptation simultaneously is required. Proposed model: To avoid operations ...
Fig. 12.8shows a match between the measured and model-calculated pressure responses given by an optimization technique. This match was obtained using the following parameter values: Sign in to download full-size image Figure 12.8.Match between measured and model calculated pressure data. ...