Multi-source information fusion based on factor graph in autonomous underwater vehicles navigation systems

Purpose This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to address the asynchronous and heterogeneous problem of multiple sensors. Design/methodology/approach The factor graph is formulated by joint probability distribution function (pdf) random variables. All available measurements are processed into an optimal navigation solution by the message passing algorithm in the factor graph model. To further aid high-rate navigation solutions, the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU measurements in the factor graph model. Findings The proposed factor graph was demonstrated both in a simulated and vehicle environment using IMU, Doppler Velocity Log, terrain-aided navigation, magnetic compass pilot and depth meter sensors. Simulation results showed that the proposed factor graph processes all available measurements into the considerably improved navigation performance, computational efficiency and complexity compared with the un-simplified factor graph and the federal Kalman filtering methods. Semi-physical experiment results also verified the robustness and effectiveness. Originality/value The proposed factor graph scheme supported a plug and play capability to easily fuse asynchronous heterogeneous measurements information in AUV navigation systems.

Detection of Stress Level in Social Media users using Cnn-Fg Model

Psychological stress has become a common condition in today's world owing to the busy life style and competitive environment. This has led to increase of suicidal rates in the recent years. Lately, there has been a tremendous increase in interactions in the social networking sites. As people are spending long hours in the virtual world it is easier to detect and analyze the stress levels of the social media users. In this paper, we have proposed a hybrid approach which is a combination of Factor Graph (FG) model and Convolutional Neural Network (CNN) to analyze the textual contents in social media users’ tweets and posts to detect the level of stress of a user. The tweets of an individual user are gathered from Twitter platform which is preprocessed and passed to the cross autoencoder embedded CNN Model which outputs user level attributes. These are then input to the Factor Graph model that detects the stressed tweets. A mechanism has been proposed to inform the friends or relatives of the concerned stressed user if the detected stress level is above the given threshold

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.