Wenqiang Guo

Bayesian network learning based on relationship prediction PSO and its application in agricultural expert system

To resolve the problem for modeling agricultural expert system effectively in the complicated and uncertain agricultural production system, a Bayesian network learning algorithm based on relationship prediction Particle Swarm Optimization (PSO) is proposed. A successful interpretation of data goes through discovering crucial relationships among variables, and such a task can be accomplished by a Bayesian network. However, when lots of variables are involved, the learning of the network slows down and may lead to wrong results. In this study, we demonstrate the feasibility of applying an existing Particle Swarm Optimization (PSO)-based approach with mutual information for filtering the irrelevant attributes of the dataset, resulting in candidate Bayesian networks which provide the optimization direction for BN learning and searching. Experimental tests carried out with both artificial data and real data coming from the agricultural domain. Experimental results demonstrate that the presented algorithm is effective and efficient, which can be used in the agricultural expert system.

Bayesian Network Based Cooperative Area Coverage Searching for UAVs

To resolve the issue of cooperative searching in a given area by a team of heterogeneous UAVs, taking into account their different sensing and range capabilities, based on Bayesian network, this paper contributes a hierarchical structure for cooperative UAVs search mission area decomposition system. A novel multiple UAV cooperative search area decomposition algorithm based on proposed UAV working capability evaluation Bayesian network is also proposed. The ability of coping with uncertainty, which makes this approach notably appealing for real-time implementation, is empirically verified by simulations. The experimental results demonstrate that the presented approach is effective and efficient in the multiple UAVs cooperative search area decomposition problem.

Graphical model-based recursive motion prediction planning algorithm in stochastic dynamic environment

Various types of autonomous vehicles(AVs) are used widely in the field of military and civilian. Aiming at the difficulty of the real-time intelligent planning of the AVs in the dynamic and uncertain complex environment, a more generalized graphical model-based planning frame and algorithm is studied in this paper. To plan the waypoints for AVs in stochastic environment, a dynamic Bayesian network-based recursive motion prediction planning (RMPP) algorithm is designed. The uncertainty object model and the dynamic utility function have been analyzed. Dynamic Bayesian network, which is one of the graphical models, has been verified to predict the mobile target status. RMPP helps to convert an uncertainty optimization into a deterministic problem with optimizing the waypoints allocation under the constraints which maximizes the utility score in dynamic environment. This approach is implemented and tested on the autonomous vehicle path planning problem. Experimental results demonstrate a substantial effectiveness in computation cost.

Early classification for bearing faults of rotating machinery based on MFES and Bayesian network

Bearing faults of rotating machinery are observed as impulses in the vibration signal, but it is mostly immersed in noise. In order to effectively remove this noise and detect the impulses, a novel technique with multiple frequency energy spectrum (MFES) and Bayesian network(BN) inference is proposed in this paper. Original acceleration signals are processed by fast Fourier transformation (FFT) from the time domain to frequency domain. According to the analysis of the frequency information, the MFES is put forward to extract features from vibration under normal and faulty conditions of rotational mechanical systems. These features were given as inputs for training and testing the BN model. By existing BN inference algorithms, and the inference result for fault diagnosis is provided. With BN inference algorithms being coupled to this new technique, it makes the presented approach be able to detect early faults. Experimental results show that the proposed approach is effective and robust in bringing out the early bearing fault classification of rotating machinery.

A novel fault diagnosis for vehicles based on time-varied Bayesian network modeling

Aiming at one of the key issues in vehicle fault diagnosis underlying time series, modeling the varying diagnosis network structures is investigated in this paper. By incorporating machine learning techniques with the Bayesian networks advantage of handling the inference in large, noisy and uncertain data, an innovative method based on modeling the varied-time Bayesian network (BN) for automotive vehicle fault diagnosis is presented. The architecture of an intelligent fault diagnosis system using time-varied Bayesian network modeling is designed, and a fault diagnosis algorithm for vehicles based on time-varied Bayesian network modeling is also advanced. Since the proposed topological model scheme can be modified by learning from the new arriving observation time series data, the inference results under modified BN structures can be improved better. Theoretical analysis about the modeling the network issues are studied in details. The proposed method has been practically applied to model a vehicle engine system. Experimental results demonstrate this automotive fault diagnosis approach based on time-varied Bayesian network modeling is effective and accurate.

Driver drowsiness detection model identification with Bayesian network structure learning method

To resolve the problem for modeling driver drowsiness detection system and automatically and effectively in the complicated and uncertain traffic conditions, a Bayesian network structure learning algorithm based on node ordering prediction Particle Swarm Optimization (NOP-PSO) is proposed. A successful interpretation of data goes through discovering crucial relationships among variables, and such a task can be accomplished by a Bayesian network. In this study, we demonstrate the feasibility of applying an existing PSO-based approach with mutual information for filtering the irrelevant attributes of the dataset, resulting in candidate Bayesian networks which provide the optimization direction for BN learning and searching. Then the benchmark Incinerator structure learning problem is used to verify the presented approach with other approaches. With both artificial data and real data coming from the driving measurements, experimental results demonstrate that the presented algorithm is more effective and efficient than original PSO method or radius basic function neural network one, which can be used in the driver drowsiness detection model identification.

Optimal coordination of multi-task allocation and path planning for UAVs using Dynamic Bayesian Network

A key challenge for the Unmanned Aerial Vehicles (UAVs) is to develop an overall system architecture that can perform optimal coordination of the UAVs and reconfigure to account for changes in the dynamic environment with uncertainty. This paper presents a multi-task allocation and path planning optimal coordination algorithm for UAVs based on Dynamic Bayesian Network (DBN) perceiving architecture, which leads to solve above autonomous problems in dynamic aerospace surroundings. Learning and inference will be based on Bayesian approach, by representing uncertainty in observed data, and by using probability techniques to compute the goal attributes given the observation data. Under given missions and guidelines, learning, inference and prediction can be carried out by the same principle and these clarify the new direction for the decision-making optimization. The valid overall approach is demonstrated on example scenarios which show that, during execution, the coordination tasks of multi-task allocation and path planning for UAVs, which react to changes in the dynamic aerospace environments, can be achieved autonomously.