A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting

This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relies on through-the-sensor (TTS) assessment, by which the sensors’ performances can be measured in situ through processing of their recorded data, yielding the local mine recognition probability, and false alarm rate. The method constructs a risk map of the minefield area composed of small grid cells (~4 m2) that are colour coded according to the remaining mine probability. The new approach can produce this map using the available evidence whenever decision support is needed during the mine hunting operation, e.g., for replanning purposes. What distinguishes the new technique from other recent TTS methods is its use of Bayesian networks that facilitate more complex reasoning within each grid cell. These networks thus allow for the incorporation of two types of evidence not previously considered in evaluation: the explosions that typically result from mine neutralization and verification of mine destruction by visual/sonar inspection. A simulation study illustrates how these additional pieces of evidence lead to the improved estimation of the number of deployed mines (M), compared to results from two recent TTS evaluation approaches that do not use them. Estimation performance was assessed using the mean squared error (MSE) in estimates of M.

A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles

The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy.

A Bayesian fusion technique for maximum power point tracking under partial shading condition

In this paper, a Bayesian fusion technique (BFT) based on maximum power point tracking (MPPT) is developed for the photovoltaic (PV) system that can exhibit faster and accurate tracking under partially shaded conditions (PSCs). Although the conventional hill-climbing algorithms have fast tracking capabilities, they are prone to steady-state oscillations and may not guarantee global peak under partially shaded conditions. Contrarily, the meta-heuristic-based techniques may promise a global peak solution, but they are computationally inefficient and take significant time for tracking. To address this problem, a BFT is proposed which combines the solutions obtained from conventional incremental conductance algorithm and Jaya optimization algorithm to produce better responses under various PSCs. The effectiveness of the proposed BFT-based MPPT is evaluated by comparing it with various MPPT methods, viz. incremental conductance, particle swarm optimization (PSO), and Jaya optimization algorithms in MATLAB/Simulink environment. From the various case studies carried, the overall average tracking speed with more than 99% accuracy is less than 0.25 s and having minimum steady-state oscillations. Even under the wide range of partially shaded conditions, the proposed method exhibited superior MPPT compared to the existing methods with tracking speed less than 0.1 s to achieve 99.8% tracking efficiency. A detailed comparison table is provided by comparing with other popular existing MPPT methodologies.