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Magnetic flux leakage (MFL) method is the most widely used non-destructive testing techniques for detection and characterization of defects in product transmission pipelines. The maximum safe operating pressure is a crucial parameter in practice, which is predicted with respect to the length, width, and depth of defects. In a previous work, an algorithm is devised to detect and estimate the width of the defect based on MFL signals. In this paper, an efficient method based on axial MFL level contours is devised to estimate the length of the defect. This method uses the patterns of signal level contours in the region corresponding to the defect’s area. In addition, a Gaussian radial basis function neural network (NN) is trained to approximate the depth of the defect. The NN is fed with the estimated length, width, and signal peak-to-peak values, and the output of the network is the estimated depth. The proposed detection and estimation method is applied to MFL measurements to detect and determine the sizing of metal loss defects. The efficacy and accuracy of the proposed methods are examined through a rich set of sample defects that contains simulated defects, designed defects that are carved on a real pipe by milling, and actual pigging data validated by several dig up verifications. Obtained results confirm the effectiveness and accuracy of the proposed depth and length estimation method along with the previously devised detection and width estimation methods, in characterization of metal loss defects. [ABSTRACT FROM AUTHOR]

In an incomplete information game, a big challenge is to find the best way of exploiting available information for optimal decision making of the agents. In this paper, two decision making methods, namely model-based and learning-based bidding strategies, are proposed and compared, for repeated Cournot competition of the generators in a day-ahead electricity market. The sum of the rivals’ offered quantities (SROQ) is considered as the state of the agent and its value is estimated using an adaptive expectation method. In the model-based approach, the convergence of the agents’ strategies to the Nash equilibrium point is also studied in two different cases. In the learning-based approach, the optimal bidding strategy is learned through combination of state estimation and a reinforcement learning method. Using the estimated state (SROQ), the optimal decision is learned through a fuzzy Q-learning algorithm. Through a case study, which is performed on the three-bus benchmark Cournot model, the convergence of the generators’ bids to the Nash-Cournot equilibrium is examined. [ABSTRACT FROM AUTHOR]