Prediction of Seaward Slope Recession in Berm Breakwaters Using M5＇ Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim＇s and Shekari＇s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5＇ model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5＇ approach.

Modeling of moral decisions with deep learning

One example of an artificial intelligence ethical dilemma is the autonomous vehicle situation presented by Massachusetts Institute of Technology researchers in the Moral Machine Experiment.To solve such dilemmas,the MIT researchers used a classic statistical method known as the hierarchical Bayesian(HB)model.This paper builds upon previous work for modeling moral decision making,applies a deep learning method to learn human ethics in this context,and compares it to the HB approach.These methods were tested to predict moral decisions of simulated populations of Moral Machine participants.Overall,test results indicate that deep neural networks can be effective in learning the group morality of a population through observation,and outperform the Bayesian model in the cases of model mismatches.

Simulation and experimental investigation on a dynamic lateral flow mode in trepanning electrochemical machining

An appropriate flow mode of electrolyte has a positive effect on process efficiency, surface roughness, and machining accuracy in the electrochemical machining（ECM） process. In this study, a new dynamic lateral flow mode, in which the electrolyte flows from the leading edge to the trailing edge, was proposed in trepanning ECM of a diffuser. Then a numerical model of the channel was set up and simulated by using computational fluid dynamics software. The result showed that the distribution of the flow field was comparatively uniform in the inter-electrode gap. Furthermore, a fixture was designed to realize this new flow mode and then corresponding experiments were carried out. The experimental results illustrated that the feeding rate of the cathode reached 2 mm/min, the best taper angle was about 0.4°, and the best surface roughness was up to Ra= 0.115 lm. It reflects that this flow mode is suitable and effective, and can also be applied to machining other complex structures in trepanning ECM.