Laser Dissimilar Joining of Al7075T6 with Glass-Fiber-Reinforced Polyamide Composite

Dissimilar joining between metal and composite sheets is usually carried out by mechanical or adhesive joining. Laser dissimilar joining between metal and composite sheets could be an alternative to these methods, as it is a cost-effective and versatile joining technique. Previously, textured metallic and composite parts have been held together and heated with a laser beam while pressure is applied to allow the melted polymer to flow into the cavities of the metal part. The main issue of this process relates to reaching the same joint strength repetitively with appropriate process parameters. In this work, both initial texturing and laser joining parameters are studied for Al 7075-T6 and glass-fiber-reinforced PA6 composite. A groove-based geometry was studied in terms of depth-to-width aspect ratio to find an optimal surface using a nanosecond fiber laser for texturing. Laser joining parameters were also studied with different combinations of surface temperature, heating strategy, pressure, and laser feed rate. The results are relatively good for grooves with aspect ratios from 0.94 to 4.15, with the widths of the grooves being the most critical factor. In terms of joining parameters, surface reference temperature was found to be the most influential parameter. Underheating does not allow correct material flow in textured cavities, while overheating also causes high dispersion in the resulting shear strength. When optimal parameters are applied using correct textures, shear strength values over 26 kN are reached, with a contact area of 35 × 45 mm2.This research was funded by the Basque Government grant number KK-2017/00088

Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours

Sistemas de predistorsión-linealización para enlaces ópticos

Ante el aumento de la demanda de conectividad de una sociedad cada vez más basada en las comunicaciones, la nueva generación de redes de transmisión apuesta claramente por la preeminencia de las tecnologías ópticas. No solo las redes troncales sino también en el acceso al hogar y en la alimentación de antenas de telefonía móvil y satélite. En este contexto en este TFC se diseñarán, se construirán y se medirán las prestaciones de circuitos de radiofrecuencia de predistorsión en el emisor y de ecualización en el receptor para la optimización de enlaces con amplificación óptica y detección directa en el receptor

General Video Game AI: Learning from screen capture

General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many different games using only a single learning algorithm

Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing

While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search

Rolling Horizon Coevolutionary planning for two-player video games

This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI. The approach is to extend recent work on rolling horizon evolutionary planning, which has been shown to work well for single-player games, to two (or in principle many) player games. To select an action the algorithm co-evolves two (or in the general case N) populations, one for each player, where each individual is a sequence of actions for the respective player. The fitness of each individual is evaluated by playing it against a selection of action-sequences from the opposing population. When choosing an action to take in the game, the first action is chosen from the fittest member of the population for that player. The new algorithm is compared with a number of general video game AI algorithms on a two-player space battle game, with promising results