Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Analyzing VR and AR I4.0 technologies for industrial applications: A comparative study and selection approach development(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Chavez Najera, Daniela Monserrat; Ahuett Garza, Horacio; emipsanchez; Urbina Coronado, Pedro Daniel; Orta Castañón, Pedro Antonio; School of Engineering and Sciences; Campus MonterreyIn recent years, the implementation of immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) for Industry 4.0 (I4.0) applications has increased considerably. These technologies enable the connection of virtual and real environments focusing on human centered manufacturing. A challenge when implementing immersive technologies in industrial tasks is the lack of clear paths to select the most appropriate technology for specific operations, and the nonexistence of metrics to evaluate the integration performance. Nonetheless, there are trends in the literature that offer insights to conduct the decision making process for selection between immersive technologies, ensuring the suitability of the application. Based on the decision criteria identified in the literature a decision making approach is developed. This thesis also presents the development workflow of three VR/AR applications implemented in Unity Engine for Meta Quest 3 and Hololens 2. These applications are evaluated using overall performance metrics and are analyzed using the proposed approach.
- Use of reinforcement learning to help players improve their skills in Super Smash Bros. Melee(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-22) Estrada Valles, Jorge Alberto; Ramírez Uresti, Jorge Adolfo; puelquio/tolmquevedo; Morales Manzanares, Eduardo; Sosa Hernández, Víctor Adrián; Medina Pérez, Miguel Ángel; Ingenieria y Ciencias; Campus MonterreyeSports have become a huge industry in recent years which has led to more and more people being interested in competing as professional players, however not all players have the same opportunities as things like the current residence of the player are a huge factor. This is especially true for fighting games as people who live in small cities or countries usually have the problem of finding people with whom to practice and even then it may not be the best practice, so people opt to play against in-game AI which is also not good practice. Due to this problem new and more accessible ways for players to train must be created which is why a reinforcement learning solution is proposed. In this thesis, we present a solution using Proximal Policy Optimization to help people train when their best option is against the in-game AI. Furthermore, several additions, namely multiple time step actions, reward shaping, and specialized training; are suggested to optimize the created model to be used as a training partner by a human. To evaluate the effectiveness of the resulting model the game named Super Smash Bros. Melee was used to compare the improvement achieved by training against our bot and against the in-game AI. The results show that people that trained against the bot improved more than the people that trained against the AI, proving that it is a good way to help players train for eSport competitions.
