Arquitectura y Tecnología de Computadores (Datos de Investigación)
URI permanente para esta colecciónhttps://hdl.handle.net/11441/168222
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Trabajo Fin de Grado Estudio de análisis de un sistema de control de la producción mediante simulación de eventos discretos(2025) Ruiz Olmo, Alejandro; Calle Suárez, Marcos; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas IEste documento presenta la implementación y análisis de un sistema de producción híbrido que integra estrategias Make to Order (MTO) y Make to Stock (MTS) en un entorno tipo JobShop, utilizando técnicas de simulación de eventos discretos mediante el lenguaje Python y la biblioteca SimPy. La problemática surge de la necesidad de gestionar eficientemente pedidos con características diferentes: los MTO, que se producen a contra pedido, y los MTS, que requieren mantener niveles de inventario para satisfacer la demanda inmediata. El modelo implementado toma como base el artículo “Integrating Make-to-Order and Make-to-Stock in Job Shop Control”, en el cual se proponen cuatro métodos de integración que regulan cómo deben ser priorizados los pedidos MTO y MTS en la producción. Cada método se ha codificado, simulado y evaluado bajo condiciones controladas con un objetivo común: analizar el trade-off entre el porcentaje de pedidos MTO tardíos y el porcentaje de ventas perdidas de MTS. Los resultados de la simulación permiten comparar el rendimiento de cada estrategia, analizar su comportamiento bajo distintas cargas del sistema y extraer conclusiones sobre qué métodos ofrecen una mayor eficiencia y adaptabilidad en entornos productivos híbridos.
Dataset Dataset of PaGER-Sync: Player Affective Gaming Experience and Responses - Synchronized ADICVIDEO(2025-06-24) Civit Masot, Javier; Luna Perejón, Francisco; Muñoz Saavedra, Luis; Civit Masot, Miguel; Domínguez Morales, Manuel Jesús; Miró Amarante, María Lourdes; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores; Miró Amarante, María Lourdes; Civit Masot, Javier; Luna Perejón, Francisco; Muñoz Saavedra, Luis; Miró Amarante, María Lourdes; Civit Masot, Javier; Luna Perejón, Francisco; Miró Amarante, María Lourdes; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP108: Robótica y Tecnología de ComputadoresThe PaGER-Sync dataset is a multimodal, temporally synchronized repository of physiological and facial expression data recorded during controlled, immersive video game sessions designed to simulate realistic home gaming environments. It integrates biosignals from the Empatica E4 wristband —including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (TEMP) — with facial expression features extracted from video recordings using FaceReader software. A total of 47 participants played three commercial video games (Tetris, Sonic Racing, and Fall Guys) under controlled conditions, while their physiological responses were continuously recorded and their facial expressions captured on video for subsequent analysis. All data streams were precisely aligned using a common video-based timestamp, enabling frame-level synchronization across modalities. The dataset supports a wide range of research applications in affective computing, human-computer interaction, and behavioral analysis, and is particularly well-suited for the development and evaluation of multimodal affect detection models. The data are segmented by game and include annotations with the top-1 and top-2 emotions, synchronized at second-level resolution.Dataset Neuromorphic Audio for Predictive Maintenance in Peristaltic Pumps [Dataset](2025-01-23) Montes-Sánchez, Juan Manuel; Domínguez Morales, Juan Pedro; Vicente Díaz, Saturnino; Jiménez Fernández, Ángel Francisco; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores; Montes-Sánchez, Juan Manuel; Agencia Estatal de Investigación. España; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; Universidad de Sevilla. TEP108: Robótica y Tecnología de computadoresThis dataset contains processed audio samples coming from a hydraulic block from a biomedical equipment. The block mounts 3 Thomas SR10/30 DC standard perisltaltic pumps, which were filled with distilled water. Only one pump was running at the same time during these recordings. There are two different predictive maintenance scenarios. In the first one, the cassettes of the pumps were changed before each recording. We used cassettes with 2 different levels of degradation: NEW (unused) and OLD (lifetime already expired). We defined 3 different classes: Class 1 is STOP (no pump running), class 2 is NEW (one pump running with a new cassette), and class 3 is OLD (one pump running with an old cassette). In the second scenario, air bubbles were introduced into the tube. This second scenario also has 3 classes: Class 1 is STOP (no pump running), class 2 is NORMAL (no air bubbles), and class 3 is BUBBLE (air bubbles present). A single microphone was used for all recordings. The .wav audio files were processed using a 64 channel Neuromorphic Auditory Sensor (NAS) into .aedat files, which are the present in this dataset. This neuromorphic audio data were also converted into cochleogram images using the software pyNAVIS, and they are also present in this format (.png files).Dataset Peristaltic pump aging detection dataset(2024-09-25) Montes-Sánchez, Juan Manuel; Uwate, Yoko; Nishio, Yoshifumi; Vicente Díaz, Saturnino; Jiménez Fernández, Ángel Francisco; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores; Montes-Sánchez, Juan Manuel; Agencia Estatal de Investigación. España; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; Universidad de Sevilla. TEP-108: Robótica y tecnología de computadoresThis dataset contains samples coming from a hydraulic block from a biomedical equipment. The block mounts 3 Thomas SR10/30 DC standard perisltaltic pumps, which were filled with distilled water. Only one pump was running at the same time during these recordings, always at maximum constant speed. The cassettes of the pumps were changed before each recording. We used cassettes with 2 different levels of degradation: NEW (unused) and OLD (lifetime already expired). We defined 3 different classes: Class 1 is STOP (no pump running), class 2 is NEW (one pump running with a new cassette), and class 3 is OLD (one pump running with an old cassette). The classified samples were recorded using several sensors: 3 accelerometers, 1 gyroscope, 1 magnetometer and 1 microphone. All data were recorded at the same time at the maximum available frequency using the device "ST SensorTile.box". The raw data has already been processed into sepparate different .csv files (.wav files for audio) using python code.