Efficient demand forecasting can yield significant competitive advantage for companies in today’s market, making it a crucial process in supply chain management. By exploring and implementing modern demand forecasting models, encompassing time-series, machine…
This thesis investigates the applicability and performance of reinforcement learn- ing (RL) for inventory management of spare parts in a single-echelon setting. Traditional inventory control methods, such as (R,Q)-policies, typically decou- ple forecasting and…
Reinforcement learning is a machine learning paradigm that aims to develop optimal policies for discrete-time control problems based on quantitative feedback on the learner’s behaviour. Reinforcement learning is formulated as a gradient-based optimisation proc…
This thesis investigated how machine learning-based anomaly detection can be applied to support predictive maintenance for a radar system using available monitoring data. An LSTM autoencoder was trained on selected healthy running operation and used to detect…
Este trabajo propone el diseño e implementación de un gemelo digital para la monitorización de la degradación de un filtro industrial además de la predicción de su vida útil restante (Remaining Useful Life, RUL). Para conseguirlo se usó un conjunto de datos fo…
Modern manufacturing is progressing towards Industry 4.0 and Robotics 4.0, which puts the industrial robots in the focus of contemporary manufacturing. These sophisticated robots keep on producing multi-sensor rich data, which depicts their internal kinematics…
No contexto da Indústria 4.0, a manutenção preditiva desempenha um papel fundamental na otimização do desempenho dos ativos e na redução dos custos operacionais. Esta tese apresenta uma nova estrutura de aprendizagem ativa projetada para melhorar a eficiência…
Recent years have seen instability in the electricity market and increasing energy demands in the world. Reliable electricity consumption forecasting is a helpful tool for consumers and other stakeholders to manage energy consumption more efficiently. This the…
Modern artificial intelligence (AI) has achieved success by learning statistical patterns from data and framing many problems as prediction tasks. However, prediction alone cannot answer questions such as why an outcome occurred or how an intervention would ch…
This dissertation presents an explainable hybrid artificial intelligence framework for stock market prediction using financial time-series information and market sentiment. The study investigates the integration of historical market observations, technical ind…
With the onset of artificial intelligence (AI), machine learning (ML) approaches have become increasingly embedded into the drug discovery pipeline with the aim of accelerating research and reducing cost. Exemplary applications include quantitative structure-p…
As the demand for energy-efficient Artificial Intelligence (AI) grows, Spiking Neural Networks (SNNs) have emerged as a leading neuromorphic alternative to traditional deep learning. However, the complex, non-linear temporal dynamics of SNNs often result in a…
أنا مساعد المستودع. اسألني عن الرسائل والأطاريح حسب الموضوع أو الجامعة أو الدولة أو السنة أو الدرجة، أو اطلب أعداداً وإحصاءات.
يجيب من قاعدة بيانات المستودع فقط، ولا يخترع سجلات أو أرقاماً. لا يُحفظ ما تكتبه على الخادم.
قد يخطئ المساعد أحياناً، فراجع المصادر المرفقة.
