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Found 14 papers, 1 papers with code
Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation
This time-series is processed using quaternion valued neural network layers, where we aim to preserve the relation between these features through the usage of the Hamilton product.
Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learning
Ultimately, our approach not only demonstrates a robust ability to generalize across various instance sizes but also leverages the Petri net's graph nature to dynamically add job operations during the inference phase without the need for agent retraining, thereby enhancing flexibility.
Information Fusion for Assistance Systems in Production Assessment
We propose a novel methodology to define assistance systems that rely on information fusion to combine different sources of information while providing an assessment.
Self Optimisation and Automatic Code Generation by Evolutionary Algorithms in PLC based Controlling Processes
The digital transformation of automation places new demands on data acquisition and processing in industrial processes.
Quaternion Backpropagation
Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts.
Anomaly Detection using Ensemble Classification and Evidence Theory
We present an architecture for multi-class ensemble classification and an approach to quantify the uncertainty of the individual classifiers and the ensemble classifier.
AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection
Object detection networks have reached an impressive performance level, yet a lack of suitable data in specific applications often limits it in practice.
Predicting Rigid Body Dynamics using Dual Quaternion Recurrent Neural Networks with Quaternion Attention
We propose a novel neural network architecture based on dual quaternions which allow for a compact representation of informations with a main focus on describing rigid body movements.
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell
To this end, we apply a curiosity based reinforcement learning, using intrinsic motivation as a form of reward, on a flexible robot manufacturing cell to alleviate this problem.
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning
The proposed GraSP-RL outperforms the FIFO, TS, and GA for the trained task of planning 30 jobs in JSSP.
Gradient Monitored Reinforcement Learning
The proposed method which we term as Gradient Monitoring(GM), is an approach to steer the learning in the weight parameters of a neural network based on the dynamic development and feedback from the training process itself.
Neural Logic Rule Layers
Compared to e. g. decision trees or bayesian classifiers, DNN suffer from bad interpretability where we understand by interpretability, that a human can easily derive the relations modeled by the network.
Learning the Non-linearity in Convolutional Neural Networks
The weights of this matrix then constitute the exponents of the corresponding components of the receptive field.
Generalized Dilation Neural Networks
One of the strengths of convolutional layers is the ability to learn features about spatial relations in the input domain using various parameterized convolutional kernels.
