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Found 15 papers, 0 papers with code
Survey of Quantization Techniques for On-Device Vision-based Crack Detection
Structural Health Monitoring (SHM) ensures the safety and longevity of infrastructure by enabling timely damage detection.
Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it.
On-device Anomaly Detection in Conveyor Belt Operations
This study proposes two distinctive pattern recognition approaches for real-time anomaly detection in the operational cycles of mining conveyor belts, combining feature extraction, threshold-based cycle detection, and tiny machine-learning classification.
Towards Measuring Ethicality of an Intelligent Assistive System
There is a lack of research when it comes to analysing how such IAT adheres to provided ethical regulations due to ethical, logistic and cost issues associated with such an analysis.
An artificial neural network-based system for detecting machine failures using tiny sound data: A case study
The augmented dataset was created by combining these synthesized sounds with the original sounds.
Denoising Induction Motor Sounds Using an Autoencoder
In the scope of this paper, we demonstrate the removal of generated noise with Gaussian distribution and the environmental noise with a specific example of the water sink faucet noise from the induction motor sounds.
Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition
We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments.
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs
We study sensor-based human activity recognition in manual work processes like assembly tasks.
SimDem A Multi-agent Simulation Environment to Model Persons with Dementia and their Assistance
Developing artificial intelligence based assistive systems to aid Persons with Dementia (PwD) requires large amounts of training data.
Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease
Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases.
State-Space Abstractions for Probabilistic Inference: A Systematic Review
Furthermore, we provide new high-level categories that classify the approaches, based on common properties of the approaches.
Lifted Filtering via Exchangeable Decomposition
We present a model for exact recursive Bayesian filtering based on lifted multiset states.
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems
We sketch a novel inference algorithm that provides a solution by incorporating concepts from Lifted Inference algorithms, Probabilistic Multiset Rewriting Systems, and Computational State Space Models.
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress in the recent past.
