AIMC Topic: Algorithms

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Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks.

Sensors (Basel, Switzerland)
Guided waves are a potent tool in structural health monitoring, with promising machine learning algorithm applications due to the complexity of their signals. However, these algorithms usually require copious amounts of data to be trained. Collecting...

Distributed adaptive fixed-time neural networks control for nonaffine nonlinear multiagent systems.

Scientific reports
This paper, with the adaptive backstepping technique, presents a novel fixed-time neural networks leader-follower consensus tracking control scheme for a class of nonaffine nonlinear multiagent systems. The expression of the error system is derived, ...

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

Scientific reports
The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. ...

Proactive approach for preamble detection in 5G-NR PRACH using supervised machine learning and ensemble model.

Scientific reports
The physical random access channel (PRACH) is used in the uplink of cellular systems for initial access requests from the users. It is very hard to achieve low latency by implementing conventional methods in 5G. The performance of the system degrades...

An image classification deep-learning algorithm for shrapnel detection from ultrasound images.

Scientific reports
Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence al...

Inference-Based Posteriori Parameter Distribution Optimization.

IEEE transactions on cybernetics
Encouraging the agent to explore has always been an important and challenging topic in the field of reinforcement learning (RL). Distributional representation for network parameters or value functions is usually an effective way to improve the explor...

Evolving Connections in Group of Neurons for Robust Learning.

IEEE transactions on cybernetics
Artificial neural networks inspired from the learning mechanism of the brain have achieved great successes in machine learning, especially those with deep layers. The commonly used neural networks follow the hierarchical multilayer architecture with ...

Symmetric All Convolutional Neural-Network-Based Unsupervised Feature Extraction for Hyperspectral Images Classification.

IEEE transactions on cybernetics
Recently, deep-learning-based feature extraction (FE) methods have shown great potential in hyperspectral image (HSI) processing. Unfortunately, it also brings a challenge that the training of the deep learning networks always requires large amounts ...

Practical Exponential Stability of Impulsive Stochastic Reaction-Diffusion Systems With Delays.

IEEE transactions on cybernetics
This article studies the practical exponential stability of impulsive stochastic reaction-diffusion systems (ISRDSs) with delays. First, a direct approach and the Lyapunov method are developed to investigate the p th moment practical exponential stab...

Gaussian Mixture Model and Self-Organizing Map Neural-Network-Based Coverage for Target Search in Curve-Shape Area.

IEEE transactions on cybernetics
This article focuses on the target search problem in a curve-shape area using multiple unmanned aerial vehicles (UAVs), with the demand for obtaining the maximum cumulative detection reward, as well as the constraint of maneuverability and obstacle a...