AIMC Topic: Online Systems

Clear Filters Showing 41 to 50 of 52 articles

Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network.

ISA transactions
Particle size soft sensing in cement mills will be largely helpful in maintaining desired cement fineness or Blaine. Despite the growing use of vertical roller mills (VRM) for clinker grinding, very few research work is available on VRM modeling. Thi...

A robust method for online heart sound localization in respiratory sound based on temporal fuzzy c-means.

Medical & biological engineering & computing
This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, th...

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

IEEE transactions on neural networks and learning systems
We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clause...

Discrete-Time Zhang Neural Network for Online Time-Varying Nonlinear Optimization With Application to Manipulator Motion Generation.

IEEE transactions on neural networks and learning systems
In this brief, a discrete-time Zhang neural network (DTZNN) model is first proposed, developed, and investigated for online time-varying nonlinear optimization (OTVNO). Then, Newton iteration is shown to be derived from the proposed DTZNN model. In a...

Adaptive neural control of nonlinear MIMO systems with time-varying output constraints.

IEEE transactions on neural networks and learning systems
In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation techn...

Learning to track multiple targets.

IEEE transactions on neural networks and learning systems
Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algo...

AI Supporting AAC Pictographic Symbol Adaptations.

Studies in health technology and informatics
The phenomenal increase in technological capabilities that allow the design and training of systems to cope with the complexities of natural language and visual representation in order to develop other formats is remarkable. It has made it possible t...

NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data.

Neuroinformatics
The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and hig...

The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu ), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpre...

Online prediction of glucose concentration in type 1 diabetes using extreme learning machines.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose an online machine-learning solution to the problem of nonlinear glucose time series prediction in type 1 diabetes. Recently, extreme learning machine (ELM) has been proposed for training single hidden layer feed-forward neural networks. Th...