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Found 9 papers, 0 papers with code
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis
Due to the incapability of one sensory measurement to provide enough information for condition monitoring of some complex engineered industrial mechanisms and also for overcoming the misleading noise of a single sensor, multiple sensors are installed to improve the condition monitoring of some industrial equipment.
Adaptive output feedback tracking control for bilinear systems
This paper deals with the trajectory tracking control problem for a class of bilinear systems with unmeasurable states and unknown parameters.
Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization
There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems.
Time-domain Classification of the Brain Reward System: Analysis of Natural- and Drug-Reward Driven Local Field Potential Signals in Hippocampus and Nucleus Accumbens
We developed novel approaches to classify LFP signals into the source of reward and recorded regions by considering the time-domain feature of these signals.
Adaptive Model Learning of Neural Networks with UUB Stability for Robot Dynamic Estimation
Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification.
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System
In this paper, with the help of a few sensors and data mining approaches, an anomaly detection system is built for CHRIST Osmotron water purifier.
A Novel Method For Designing Transferable Soft Sensors And Its Application
In this paper, a new approach is proposed for designing transferable soft sensors.
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM
Gibbs sampling is an algorithm in which instances are likely to transfer to a new state with a higher possibility with respect to a probability distribution.
Improving the Izhikevich Model Based on Rat Basolateral Amygdala and Hippocampus Neurons, and Recognizing Their Possible Firing Patterns
This aim was achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns, and eliminating unrealistic ones.
