The importance of explicitly resolving the Stokes drift in ocean surface modelling is demonstrated by comparing four models with 58,612 observational data points obtained from undrogued drifting buoys Estuary and Gulf St. Lawrence, Canada. Drift model inputs are regional atmosphere circulation, spectral wave models. control considers near-surface currents provided top grid cell circulation mode...

In this paper, we explore computational learning models for learning concepts classes where the target concept changes with time, a scenario known in the literature as concept drift. The importance of this topic is immediately evident in the dynamic nature of real-world concepts; if a phenomenon can be represented by a static concept, then it often does not even require learning. The most obvio...

In the absence of visual feedback, subject reports of hand location tend to drift over time. Such drift has been attributed to a gradual reduction in the usefulness of proprioception to signal limb position. If this account is correct, drift should degrade the accuracy of movement distance and direction over a series of movements made without visual feedback. To test this hypothesis, we asked p...

Psychophysical studies reveal distortions in perception of distance and shape. Are reaches calibrated to eliminate distortions? Participants reached to the front, side, or back of a target sphere. In Experiment 1, feedforward reaches yielded distortion and outward drift. In Experiment 2, haptic feedback corrected distortions and instability. In Experiment 3, feedforward reaches with only haptic...

Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on th...

Data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such distribution change in streaming data is called concept drift. Numerous histogram-based detection methods have been proposed detect Most histograms are developed on grid-based or tree-based space partitioning algorithms which makes ...

In recent years, stream data have become an immensely growing area of research for the database, computer science and data mining communities. Stream data is an ordered sequence of instances. In many applications of data stream mining data can be read only once or a small number of times using limited computing and storage capabilities. Some of the issues occurred in classifying stream data tha...

This paper analyzes the behavior of a variety of tracking algorithms for single layer threshold networks in the presence of random drift. We use a system identiication model to model a target network where weights slowly change and a tracking network. Tracking algorithms are divided into conservative and nonconservative algorithms. For a random drift rate of , we nd upper bounds for the general...

The efficiency of applications through spraying is connected to the capacity minimize losses by endo-drift and exo-drift while ensuring adequate coverage deposition on target surface; spray nozzles main factor application quality. However, few researches focus technology in black pepper crops. objective this work was evaluate droplets simulated herbicide using different nozzles. experiment cond...