Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
Gradually Varying Flow01:29

Gradually Varying Flow

Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
Stream Function01:20

Stream Function

In two-dimensional incompressible fluid flow, the continuity equation is essential for ensuring mass conservation, meaning that any change in fluid entering or exiting a region is balanced by a corresponding change elsewhere. For incompressible flow, where density remains constant, this requirement simplifies to the condition that the divergence of the velocity field must be zero. Mathematically, this is expressed as,
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Downstream Processing01:29

Downstream Processing

Downstream processing begins once fermentation is complete and involves a series of steps to recover and purify products such as acids, vitamins, antibiotics, or proteins.Cell HarvestingFor example, for intracellular protein-based products, the first step is harvesting the cells. This is typically achieved using centrifugation or filtration to separate the cells from the liquid phase.Cell Disruption for Intracellular ProductsIf the target product is intracellular, the harvested cells must be...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

WormSORT: A detection-based multiple object tracking model for individual silkworms in breeding environments.

PLoS computational biology·2026
Same author

Research progress on chemical metabolites, processing technologies, and pharmacological activities of asperosaponin VI: a systematic review and critical evaluation.

Frontiers in pharmacology·2026
Same author

Artificial intelligence-assisted detection of epileptic spasms using electroencephalographic-video analysis.

Epilepsia·2026
Same author

Epidemiological characteristics and incidence prediction analysis of brucellosis in Bayingolin mongol autonomous prefecture, Xinjiang.

BMC infectious diseases·2026
Same author

Patient satisfaction and its influencing factors: results from a survey in inpatient department in a tertiary hospital setting in China.

BMC health services research·2026
Same author

Ecological regime shifts weaken sedimentary carbon sequestration in shallow Lake liangzi.

Water research·2025
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Videos

Incremental learning from stream data.

Haibo He1, Sheng Chen, Kang Li

  • 1Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA. he@ele.uri.edu

IEEE Transactions on Neural Networks
|November 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces ADAIN, an adaptive incremental learning framework for continuous data streams. ADAIN effectively transforms raw data into knowledge, enhancing future learning and prediction performance.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Incremental learning addresses continuously available data streams, challenging traditional machine learning assumptions.
  • The need exists to process vast raw data into knowledge for ongoing decision-making.
  • Existing methods struggle with accumulating experience from dynamic data flows.

Purpose of the Study:

  • To propose a general adaptive incremental learning framework, ADAIN.
  • To enable learning from continuous raw data and accumulating experience over time.
  • To enhance future learning and prediction performance using accumulated knowledge.

Main Methods:

  • Development of a general adaptive incremental learning framework named ADAIN.
  • Implementation of strategies for transforming stream raw data into information and knowledge representation.
  • System-level architecture and design strategies for continuous learning.

Main Results:

  • ADAIN demonstrates effectiveness in learning from continuous raw data.
  • The framework successfully accumulates experience over time.
  • Simulation results validate the method's ability to improve future performance.

Conclusions:

  • ADAIN provides a robust solution for incremental learning from continuous data streams.
  • The framework effectively leverages accumulated knowledge for enhanced predictions.
  • The proposed method shows significant potential for real-world applications involving dynamic data.