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

Reinforcement01:23

Reinforcement

901
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
901
Corrosion of Reinforcement01:27

Corrosion of Reinforcement

567
The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
However, over time and under certain conditions like carbonation, chloride ingress, and cracking this protective state can be compromised. Steel has areas with...
567
Reinforcement Schedules01:24

Reinforcement Schedules

494
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
494
Reinforcements in Concrete01:25

Reinforcements in Concrete

461
Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
461
Fiber Reinforced Concrete01:22

Fiber Reinforced Concrete

380
Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
380
Reinforced Brick Masonry01:15

Reinforced Brick Masonry

1.7K
Reinforced brick masonry is an advanced construction technique that enhances the structural integrity of brick walls by incorporating steel reinforcements. These reinforcements are either placed within the hollow cores of bricks or sandwiched between two layers of masonry, known as wythes, and are then secured in place with grout. Grout is a fluid mixture composed of Portland cement, aggregate, and water, providing the necessary bonding agent for the steel and brick.
To fortify brick walls...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Peripheral and central vestibular neuromodulation improve postural control in adolescent idiopathic scoliosis: a randomized, sham-controlled, multi-arm intervention study.

Journal of neuroengineering and rehabilitation·2026
Same author

scCCVGBen for benchmarking of single-cell representation learning anchored on a centroid-coupled variational graph attention autoencoder across scRNA-seq and scATAC-seq.

Frontiers in genetics·2026
Same author

Development of APH003─a Highly Potent, Selective, and Orally Bioavailable IRAK4 PROTAC Degrader for the Treatment of Inflammatory Diseases.

Journal of medicinal chemistry·2026
Same author

Reduced HAV IgG Seropositivity Among Unvaccinated People Living with HIV: The Weak Shield.

Tropical medicine and infectious disease·2026
Same author

Immunosuppression, resistance burden, and qSOFA on short-term prognosis and difficult clearance in hospitalized patients with Salmonella infection: a single-center retrospective cohort study.

BMC infectious diseases·2026
Same author

LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.

Frontiers in genetics·2026
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Reinforcement Learning-Based Optimal Sensor Placement for Spatiotemporal Modeling.

Zhi Wang, Han-Xiong Li, Chunlin Chen

    IEEE Transactions on Cybernetics
    |March 21, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a reinforcement learning approach for optimal sensor placement in distributed parameter systems (DPSs). The method effectively minimizes modeling errors by learning optimal sensor configurations.

    More Related Videos

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
    11:18

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

    Published on: June 1, 2015

    11.1K
    Synthesis, Cellular Delivery and In vivo Application of Dendrimer-based pH Sensors
    16:19

    Synthesis, Cellular Delivery and In vivo Application of Dendrimer-based pH Sensors

    Published on: September 10, 2013

    12.2K

    Related Experiment Videos

    Last Updated: Jan 27, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.4K
    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
    11:18

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

    Published on: June 1, 2015

    11.1K
    Synthesis, Cellular Delivery and In vivo Application of Dendrimer-based pH Sensors
    16:19

    Synthesis, Cellular Delivery and In vivo Application of Dendrimer-based pH Sensors

    Published on: September 10, 2013

    12.2K

    Area of Science:

    • Control Engineering
    • Applied Mathematics
    • Machine Learning

    Background:

    • Distributed Parameter Systems (DPSs) require effective sensor placement for accurate modeling.
    • Traditional methods face challenges in optimizing sensor locations for complex spatial dynamics.

    Purpose of the Study:

    • To develop a reinforcement learning-based method for optimal sensor placement in DPSs.
    • To minimize modeling errors across the entire time-space domain of DPSs.

    Main Methods:

    • Utilizing Karhunen-Loève decomposition to identify dominant dynamic features in a low-dimensional subspace.
    • Defining a spatial objective function within this subspace to guide sensor placement.
    • Formulating sensor configuration as a Markov decision process (MDP).
    • Optimizing sensor locations by learning optimal MDP policies.

    Main Results:

    • Demonstrated feasibility and efficiency on a simulated catalytic rod system.
    • Validated effectiveness on a real snap curing oven system.
    • Successfully addressed combinatorial optimization challenges in sensor placement.

    Conclusions:

    • The proposed reinforcement learning method offers an efficient solution for optimal sensor placement in DPSs.
    • This approach effectively reduces modeling errors by learning optimal sensor configurations.
    • The technique is applicable to both simulated and real-world distributed systems.