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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

134
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
134
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

270
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
270
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

174
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
174
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
107
Actor-Observer Effect01:23

Actor-Observer Effect

9
The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
9
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

178
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
178

You might also read

Related Articles

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

Sort by
Same author

Histamine in Diabetic Cardiovascular Complications.

Reviews in cardiovascular medicine·2026
Same author

eIF3e-mediated translational checkpoint maintains immune tolerance and prevents lymphoid malignancy.

The Journal of experimental medicine·2026
Same author

A Dual-Ion Source GC-HRMS Nontargeted Screening Strategy for Comprehensive Profiling of G-Series Nerve Agents and Related Chemicals.

Analytical chemistry·2026
Same author

Elucidation of Fragmentation Pathways and GC-HRMS Nontargeted Screening of V-Series Nerve Agent-Related Compounds: Alkylthiophosphonates.

Analytical chemistry·2026
Same author

Screening and SPR-guided maturation of anti-ricin cyclononapeptide for high-sensitivity fluorescent LFA integrated with ultracompact handheld reader.

Talanta·2026
Same author

Efficacy and safety of remimazolam tosylate for sedation in ICU patients: A multicenter, randomized, phase 2 study.

Journal of intensive medicine·2026

Related Experiment Video

Updated: Sep 26, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.8K

Research on Distributed Multi-Sensor Cooperative Scheduling Model Based on Partially Observable Markov Decision

Zhen Zhang1, Jianfeng Wu1, Yan Zhao1

  • 1Air Defense and Missile Defense College, Air Force Engineering University, Xi'an 710051, China.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary

This study introduces a novel multi-sensor cooperative scheduling model using partially observable Markov decision processes for enhanced target detection. The proposed model improves tracking accuracy and offers superior performance for distributed defense applications.

Keywords:
distributed defenseintelligent optimization algorithmmulti-sensor schedulingpartially observable Markov decision process

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

672
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K

Related Experiment Videos

Last Updated: Sep 26, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.8K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

672
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K

Area of Science:

  • Distributed Systems
  • Sensor Networks
  • Artificial Intelligence

Background:

  • Effective target detection in distributed defense relies on efficient multi-sensor planning and scheduling.
  • Existing methods face challenges in achieving continuous, accurate, and rapid detection.

Purpose of the Study:

  • To propose a multi-sensor cooperative scheduling model using partially observable Markov decision process (POMDP).
  • To enhance target tracking accuracy and optimize scheduling scheme performance.

Main Methods:

  • Developed a multi-sensor cooperative scheduling model based on POMDP and posterior Cramer-Rao lower bound.
  • Improved particle filter algorithm using beetle swarm optimization (BSO) for enhanced tracking.
  • Utilized an improved elephant herding optimization (EHO) algorithm for solving the scheduling scheme.

Main Results:

  • The proposed model effectively addresses distributed multi-sensor cooperative scheduling problems.
  • Achieved higher solution performance compared to existing algorithms.
  • Demonstrated compliance with real-time detection requirements.

Conclusions:

  • The POMDP-based model offers a robust solution for cooperative multi-sensor scheduling.
  • The integration of BSO and improved EHO significantly enhances tracking and scheduling efficiency.
  • The model is suitable for real-time distributed defense applications requiring precise target detection.