Jove
Visualize
Contact Us

Related Concept Videos

Conservation of Declining Populations02:07

Conservation of Declining Populations

9.7K
Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.
9.7K
Upsampling01:22

Upsampling

286
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
286
Bandpass Sampling01:17

Bandpass Sampling

241
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
241
Cluster Sampling Method01:20

Cluster Sampling Method

12.3K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.3K

You might also read

Related Articles

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

Sort by
Same author

[Effects of Pa-pex 11 gene on penicillin production in Penicillium aurantiogriseum].

Wei sheng wu xue bao = Acta microbiologica Sinica·2010
Same author

Inhibition of lung fluid clearance and epithelial Na+ channels by chlorine, hypochlorous acid, and chloramines.

The Journal of biological chemistry·2010
Same author

Discovery and optimization of novel 3-piperazinylcoumarin antagonist of chemokine-like factor 1 with oral antiasthma activity in mice.

Journal of medicinal chemistry·2010
Same author

Evidence for dimeric BACE-mediated APP processing.

Biochemical and biophysical research communications·2010
Same author

Involvement of mineralocorticoid receptor in high glucose-induced big mitogen-activated protein kinase 1 activation and mesangial cell proliferation.

Journal of hypertension·2010
Same author

Nanosized anatase TiO2 single crystals for enhanced photocatalytic activity.

Chemical communications (Cambridge, England)·2010
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 Experiment Video

Updated: Aug 19, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K

An Adaptive Sinusoidal-Disturbance-Strategy Sparrow Search Algorithm and Its Application.

Feng Zheng1, Gang Liu1

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

This study introduces an improved sparrow search algorithm (ASDSSA) to overcome limitations in speed and accuracy. The enhanced algorithm demonstrates superior optimization performance and efficiency, successfully applied to long short-term memory network (LSTM) parameter tuning for metro passenger flow prediction.

Keywords:
LSTM neural networkadaptive Cauchy mutationadaptive sinusoidal disturbancepassenger flow predictionpopulation quality

More Related Videos

A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.5K
Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.7K

Related Experiment Videos

Last Updated: Aug 19, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K
A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.5K
Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.7K

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Machine Learning

Background:

  • The standard sparrow search algorithm (SSA) suffers from slow convergence, suboptimal accuracy, and a tendency to get trapped in local optima.
  • Addressing these limitations is crucial for enhancing the performance of metaheuristic optimization techniques.

Purpose of the Study:

  • To propose and validate an Adaptive Sinusoidal-Disturbance-Strategy Sparrow Search Algorithm (ASDSSA).
  • To improve the global search capability, convergence speed, and ability to escape local optima compared to the original SSA.
  • To demonstrate the practical applicability of ASDSSA in optimizing machine learning models for real-world problems.

Main Methods:

  • Fusing cubic chaos mapping and perturbation compensation factors to enhance initial population quality.
  • Introducing a sinusoidal disturbance strategy to the discoverer's position update equation for improved information exchange and global search.
  • Incorporating an adaptive Cauchy mutation strategy to enhance the algorithm's ability to escape local optima.
  • Evaluating ASDSSA performance on benchmark functions (e.g., CEC2017) and conducting statistical tests (Wilcoxon rank-sum) and complexity analysis.

Main Results:

  • ASDSSA exhibited significantly better optimization performance and convergence efficiency compared to the standard SSA on benchmark test functions.
  • The Wilcoxon rank-sum test confirmed the statistical superiority of ASDSSA.
  • Time complexity analysis indicated efficient computational performance.
  • Application to optimize Long Short-Term Memory (LSTM) network parameters for metro passenger flow prediction demonstrated the algorithm's effectiveness and feasibility.

Conclusions:

  • The proposed ASDSSA effectively addresses the limitations of the original SSA, offering improved optimization accuracy and convergence.
  • ASDSSA provides a robust and efficient method for parameter optimization in machine learning models, as evidenced by its successful application in passenger flow prediction.
  • The enhanced algorithm holds promise for various complex optimization tasks in computational intelligence and machine learning.