The application of spatial domain in optimum initialization for clustering image data using particle swarm optimization

Dadjoo, Mehran; Nasrabadi, Sayyed Bagher Fatemi

doi:10.1016/j.eswa.2020.114224

Expert Systems with Applications

2021

DOI: 10.1016/j.eswa.2020.114224

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The application of spatial domain in optimum initialization for clustering image data using particle swarm optimization

Mehran Dadjoo

Sayyed Bagher Fatemi Nasrabadi

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Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Consequently, depending solely on co-occurrence frequency proved inadequate for discerning sample relationships. In a prior investigation [24], a stability function was introduced as an assessment metric for discriminating among samples. This function employs the average confidence of the frequency between one sample and another as the stability measure.…”

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Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Li,

et al. 2024

Animals

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Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

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“…The algorithm was relatively straightforward, easy to comprehend and implement. It exhibits excellent global search capabilities, particularly in addressing problems within high-dimensional and complex spaces, garnering attention from scholars in the field [24].…”

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confidence: 99%

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Li,

et al. 2024

Animals

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A meta-heuristics based framework of cluster label optimization in MR images using stable random walk

Srivastava

Singh

2023

Multimed Tools Appl

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An efficient, robust optimization model for the unit commitment considering renewable uncertainty and pumped-storage hydropower

Nasab

Zand

Padmanaban

et al. 2022

Computers and Electrical Engineering

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The application of spatial domain in optimum initialization for clustering image data using particle swarm optimization

Cited by 4 publications

References 26 publications

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

A meta-heuristics based framework of cluster label optimization in MR images using stable random walk

An efficient, robust optimization model for the unit commitment considering renewable uncertainty and pumped-storage hydropower

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