Machine Vision-Based Dried Danggit Sorter

Barrios, Dennis M.; Lumauag, Ramil G.; Villaruz, Jolitte A.

doi:10.1109/ccoms.2019.8821634

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Knowing that FA has a universal application makes it a fascinating subject to pursue. In fact, this metaheuristic approach can be investigated further to provide solutions to real-world problems, such as IoT-based applications [9], time-series forecasting [10], [11], and machine vision-based tasks [12].…”

Section: Introductionmentioning

confidence: 99%

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Villaruz¹,

Gerardo²,

Gamao³

et al. 2023

IJACSA

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For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, the standard firefly algorithm (FA) posted some limitations in its exploration process that can eventually lead to premature convergence, affecting its performance and adding uncertainty to the optimization results. To address these constraints, this study introduces an additional novel search mechanism for the standard FA inspired by the behavior of the scout bee in the artificial bee colony (ABC) algorithm, termed the "Scouting FA". Specifically, fireflies stuck in the local optima will take directed extra random walks to escape toward the region of the optimum solution, thus improving convergence accuracy. Empirical findings on the five standard benchmark functions have validated the effects of this modification and revealed that Scouting FA is superior to its original version.

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Section: Introductionmentioning

confidence: 99%

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Villaruz¹,

Gerardo²,

Gamao³

et al. 2023

IJACSA

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Trend Forecasting of Computer Vision Application in Aquaponic Cropping Systems Industry

Aquino

Concepcion

Dadios

et al. 2020

2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environ

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A Real-Time Machine Vision System for Grading Quartz Mineral

Akkoyun

2022

Journal of Testing and Evaluation

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Classification is an indispensable process in industrial mass production applications when competitive marketing and increasing the product value by reducing time and saving costs are in concern. In this regard, the machine vision system (MVS) is a prominent technology, especially for automated industrial production flow lines. Recent studies consisted of increasing the accuracy of such systems using advanced technology and complex solutions. Nevertheless, for automated industrial production flow lines, considering only the accuracy rate of an MVS by ignoring the cost and processing speed is not a sufficient parameter to evaluate the success rate concerning the marketing capability. In this study, a relatively low-cost and automated MVS production line for grading white stones in real-time is demonstrated. A conveyor line and a rotary mechanism are integrated into the system for performing a stone grading task. A conditioned cabinet is used for inspecting the flow line continuously. Forty different stone samples in four groups are evaluated in the experimental stage to observe the flow speed and processing accuracy. Different flow speeds of the conveyor line are investigated. The results are demonstrated that the low-cost MVS is successfully operated for grading white stones at relatively high speed with a 92 % accuracy.

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Machine Vision-Based Dried Danggit Sorter

Cited by 3 publications

References 8 publications

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Trend Forecasting of Computer Vision Application in Aquaponic Cropping Systems Industry

A Real-Time Machine Vision System for Grading Quartz Mineral

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