Within batch non-linear profile monitoring applied to shrimp farming: A case study

Quevedo, A. Valeria; Vegas, Susana; Loda, Justin B.; Cedillo, Gutiérrez; Vining, G. Geoffrey

doi:10.1080/08982112.2020.1844894

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Ref. [10,11] propose models to generate timely control charts supporting the identification of growing ponds needing attention by analysing the shrimps' weight profiles. Ref.…”

Section: Iot and Analytics In Shrimp Farmingmentioning

confidence: 99%

An IoT-Based System Implementing Statistical Models for the Post-Larvae Shrimp Acclimatisation Process

Quinde,

Quevedo,

Vasquez

et al. 2024

Intelligent Environments 2024: Combined Proceedings of Workshops and Demos &Amp; Videos Session

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Acclimatisation of post-larvae shrimp is critical to guarantee adequate growth in freshwater shrimp farming. Acclimatisation aims to adapt shrimp from their natural habitat (seawater) to the freshwater that is in the pools where they will grow later. Acclimatisation is a challenging process because of the more frequent required monitoring, the potential harm that out-of-limit indicators could cause, and the rapid correction needed to return the monitoring indicators to their specification ranges. This research proposes an IoT-based system integrated with advanced statistical tools in the form of control charts to support the monitoring of the acclimatisation process. The proposal is to be implemented in a shrimp farming company and addresses the negative effects of the existing manual monitoring (operators taking readings and annotating in notebooks manually), the need to monitor acclimatisation proactively (with control charts) and, eventually, reduce the current mortality rate of the process. The paper reports on the design, future implementation, validation and integration with advanced statistical models, challenges, and eventual benefits of the proposed IoT-based system.

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“…Ref. [10,11] propose models to generate timely control charts supporting the identification of growing ponds needing attention by analysing the shrimps' weight profiles. Ref.…”

Section: Iot and Analytics In Shrimp Farmingmentioning

confidence: 99%

An IoT-Based System Implementing Statistical Models for the Post-Larvae Shrimp Acclimatisation Process

Quinde,

Quevedo,

Vasquez

et al. 2024

Intelligent Environments 2024: Combined Proceedings of Workshops and Demos &Amp; Videos Session

View full text Add to dashboard Cite

show abstract

“…Development of generalized likelihood ratio [ 32 ], Bayes theorem [ 33 ], random effect model [ 34 ], semiparametric methods [ 35 ] and machine learning [ 36 ] in profile monitoring are some instances for the first group. On the other hand, we can find several practical applications of profile monitoring in different industries such as wood composites [ 37 ], chemical gas sensors [ 38 ], shrimp farming [ 39 ] and so forth. As there are a vast number of research papers related to this topic, it is not possible to mention them all, but we refer the interested readers to the existing review papers by Woodall and Montgomery [ 40 ] and Maleki, Amiri [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Yeganeh

Shongwe

2023

PLoS ONE

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The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method.

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Within batch non-linear profile monitoring applied to shrimp farming: A case study

Cited by 2 publications

References 29 publications

An IoT-Based System Implementing Statistical Models for the Post-Larvae Shrimp Acclimatisation Process

An IoT-Based System Implementing Statistical Models for the Post-Larvae Shrimp Acclimatisation Process

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

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