Length-Weight Relations and Monthly Occurrence of Juvenile Fish Species from the Donji Molunat Bay, Croatia (South-East Adriatic Sea)

Kurtela, Antonia; Antolović, Nenad; Glavić, Nikša; Kožul, Valter

doi:10.17818/nm/2019/3.7

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Where is the mean of the data results calculated per (10) Each solution is fi rst evaluated using R 2 . Coeffi cient of determination is given in range of [0,1], with higher values being better [56]. If the coeffi cient of determination value is higher than 0.99 the model is stored and evaluated again using MAE where a smaller MAE is considered better.…”

Section: Solution Evaluation / Procjena Rješenjamentioning

confidence: 99%

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

et al. 2020

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Authors present a Multilayer Perceptron (MLP) artifi cial neural network (ANN) method for the purpose of estimating a speed of a frigate using a combined diesel-electric and gas (CODLAG) propulsion system. Dataset used is publicly available, as conditionbased maintenance of naval propulsion plants dataset, out of which GT Compressor decay state coeffi cient and GT Turbine decay state coeffi cient are unused, while 15 features are used as input and ship speed is used as dataset output. Data set consists of 11934 data points out of which 8950 (75%) are used as a training set and 2984 (25%) are used as a testing set. 26880 MLPs, with 8960 diff erent parameter combinations are trained using a grid search algorithm, quality of each solution being estimated with coeffi cient of determination (R2) and mean absolute error (MAE). Results show that a high-quality estimation can be made using an MLP, with best result having an error of just 3.4485x10 -5 knots (absolute error of 0.00014% of the range). This result was achieved with a MLP with three hidden layers containing 100 neurons each, logistic activation function, LBFGS solver, constant learning rate of 0.1 and no L2 regularization. Sažetak Autori predstavljaju metodu višeslojnog perceptrona (MLP) umjetne neuronske mreže (ANN) u svrhu procjene brzine fregate koristeći se kombiniranim dizel-električnim i plinskim pogonskim sustavom (CODLAG). Korišteni skup podataka javno je dostupan, poput skupa podataka o održavanju po stanju brodskih pogonskih postrojenja, od kojih se ne koriste koefi cijent raspadanja GT kompresora i koefi cijent stanja raspada GT Turbine, dok se 15 značajki koriste kao ulazni, a brzina broda kao izlazni podatak. Skup podataka sastoji se od 11934 podatkovne točke, od čega se 8950 (75 %) koristi kao skup za vježbu, a 2984 (25 %) kao testni skup. 26880 MLP-ova s 8960 različitih kombinacija parametara uvježbava se algoritmom pretraživanja energetske mreže, a kvaliteta svakog rješenja procjenjuje se koefi cijentom određivanja (R2) i prosječnom apsolutnom pogreškom (MAE). Rezultati pokazuju da se visokokvalitetna procjena može napraviti uz pomoć MLP-a, pri čemu će najbolji rezultat imati pogrešku od samo 3,4485x10 -5 čvorova (apsolutna pogreška raspona 0,00014 %). Za postizanje rezultata korišten je MLP s tri skrivena sloja koji sadrže po 100 neurona, logistička funkcija aktivacije, LBFGS rješavač, konstantna brzina učenja od 0,1 bez L2 regulacija. KEY WORDS artifi cial intelligence artifi cial neural networks CODLAG propulsion system multilayer perceptron speed estimation KLJUČNE RIJEČI umjetna inteligencija umjetne neuronske mreže CODLAG pogonski sustav višeslojni perceptron procjena brzine

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Section: Solution Evaluation / Procjena Rješenjamentioning

confidence: 99%

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

et al. 2020

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First approximation of the growth and the spawning period of Symphodus ocellatus (Linnaeus, 1758) (Pisces: Labridae) in the southeastern Black Sea

Onay

2023

Cienc. Mar.

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The aim of this study was to provide a first approximation of the growth, reproduction, and length–weight relationships of Symphodus ocellatus in the southeastern Black Sea, Turkey. A total of 320 specimens (273 males, 47 females) were collected between June 2015 and May 2016, and their biological parameters were estimated for each sex and both sexes combined. Total length ranged from 6.90 to 13.70 cm and the parameters (a, b) in the power length–weight relationships were a = 0.065 and b = 2.38 for males and a = 0.102 and b = 2.20 for females. Age was successfully estimated by commonly accepted procedures for otolith analysis. The von Bertalanffy growth parameters from the otolith age estimation were L∞ = 12.66 cm, k = 0.764, and t0 = –1.151 (0–3 years) for males, and L∞ = 14.15 cm, k = 0.764, and t0 = –0.188 (1–3 years) for females. The monthly gonadosomatic index values reflected a spawning period between April and July, with a spawning peak in May–June. The present study provides the first estimate of the growth and reproduction of S. ocellatus in the southeastern Black Sea of Turkey and compares the results with those from other areas.

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Length-Weight Relations and Monthly Occurrence of Juvenile Fish Species from the Donji Molunat Bay, Croatia (South-East Adriatic Sea)

Cited by 2 publications

References 9 publications

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

First approximation of the growth and the spawning period of Symphodus ocellatus (Linnaeus, 1758) (Pisces: Labridae) in the southeastern Black Sea

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