Temat: approximation network - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: An artifical neural network usage in measurement of exhaust gas emission from marine engines: case study
Autorzy:: Kowalski, J.
Rudzki, K.
Powiązania:: https://bibliotekanauki.pl/articles/2073611.pdf
Data publikacji:: 2016
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: marine engine
emission
neural network
measurement
power approximation
silnik okrętowy
emisja
sieć neuronowa
pomiar
Opis:: The paper presents the case study of use the artificial neural network to predict the main propulsion marine engine load. Mentioned load of the engine is important parameter to assess the emission level of toxic compounds into the atmosphere according to ISO standard and MARPOL convention. The engine load depends on the ship speed, rotational speed of the engine, propeller blades settings, the direction and the speed of wind, the condition of sea and the direction and the speed of sea currents and construction parameters of the ship. The realization of the aim of the work requires the direct measurement of presented parameters and measurement of exhaust gas composition. The experiment was carried out onboard STS “Pogoria”. Obtained results are enough to use the ANN to predict engine load to measure the emission level of toxic compounds.
Źródło:: Journal of Polish CIMEEAC; 2016, 11, 1; 87--93
1231-3998
Pojawia się w:: Journal of Polish CIMEEAC
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A Deep Q-Learning Network for ship stowage planning problem
Autorzy:: Shen, Y.
Zhao, N.
Xia, M.
Du, X.
Powiązania:: https://bibliotekanauki.pl/articles/260614.pdf
Data publikacji:: 2017
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:: Deep Q-Leaning Network (DQN)
container terminal
ship stowage plan
markov decision process
value function approximation
generalization
Opis:: Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significantly affected by case size. In this study, a Deep Q-Learning Network (DQN) is proposed to solve ship stowage planning problem. With DQN, massive calculation and training is done in pre-training stage, while in application stage stowage plan can be made in seconds. To formulate network input, decision factors are analyzed to compose feature vector of stowage plan. States subject to constraints, available action and reward function of Q-value are designed. With these information and design, an 8-layer DQN is formulated with an evaluation function of mean square error is composed to learn stowage planning. At the end of this study, several production cases are solved with proposed DQN to validate the effectiveness and generalization ability. Result shows a good availability of DQN to solve ship stowage planning problem.
Źródło:: Polish Maritime Research; 2017, S 3; 102-109
1233-2585
Pojawia się w:: Polish Maritime Research
Dostawca treści:: Biblioteka Nauki

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