Temat: decision tree model - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Small Wind Turbine Output Model for Spatially Constrained Remote Island Micro-Grids
Autorzy:: Žigman, D.
Meštrović, K.
Tomiša, T.
Powiązania:: https://bibliotekanauki.pl/articles/2172468.pdf
Data publikacji:: 2022
Wydawca:: Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:: wind turbine
small wind turbine
decision tree model
artificial neural network model
random forest model
micro-grids
spatially constrained remote Island micro-grids
remote Island micro-grid
Opis:: Modelling operation of the power supply system for remote island communities is essential for its operation, as well as a survival of a modern society settled in challenging conditions. Micro-grid emerges as a proper solution for a sustainable development of a spatially constrained remote island community, while at the same time reflecting the power requirements of similar maritime subjects, such as large vessels and fleets. Here we present research results in predictive modelling the output of a small wind turbine, as a component of a remote island micro-grid. Based on a month-long experimental data and the machine learning-based predictive model development approach, three candidate models of a small wind turbine output were developed, and assessed on their performance based on an independent set of experimental data. The Random Forest Model out performed competitors (Decision Tree Model and Artificial Neural Network Model), emerging as a candidate methodology for the all-year predictive model development, as a later component of the over-all remote island micro-grid model.
Źródło:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2022, 16, 1; 143--146
2083-6473
2083-6481
Pojawia się w:: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A decision support tool for a walkable integrated neighbourhood design using a multicriteria decision-making method.
Autorzy:: Keyvanfar, A.
Shafaghat, A.
Lamit, H.
Powiązania:: https://bibliotekanauki.pl/articles/196315.pdf
Data publikacji:: 2018
Wydawca:: Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:: walkability
walkable city
assessment model
pedestrian behavior
decision tree
decision support tool
dostępność piesza
piesze miasto
model oceny
zachowanie pieszych
drzewo decyzyjne
narzędzie wspomagania decyzji
Opis:: Growing concern about transportation emissions and energy security has persuaded urban professionals and practitioners to pursue non-motorized urban development. They need an assessment tool to measure the association between the built environment and pedestrians’ walking behaviour more accurately. This research has developed a new assessment tool called the Walkable Integrated Neighbourhood Design (WIND) support tool, which interprets the built environment’s qualitative variables and pedestrians’ perceptual qualities in relation to quantifiable variables. The WIND tool captures and forecasts pedestrians’ mind mapping, as well as sequential decision-making during walking, and then analyses the path walkability through a decision-tree-making (DTM) algorithm on both the segment scale and the neighbourhood scale. The WIND tool measures walkability by variables clustered into five features, 11 criteria and 92 subcriteria. The mind-mapping analysis is presented in the form of a ‘Walkability_DTM-Mind-mapping sheet’ for each destination and the overall neighbourhood. The WIND tool is applicable to any neighbourhood cases, although it was applied to the Taman Universiti neighbourhood in Malaysia. The tool’s outputs aid urban designers to imply adaptability between the neighbourhood environment and residents’ perceptions, preferences and needs.
Źródło:: Zeszyty Naukowe. Transport / Politechnika Śląska; 2018, 100; 45-68
0209-3324
2450-1549
Pojawia się w:: Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Cloud Computing Application for Romanian SMEs
Zastosowanie technologii chmury obliczeniowej wśród małych i średnich przedsiębiorstw w Rumunii
Autorzy:: Pistol, Luminiţa
Bucea-Manea Ţoniș, Rocsana
Bucea-Manea Ţoniș, Radu
Powiązania:: https://bibliotekanauki.pl/articles/1342424.pdf
Data publikacji:: 2017
Wydawca:: Sieć Badawcza Łukasiewicz - Instytut Lotnictwa
Tematy:: AngularJS
cloud computing
decision tree
JSON
marketing
minib
model of innovation
SMEs
supply chain
chmura obliczeniowa
drzewo decyzyjne
łańcuch dostaw
model innowacyjny
MŚP
Opis:: The article studies the current economical state of Romanian SMEs and the utility of cloud computing technologies in the process of sustainable open innovation. The study is based on a supply chain adapted for SMEs, on a model of innovation within a network business environment and on a decision tree dedicated for SMEs when starting a new project. Taking into account the statements of the article, a new framework of cloud computing economics can be developed.
Artykuł bada obecną sytuację gospodarczą małych i średnich przedsiębiorstw (MŚP) w Rumunii oraz przydatność technologii chmury obliczeniowej w procesie zrównoważonych otwartych innowacji. Badanie opiera się na koncepcji łańcucha dostaw przystosowanego do potrzeb MŚP, na modelu innowacji w środowisku sieci biznesu oraz na modelu drzewa decyzyjnego opracowanego dla realiów MŚP przy uruchamianiu nowego projektu. Tezy przedstawione w artykule mogą posłużyć do nakreślenia nowych ram ekonomiki chmury obliczeniowej.
Źródło:: Marketing Instytucji Naukowych i Badawczych; 2017, 3(25); 117-134
2353-8414
Pojawia się w:: Marketing Instytucji Naukowych i Badawczych
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Imitation learning of car driving skills with decision trees and random forests
Autorzy:: Cichosz, P.
Pawełczak, Ł.
Powiązania:: https://bibliotekanauki.pl/articles/329901.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: imitation learning
behavioral cloning
model ensemble
random forest
control
autonomous driving
car racing
decision tree
drzewo decyzyjne
lasy losowe
sterowanie
wyścigi samochodowe
Opis:: Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 579-597
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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