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Tytuł:
Assessment of the engine boost modernized according to the rules of downsizing
Autorzy:
Sroka, Z. J.
Paropkari, M.
Powiązania:
https://bibliotekanauki.pl/articles/243541.pdf
Data publikacji:
2015
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
combustion engine
downsizing
boosting
Opis:
Downsizing is one of the development trends of internal combustion engine due to its direct impact on fuel economy and indirectly in reducing the emission of carbon dioxide into the atmosphere. Changing the displacement associated with the same engine performance needs support by additional systems, which primarily include the boost. This paper describes downsizing idea, a review of recharging methods and thermodynamic analysis of the combustion process for the chosen engine before and after downsizing taking into account the different variants of boost. The core objective of this study is to downsize a naturally aspirated 1.6L BMW PSA engine by 25% of its initial swept volume and then boosting downsized engine with higher-pressure ratio using the turbocharger set. The study focuses on the analysis of four turbochargers from Garrett turbos. The study winds up with the analysis of engine performance based on the values of compression ratio, air-fuel ratio, polytropic exponents of compression and decompression with keeping the same chemical composition of the fuel. At the end, study was resulted with turbocharger Garrett GT1548 as a the best solution form considered ones, because of: wide range of pressure rate, reasonably sufficient for the engine of this size, enough room (60%) for extracting better performance, lower compression ratio value, which counts the rise of brake mean effective pressure, although to a very little extent and leaner mixture at 1,20 value of the air/fuel ratio with maximum power and reduction of fuel consumption, what was satisfied for downsizing techniques.
Źródło:
Journal of KONES; 2015, 22, 3; 235-239
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Gradient Boosting in Regression
Gradientowa odmiana metody boosting w analizie r e g r e s ji
Autorzy:
Gatnar, Eugeniusz
Powiązania:
https://bibliotekanauki.pl/articles/904716.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
tree-based models
regression
boosting
Opis:
Szeroko stosowane w praktyce metody nieparametryczne wykorzystujące tzw. drzewa regresyjne mają jedną istotną wadę. Otóż wykazują one niestabilność, która oznacza, że niewielka zmiana wartości cech obiektów w zbiorze uczącym może prowadzić do powstania zupełnie innego modelu. Oczywiście wpływa to negatywnie na ich trafność prognostyczną. Tę wadę można jednak wyeliminować, dokonując agregacji kilku indywidualnych modeli w jeden. Znane są trzy metody agregacji modeli i wszystkie opierają się na losowaniu ze zwracaniem obiektów ze zbioru uczącego do kolejnych prób uczących: agregacja bootstrapowa (boosting), losowanie adaptacyjne (bagging) oraz metoda hybrydowa, łącząca elementy obu poprzednich. W analizie regresji szczególnie warto zastosować gradientową, sekwencyjną, odmianę metody boosting. W istocie polega ona wykorzystaniu drzew regrcsyjnych w kolejnych krokach do modelowania reszt dla modelu uzyskanego w poprzednim kroku.
The successful tree-based methodology has one serious disadvantage: lack of stability. That is, regression tree model depends on the training set and even small change in a predictor value could lead to a quite different model. In order to solve this problem single trees are combined into one model. There are three aggregation methods used in classification: bootstrap aggregation (bagging), adaptive resample and combine (boosting) and adaptive bagging (hybrid bagging-boosting procedure). In the field of regression a variant of boosting, i.e. gradient boosting, can be used. Friedman (1999) proved that boosting is equivalent to a stepwise function approximation in which in each step a regression tree models residuals from last step model.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2005, 194
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Boosting Regression Models
Agregacja modeli regresyjnych metodą boosting
Autorzy:
Rozmus, Dorota
Powiązania:
https://bibliotekanauki.pl/articles/906274.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
regression
aggregated model (ensebles)
boosting
Opis:
Boosting jest jedną z najlepszych metod agregacji modeli dyskryminacyjnych (Bauer, Kohavi, 1999). Liczne badania empiryczne potwierdzają możliwość znacznej poprawy jakości modeli klasyfikacyjnych, niewiele jednakże wiadomo na temat efektywności tej metody w przypadku modeli regresyjnych. Freund i Schapire (1995), stosując swój algorytm AdaBoost.R, podjęli próbę wykorzystania metody boosting do tego typu zagadnień. Głównym celem artykułu jest prezentacja nowej implementacji metody boosting w regresji, która opracowana została przez Ridgeway’a (2005). W przeprowadzonych eksperymentach zbadany został wpływ wartości podstawowych parametrów tego algorytmu, takich jak np. współczynnik uczenia, czy też liczba iteracji, na jakość modelu zagregowanego.
In a wide variety of classification problems, boosting technique have proven to be very effective method for improving prediction accuracy (Bauer, Kohavi, 1999). While more evidence compiles about the utility of these technique in classification problems, little is known about their effectiveness in regression. Freund and Schapire (1995) gave a suggestion as to how boosting might improve regression models using their algorithm AdaBoost.R. The main aim of this article is to present an application of the new boosting method for regression problems which was introduced by Ridgeway (2005). We will discuss the influence of the main parameters of this algorithm, such as eg. learning rate or number of iterations on the model performance.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2009, 228
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers
Autorzy:
Biju, V. G.
Prashanth, CM
Powiązania:
https://bibliotekanauki.pl/articles/108646.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi
Tematy:
bagging
boosting
SVM
KNN
decision tree
Opis:
This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.
Źródło:
Journal of Applied Computer Science Methods; 2017, 9 No. 1; 23-47
1689-9636
Pojawia się w:
Journal of Applied Computer Science Methods
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bagging and boosting techniques in prediction of particulate matters
Autorzy:
Triana, D.
Osowski, S.
Powiązania:
https://bibliotekanauki.pl/articles/202449.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
ensemble of predictors
bagging
boosting
PM pollution
Opis:
The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1207-1215
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming
Autorzy:
Ampadu, Vincent-Michael Kwesi
Haq, Muhammad Tahmidul
Ksaibati, Khaled
Powiązania:
https://bibliotekanauki.pl/articles/2176018.pdf
Data publikacji:
2022
Wydawca:
Fundacja Centrum Badań Socjologicznych
Tematy:
crash severity
performance
extreme gradient boosting tree
adaptive boosting tree
random forest
gradient boost decision tree
adaptive synthetic algorithm
Opis:
This study involved the investigation of various machine learning methods, including four classification tree-based ML models, namely the Adaptive Boosting tree, Random Forest, Gradient Boost Decision Tree, Extreme Gradient Boosting tree, and three non-tree-based ML models, namely Support Vector Machines, Multi-layer Perceptron and k-Nearest Neighbors for predicting the level of severity of large truck crashes on Wyoming road networks. The accuracy of these seven methods was then compared. The Final ROC AUC score for the optimized random forest model is 95.296 %. The next highest performing model was the k-NN with 92.780 %, M.L.P. with 87.817 %, XGBoost with 86.542 %, Gradboost with 74.824 %, SVM with 72.648 % and AdaBoost with 67.232 %. Based on the analysis, the top 10 predictors of severity were obtained from the feature importance plot. These may be classified into whether safety equipment was used, whether airbags were deployed, the gender of the driver and whether alcohol was involved.
Źródło:
Journal of Sustainable Development of Transport and Logistics; 2022, 7, 2; 6--24
2520-2979
Pojawia się w:
Journal of Sustainable Development of Transport and Logistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Gas exchange in valved two-stroke SI engine
Autorzy:
Buczek, K.
Mitianiec, W.
Powiązania:
https://bibliotekanauki.pl/articles/245914.pdf
Data publikacji:
2010
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
transport
engine development
two-stroke engine
boosting
Opis:
The paper describes the work of high speed charged spark ignition overhead poppet valve two-stroke engine, which enables to achieve higher total efficiency and exhaust gas emission comparable to four-stroke engines. The work of such engines is possible by proper choice ofvalve timings, geometrical parameters of inlet, outlet ducts and charge pressure. The engine has to be equipped with direct fuel injection system enabling lower emission of pollutants. The work is based on theoretical considerations performed in GT-Power in previous authors' research and carried out in CFD code (KIVA 3 V) for different engine configurations. The initial results included in the paper show influence of inlet port geometry and charge pressure on engine scavenging process. Additionally, optimum fuel spray injector position was considered in order to obtain proper fuel vaporization and avoid significant wall-wetting. The simulation results show that the nitrogen oxides arę considerably reduced in comparison to four-stroke engines because ofhigher internal exhaust gas recirculation. The innovation of this proposal is applying of poppet intake and exhaust valves with turbocharging in the two-stroke engine and obtaining a significant downsizing effect. The conclusion shows the possibilities of proper gas exchange process in this type of two-stroke engine and thus, the feasibility of its application as a power unii for transportation means with higher total efficiency than traditional engines with possible change of engine work in two modes: two- and four-stroke cycles.
Źródło:
Journal of KONES; 2010, 17, 1; 73-80
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
APPLICATION OF MIXED MODELS AND FAMILIES OF CLASSIFIERS TO ESTIMATION OF FINANCIAL RISK PARAMETERS
Autorzy:
Grzybowska, Urszula
Karwański, Marek
Powiązania:
https://bibliotekanauki.pl/articles/452746.pdf
Data publikacji:
2015
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
LGD
mixed models
random forests
gradient boosting
Opis:
The essential role in credit risk modeling is Loss Given Default (LGD) estimation. LGD is treated as a random variable with bimodal distribution. For LGD estimation advanced statistical models such as beta regression can be applied. Unfortunately, the parametric methods require amendments of the “inflation” type that lead to mixed modeling approach. Contrary to classical statistical methods based on probability distribution, the families of classifiers such as gradient boosting or random forests operate with information and allow for more flexible model adjustment. The problem encountered is comparison of obtained results. The aim of the paper is to present and compare results of LGD modeling using statistical methods and data mining approach. Calculations were done on real life data sourced from one of Polish large banks.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2015, 16, 1; 108-115
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Are students at Krakow universities turning to energy-boosting dietary supplements?
Autorzy:
Nessler, K.
Drwiła, D.
Kwaśniak, J.
Kopeć, S.
Nessler, M.
Krztoń-Królewiecka, A.
Windak, A.
Powiązania:
https://bibliotekanauki.pl/articles/2085538.pdf
Data publikacji:
2020
Wydawca:
Instytut Medycyny Wsi
Tematy:
energy-boosting supplements
university students
caffeine overdose
Opis:
Introduction. Recent studies have revealed an increase in the consumption of dietary supplements including frequency of use of caffeine, which is addictive and potentially harmful in higher doses. Energy drinks include high doses of caffeine and are particularly targeted at young people. Objective.The aim of the study was to investigate the frequency of use of caffeine-containing energy products, associated factors and understanding the associated side- effects in university students. Materials and method. A cross-sectional questionnaire-based survey was conducted among students of the 5 largest Universities in Krakow. Statistical significance was set at the 0.05 level. Results. Around 35% of respondents reported the use of different supplements including high doses of caffeine. Frequency of caffeine-containing products consumption was significantly higher in female students compering to males. Also, those respondents who originated from big cities were more likely to use caffeine-containing products. The study revealed that these substances were also more popular among those participants who study economics. Most students use these substances in order to reduce feeling tired and the duration of sleep, others mainly to increase concentration prior to examinations. Almost one fourth of the group who used these substances admitted to having experienced some side-effects in the past. They suffered mainly from insomnia, but also from excessive stimulation and muscle trembling. Almost half of the substances users did not know of any potential side-effects. Conclusions. Attempts should be made to increase public awareness of the side-effects of these substances, particularly among the student population. These campaigns should be targeted especially at female students who come from bigger cities. This study is a step towards drawing attention to this issue.
Źródło:
Annals of Agricultural and Environmental Medicine; 2020, 27, 2; 295-300
1232-1966
Pojawia się w:
Annals of Agricultural and Environmental Medicine
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
BOOSTING UNDER QUANTILE REGRESSION – CAN WE USE IT FOR MARKET RISK EVALUATION?
Autorzy:
Bień-Barkowska, Katarzyna
Powiązania:
https://bibliotekanauki.pl/articles/453152.pdf
Data publikacji:
2014
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
Boosting
quantile regression
GARCH models
value-at-risk
Opis:
We consider boosting, i.e. one of popular statistical machine-learning meta-algorithms, as a possible tool for combining individual volatility estimates under a quantile regression (QR) framework. Short empirical exercise is carried out for the S&P500 daily return series in the period of 2004-2009. Our initial findings show that this novel approach is very promising and the in-sample goodness-of-fit of the QR model is very good. However much further research should be conducted as far as the out-of-sample quality of conditional quantile predictions is concerned.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2014, 15, 1; 7-17
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predictive Business Process Monitoring with Tree-based Classification Algorithms
Autorzy:
Owczarek, Tomasz
Janke, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/503954.pdf
Data publikacji:
2018
Wydawca:
Międzynarodowa Wyższa Szkoła Logistyki i Transportu
Tematy:
business process
prediction
classification
random forest
gradient boosting
Opis:
Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show that.
Źródło:
Logistics and Transport; 2018, 40, 4; 73-82
1734-2015
Pojawia się w:
Logistics and Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolving ensembles of linear classifiers by means of clonal selection algorithm
Autorzy:
Bereta, M.
Burczyński, T.
Powiązania:
https://bibliotekanauki.pl/articles/969829.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
artificial immune systems
clonal selection
linear classifiers
bagging
boosting
Opis:
Artificial immune systems (AIS) have become popular among researchers and have been applied to a variety of tasks. Developing supervised learning algorithms based on metaphors from the immune system is still an area in which there is much to explore. In this paper a novel supervised immune algorithm based on clonal selection framework is proposed. It evolves a population of linear classifiers used to construct a set of classification rules. Aggregating strategies, such as bagging and boosting, are shown to work well with the proposed algorithm as the base classifier.
Źródło:
Control and Cybernetics; 2010, 39, 2; 325-342
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effect of the fuel injection pressure on the combustion process in a PFI boosted spark-ignition engine
Autorzy:
Merola, S. S.
Sementa, P.
Tornatore, C.
Vaglieco, B. M.
Powiązania:
https://bibliotekanauki.pl/articles/244891.pdf
Data publikacji:
2008
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
PFI SI engine
boosting
fuel injection
fuel deposition
optical diagnostics
Opis:
In this paper, low-cost solutions were proposed to reduce the fuel consumption in a boosted port fuel injection spark ignition (PFI SI) engine, taking into account the engine performances and the pollutants emission. To this purpose, the optical characterization of the fuel injection and of the combustion process was carried out in a PFI SI engine. The experiments were performed on a partially transparent single-cylinder SI engine, equipped with a four-valve head and an external boost device. The intake manifold was optically accessible through three holes that allowed the introduction of an endoscope and of optical fibres. The standard injection condition planned by the engine manufacturer was investigated; it consisted in the fuel injection at 3.5 bar when the intake valves were closed. Moreover, the fuel injection with open intake valves was tested; 3.5 and 6.5 bar fuel pressures were studied for open and closed valves conditions. Optical techniques based on 2D-digital imaging were used to follow the fuel injection spray in the intake manifold and the flame propagation in the combustion chamber. The results of in-cylinder optical investigations were correlated with the engine performances and with the exhaust emissions.
Źródło:
Journal of KONES; 2008, 15, 4; 341-349
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multiple Additive Regression Trees (MART) and their Application
Addytywna metoda budowy drzew regresyjnych (MART) i jej zastosowanie
Autorzy:
Trzęsiok, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/906893.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
multivariate regression
adaptive method
regression trees
gradient boosting
MART
Opis:
Multiple additive regression trees MART is a methodology for trying to solve prediction problems in regression and classification. It’s one of the boosting methods. It was introduced by J. H. Friedman (1999a). Besides accuracy, its primary goal is robustness. It lends to be resistant against outliers, missing values, and the inclusion of potentially large numbers of irrelevant predictor variables that have little or no effect on the response. In this paper the MART algorithm and their applications will be discussed.
Addytywna metoda budowy drzew regresyjnych (MART), została zaproponowana przez J. H. Friedmana w 1999 r. (1999a, b). Jest to jedna z metod agregacyjnych, mająca zastosowanie w regresji i dyskryminacji opierająca się na modelach w postaci drzew. Jej zaletami, poza dokładnością predykcji, jest odporność na wartości oddalone i braki danych. Bardzo dobrze radzi sobie również z dużą liczbą zmiennych objaśniających, wśród których wiele może nie mieć istotnego wpływu na zmienną zależną. W artykule przedstawiona została ogólna idea metod agregacyjnych. Zaprezentowano i omówiono kolejne kroki algorytmu MART, a następnie, dla ilustracji, podany został przykład zastosowania procedury MART dla zbioru danych „Boston”.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2007, 206
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effect of the fuel injection splitting on the combustion process in a PFI boosted spark-ignition engine
Autorzy:
Merola, S. S.
Sementa, P.
Tornatore, C.
Vaglieco, B. M.
Powiązania:
https://bibliotekanauki.pl/articles/244873.pdf
Data publikacji:
2008
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
PFI SI engine
boosting
double injection strategies
fuel deposits
optical diagnostics
Opis:
Future stringent legislation on emissions in combination with the market request of an increase in engine efficiency and optimization poses a great challenge to the engine and components manufacturers. The technologies developed in the last years for Spark Ignition (SI) engines such as turbocharging and variable valve actuation are not able to totally satisfy the future normative. More progress still has to be made in terms of in-cylinder combustion process and efficiency. The aim of this paper is the optimisation of a boosted SI engine in terms of performances, fuel consumption and pollutants emissions with low costs. The experimental activity was carried out on a port fuel injection SI optical engine, equipped with a commercial four-valve head. Innovative injection strategies were tested: in particular, single and double injections were performed when the intake valves were open. Optical techniques based on 2D-digital imaging were used to follow the fuel injection in the intake manifold and simultaneously the flame propagation in the combustion chamber. Conventional measurements of engine parameters and exhaust emissions completed the experimental investigations. The tests demonstrated that the double injection strategies were characterized by higher combustion process efficiency than single injection on. The injection splitting resulted a suitable solution for the reduction in pollutants concentration in the combustion chamber and at the exhaust with a good compromise between performance and fuel consumption.
Źródło:
Journal of KONES; 2008, 15, 4; 331-339
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
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