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Control Algorithm for Equal Current Sharing between Parallel-Connected Boost Converters in a DC Microgrid
2020
Journal of Electrical and Computer Engineering
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The control algorithm is based on the percentage of current sharing for each module to the total load current. ...
To address this limitation, a control algorithm that supervises a modified droop method to achieve precise current sharing between parallel modules is proposed in this paper. ...
Acknowledgments e authors would like to thank the Libyan Government for funding this research. ...
doi:10.1155/2020/6876317
fatcat:fm6noipkk5hvtf73avhyse75z4
PARALLEL IMPLEMENTATION OF THE METHOD OF GRADIENT BOOSTING
2018
Advanced Information Systems
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K e ywor d s : gradient boosting; parallel algorithm; decision tree; estimation of parallel algorithm efficiency; e-learning. ...
Using parallel computing systems and parallel programming technologies can produce positive results, but requires the development of new methods for constructing gradient boosting. ...
Step 3 (symbol 7, Fig. 3 ) -provides a choice of technology for parallel construction of the algorithm of gradient boosting. ...
doi:10.20998/2522-9052.2018.3.03
fatcat:h3wkh55yvjck3nfd43z4hcip44
Modified Droop Method Based on Master Current Control for Parallel-Connected DC-DC Boost Converters
2018
Journal of Electrical and Computer Engineering
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The modified droop method uses an algorithm for parallel-connected DC-DC boost converters to adaptively adjust the reference voltage for each converter according to the load regulation characteristics ...
Load current sharing between parallel-connected DC-DC boost converters is very important for system reliability. ...
Acknowledgments The authors would like to thank the Libyan Government for funding this research. ...
doi:10.1155/2018/9819787
fatcat:e3rqt3xeljh3birivtsjumcw3e
Page 1297 of Neural Computation Vol. 6, Issue 6
[page]
1994
Neural Computation
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Boosting and Other Ensemble Methods
@ ORIGINAL BOOSTING O SINGLE MACHINE
w= PARALLEL MACHINE o MODIFIED BOOSTING
TRAINING SET SIZE
1000 10 000 100 000
Figure 3: Test performance of four algorithms using ...
For the original boosting algorithm, there are not enough potential training patterns in the NIST database and it is too computationally expensive to use the modified boosting algorithm. ...
Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques
2021
International Journal of Big Data and Analytics in Healthcare
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This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers ...
The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. ...
These three ensemble methods, namely AdaBoostM1, XG Boost and Gradient Boosting generated for J48, Reduced Error Pruning and Decision Stump algorithms. ...
doi:10.4018/ijbdah.20210101.oa4
fatcat:hjzzuq67zje73oajjp5wzkixbi
Scalable and Parallel Boosting with MapReduce
2012
IEEE Transactions on Knowledge and Data Engineering
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In this paper, we propose two parallel boosting algorithms, ADABOOST.PL and LOGITBOOST.PL, which facilitate simultaneous participation of multiple computing nodes to construct a boosted ensemble classifier ...
However, due to the inherent sequential nature, achieving scalability for boosting is nontrivial and demands the development of new parallelized versions which will allow them to efficiently handle large-scale ...
In this paper, we propose two novel parallel boosting algorithms, ADABOOST.PL (Parallel ADABOOST) and LO-GITBOOST.PL (Parallel LOGITBOOST). ...
doi:10.1109/tkde.2011.208
fatcat:5ciim4bp5jcmdap6rgycqpzbvi
Boosting and Other Ensemble Methods
1994
Neural Computation
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For single and parallel machines, this ratio is 1. For the original boosting algorithm, not all the patterns in the training set are used and the ratio is less than 1. ...
network
Single
0.55
Original boosting
0.87
Modified boosting
0.28
Parallel machine (3 members)
0.51
Parallel machine (5 members)
0.58
3 Conclusions
Figures 3 and 4 allow us to conclude that, for a given ...
doi:10.1162/neco.1994.6.6.1289
fatcat:f7eveqzwtrcdhpp4tzkq4atkoq
Performance Analysis of Different PV Topologies with MPPT
2017
International Journal of Trend in Scientific Research and Development
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Conventionally, MPPT controller and Boost Converter is utilized for each solar panel or channel in this in series and parallel connection architecture, which results in cost increase. ...
This paper presents a single MPPT controller for two solar panels in any series or parallel connection architecture. It is suited when the load is fix voltage or is resistive. ...
Solar One Boost (b) Parallel Two Solar One Boost Series Two Solar Two Boost (d) Parallel Two Solar Two Boost IJTSRD | May-Jun 2017 Available Online @www.ijtsrd.com
Fig. 4 4 Fig. 4 (a) Load Output Power ...
doi:10.31142/ijtsrd2185
fatcat:mynujjptwndufmjvhksdv64m7m
Feed-Forward Control Method for Digital Power Factor Correction in Parallel Connected Buck-Boost Converter (CCM Mode)
2020
2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)
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Feed-forward algorithms are used to tune the model parameters in order to strongly reduce the input current harmonics. ...
As the parallel connection of PFC converters is a promising way to achieve a higher power rating, questions arise on balancing the current and power over these connected converters. ...
FEED-FORWARD ALGORITHM FOR PARALLEL CONNECTED
BUCK-BOOST PFCS For the purpose of Power Factor Correction, the inductance is needed. ...
doi:10.1109/speedam48782.2020.9161901
fatcat:4dkj7aofi5ezpnjjl2iucnkmxm
Paralleled DC Boost Converters with Feedback Control using PSO Optimization Technique for Photovoltaic Module Application
2013
International Journal of Computer Applications
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In this paper a novel method of controller design for boost type dc-dc converter is proposed. ...
The optimal values of feedback PID controller are obtained using Particle Swarm Optimization Algorithm (PSOA). ...
Tuning method comparison of PID controller between transient performance specification (T-PID) and PSO algorithm method for the paralleled closed loop boost converters.
table 2 . 2 Table2. ...
doi:10.5120/14659-2839
fatcat:dgpvtdgdcndj3nymatiiqg4gme
Asynch-SGBDT: Asynchronous Parallel Stochastic Gradient Boosting Decision Tree based on Parameters Server
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1804.04659v3 fatcat:pbaucbs345aflng24wp2pwg6heOther Versions
In this paper, we examine the possibility of using asynchronous parallel methods to train GBDT model and name this algorithm as asynch-SGBDT (asynchronous parallel stochastic gradient boosting decision ...
However, those GBDT algorithms are synchronous parallel algorithms which fail to make full use of Parameter Server. ...
The goal for boosting is to find an additive classifier predictor function, i.e. ...
arXiv:1804.04659v4
fatcat:qddpqdarszhexbufa7gxodjbuu
Parallel Computing to Predict Breast Cancer Recurrence on SEER Dataset using Map-Reduce Approach
2016
International Journal of Computer Applications
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By exploiting their own parallel architecture the algorithm increases noteworthy speedup. ...
In this paper, parallel Map-Reduce algorithm is proposed, that encourages concurrent participation of various computing hubs to develop a classifier on SEER breast cancer data set. ...
In spite of these endeavors, there has not been any huge examination to parallelize the boosting algorithm itself. ...
doi:10.5120/ijca2016911669
fatcat:th7z5cirx5g67bvob7xxfavlxu
Parallel boosted regression trees for web search ranking
2011
Proceedings of the 20th international conference on World wide web - WWW '11
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In this paper, we propose a novel method for parallelizing the training of GBRT. ...
Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned websearch ranking -a domain notorious for very large data sets. ...
ACKNOWLEDGEMENTS We would like to thank Ananth Mohan for sharing his exact implementation of Gradient Boosted Regression Trees and Yahoo Labs for providing resources for this research. ...
doi:10.1145/1963405.1963461
dblp:conf/www/TyreeWAP11
fatcat:32w5v4mekjduni2dmliat4elxm
TEG Cascaded Solar PV System with Enhanced Efficiency by Using the PSO MPPT Boost Converter
2020
International Journal of Research in Engineering, Science and Management
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For this reason, maximum power point tracking (MPPT) algorithms are utilized. In this study, both TEGs and a boost converter with MPPT were modeled together. ...
In addition, a boost converter having a particle swarm optimization (PSO) MPPT algorithm was added to the TEG modeling. ...
The algorithm is a reference for other algorithms and the results of the developed algorithms are compared with those of the algorithm. ...
doi:10.47607/ijresm.2020.384
fatcat:sbbdkj3tv5bsxm6wx3vvtbfpqq
Identification of Default Payments of Credit Card Clients using Boosting Techniques
2020
International journal of recent technology and engineering
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The main idea is by analyzing the customer data and by combining machine-learning algorithm to identify the default credit card user. ...
For the huge customers related dataset we can use various classification techniques used in the field of data mining. ...
Parallel ensemble methods where parallel baseline learners are created (e.g. Random Forest). ...
doi:10.35940/ijrte.f8897.038620
fatcat:fzygfltqzvem5fmyveezb3c7s4
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