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Journal articles on the topic 'Hybridní algoritmus'

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1

Kohel, Petr, and Rastislav Toman. "DEVELOPMENT OF A CONTROL ALGORITHM FOR A PARALLEL HYBRID POWERTRAIN." MECCA Journal of Middle European Construction and Design of Cars 17, no. 1 (July 20, 2020): 14. http://dx.doi.org/10.14311/mecdc.2020.01.03.

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The current legislation calls for fast electrification of vehicle powertrains, since it is necessary to fulfil the CO2 requirements for the vehicle fleets. The hybrid electric vehicles (HEV) with parallel powertrain topologies – together with pure battery electric vehicles (BEV) – are the most common ways of electrification. However, the HEV powertrain – opposed to the BEV or conventional powertrain – poses an interesting challenge associated with the control system design to achieve the ideal power split between an internal combustion engine (ICE) and electrical machines (EM) during the whole vehicle operation.The presented paper sums up the specific functions and requirements on a control system, together with the description of general control strategy options for a HEV powertrain. The proposed control strategy then combines heuristic rules with a suboptimal numerical control method, calculating the optimal power split ratio based on the efficiencies of ICE and EMs. This control strategy is built into a modular algorithm in Matlab/Simulink for two different parallel HEV powertrain topologies: P2 and P0P4. It is subsequently coupled with a vehicle models created in GT-Suite environment and tested on a WLTC homologation driving cycles. The following simulation tests show the fuel consumption reduction potential for chosen HEV topologies working in hybrid modes, in comparison to a base operation with conventional mode only. Yet, the heuristic rules can be further optimized to obtain even better overall results.Současná legislativa tlačí výrobce vozidel k okamžité elektrifikaci pohonu, protože je to v tuto chvíli jediná možnost, jak dostát požadavkům na flotilové emise CO2. Nejběžnější formou elektrifikace pohonu jsou v dnešní době vozidla s paralelním hybridním pohonem anebo bateriové elektromobily. Nicméně hybridní pohon, na rozdíl právě od konvenčního nebo čistě elektrického pohonu, představuje zajímavé výzvy spojené s návrhem řídicího algoritmu, který musí v každém okamžiku zajišťovat optimální rozdělení výkonu mezi spalovací motor a elektromotor.Tento článek v úvodu krátce shrnuje specifické funkce a požadavky na takový řídicí algoritmus, společně s obecným přehledem možných řídicích strategií hybridních vozidel. Následně je navržena řídicí strategie kombinující heuristická pravidla se suboptimální numerickou metodou, která vypočítává parametr optimálního dělení výkonu na základě účinností spalovacího motoru a elektromotoru. Na základě navrhnuté strategie je v programu Matlab/Simulink vytvořen modulární řídicí algoritmus pro dvě paralelní hybridní topologie: P2 a P0P4, který je následně propojen s modely vozidel vytvořenými v simulačním prostředí GT-Suite a testován v homologačním cyklu WLTC. Nakonec je prezentováno několik testů řídicího algoritmu, které demonstrují úsporu paliva vybraných topologií hybridního vozidla pracujících v hybridních režimech, ve srovnání s provozem pouze v konvenčním režimu pohonu. Avšak heuristická pravidla mohou být dále optimalizována, s cílem dosáhnout ještě příznivějších celkových výsledků.
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2

Habbal, Abderrahmane, Lionel Fourment, and Tien Tho Do. "Algorithmes hybrides pour l’optimisation globale." European Journal of Computational Mechanics 17, no. 3 (January 2008): 303–22. http://dx.doi.org/10.3166/remn.17.303-322.

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3

Kopfer, Herbert, Giselher Pankratz, and Elmar Erkens. "Entwicklung eines hybriden Genetischen Algorithmus zur Tourenplanung." Operations-Research-Spektrum 16, no. 1 (March 1994): 21–31. http://dx.doi.org/10.1007/bf01719700.

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4

Fleurent, Charles, and Jacques A. Ferland. "Algorithmes génétiques hybrides pour l'optimisation combinatoire." RAIRO - Operations Research 30, no. 4 (1996): 373–98. http://dx.doi.org/10.1051/ro/1996300403731.

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5

Fieg, G., X. Luo, and B. Oest. "Hybrider genetischer Algorithmus für die Synthese von großskaligen Wärmeübertragernetzwerken." Chemie Ingenieur Technik 81, no. 8 (August 2009): 1119. http://dx.doi.org/10.1002/cite.200950318.

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6

Robinson, A. Jebamalai, S. C. Punitha S. C. Punitha, and Dr P. Ranjit Jeba Thangaiah. "Hybrid Attribute Selection Process for Decision Tree Based Classification Algorithms." Indian Journal of Applied Research 1, no. 9 (October 1, 2011): 34–36. http://dx.doi.org/10.15373/2249555x/jun2012/14.

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7

Sawant, Parantapa, Willi Nieratschker, Marc Nadler, and Christian Braasch. "Energetische und ökonomische Analyse von realen dezentralen hybriden Energiesystemen." BWK ENERGIE. 73, no. 7-8 (2021): 61–65. http://dx.doi.org/10.37544/1618-193x-2021-7-8-61.

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Im Rahmen des EU-Forschungsprojektes ACA-Modes (Advanced Control Algorithms for Management of Decentralised Energy Systems) werden reale Labore der Projektpartner primärenergetisch, ökonomisch und die Emissionen betreffend bewertet. Vier Projektpartner liefern Datensätze aus Messreihen typischer Bereitstellungsszenarien. Die verschiedenen Systeme bestehen unter anderem aus einer KWK-Anlage mit Erdgas-Verbrennungsmotor, einer KWKK-Anlage mit Adsorptionskältemaschine, einer Photovoltaik-Anlage mit Batteriespeicher und Wärmepumpe und einer Solarthermieanlage mit Adsorptionskältemaschine.
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8

Deleau, Hervé, Jin-Kao Hao, and Frédéric Saubion. "Algorithmes hybrides génériques pour la résolution de problèmes de satisfaction de contraintes." RAIRO - Operations Research 39, no. 2 (April 2005): 87–103. http://dx.doi.org/10.1051/ro:2005006.

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9

Widarma, Adi. "COMBINATION AES, RC4 AND ELGAMAL ALGORITHM IN HYBRID SCHEME FOR DATA SECURITY." Computer Engineering, Science and System Journal 1, no. 1 (January 31, 2016): 1–8. http://dx.doi.org/10.24114/cess.v1i1.4040.

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10

Haque, Md Enamul, and Talal M. Alkharobi. "Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms." International Journal of Machine Learning and Computing 5, no. 1 (February 2015): 17–23. http://dx.doi.org/10.7763/ijmlc.2015.v5.476.

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11

Ahmed Mitras, Ban, and Borhan F. Jumaa. "Estimating the Parameter of RNA by using Hybrid Bat with Modified CG Algorithms." Journal of Zankoy Sulaimani - Part A 22, no. 2 (December 20, 2020): 225–36. http://dx.doi.org/10.17656/jzs.10823.

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12

Habibpour, Ramin, and Kamal Khalilpour. "A new hybrid k-means and K-nearest-neighbor algorithms for text document clustering." International Journal of Academic Research 6, no. 3 (May 30, 2014): 79–84. http://dx.doi.org/10.7813/2075-4124.2014/6-3/a.12.

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13

Hopmann, C., S. Stender, N. Magura, M. Emonts, K. Fischer, and D. Schneider. "Methoden zur produktfunktionsgeregelten Fertigung von Composite-Bauteilen/Methods for product function controlled manufacturing of composite components – Hybrid component manufacturing with chained forward control loop." wt Werkstattstechnik online 109, no. 10 (2019): 785–92. http://dx.doi.org/10.37544/1436-4980-2019-10-89.

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Im Projekt „iComposite4.0“ wurde ein Produktionssystem zur Fertigung hybrider FVK-Bauteile entwickelt. Durch den Multimaterialansatz werden kostengünstige Materialien lastspezifisch eingesetzt. Zudem wird der mehrstufige Prozess genutzt, um während der Produktion durch eine Ausgleichsfunktion eine Anpassung der mechanischen Bauteileigenschaften umzusetzen. Kernaspekte hierbei liegen in der Integration von Messtechnik, Modellierungen und Entscheidungsalgorithmen in das Produktionssystem.   In the iComposite4.0 project, a production system for the manufacture of hybrid FRP components was developed. The multi-material approach allows cost-effective materials to be used for load specific designs. Moreover, the multi-stage process is used to establish an control-algorithm to compensate fluctuations of the mechanical properties of the final part during production. Core aspects are the integration of measurement technology, modelling and decision algorithms into the production system.
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14

Gharehchopogh, Farhad Soleimanian, and Zahra Asheghi Dizaji. "A new chaos agent based approach in prediction of the road accidents with hybrid of PSO optimization and chaos optimization algorithms: a case study." International Journal of Academic Research 6, no. 2 (March 30, 2014): 108–15. http://dx.doi.org/10.7813/2075-4124.2014/6-2/a.18.

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15

NONAMI, Ryota, Mitsuru KITAMURA, and Akihiro TAKEZAWA. "Structural optimization of the large structure with stiffeners by hybrid genetic algorithms." Transactions of the JSME (in Japanese) 81, no. 832 (2015): 15–00437. http://dx.doi.org/10.1299/transjsme.15-00437.

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16

Habbal, Abderrahmane, Lionel Fourment, and Tien Tho Do. "Algorithmes hybrides pour l’optimisation globale." European Journal of Computational Mechanics, June 20, 2008, 303–22. http://dx.doi.org/10.13052/remn.17.303-322.

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We introduce two evolutionnary hybrid optimizers, based on surrogate models which use a limited prescribed number of exact evaluations of the criterion and its gradient. The first algorithm uses a discontinuous ansatz with a clustering technique. The second one uses a Liszka-Orkisz interpolation scheme, and keeps memory of the exactly evaluated individuals of previous generations. These two methods are applied to a 3D forging shape optimization problem. The considered objective combines the total energy cost and a defect criterion. We present numerical results which illustrate the efficiency of the developped algorithms.
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17

Lazli, Lilia, and Mohamed Tayeb Laskri. "A New Data Fusion Method for Hybrid MMC/RNA Learning : Application to Automatic Speech Recognition." Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées Volume 3, Special Issue... (September 2, 2005). http://dx.doi.org/10.46298/arima.1842.

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International audience It is well known that traditional Hidden Markov Models (HMM) systems lead to a considerable improvement when more training data or more parameters are used. However, using more data with hybrid Hidden Markov Models and Artificial Neural Networks (HMM/ANN) models results in increased training times without improvements in performance. We developed in this work a new method based on automatically separating data into several sets and training several neural networks of Multi-Layer Perceptrons (MLP) type on each set. During the recognition phase, models are combined using several criteria (based on data fusion techniques) to provide the recognized word. We showed in this paper that this method significantly improved the recognition accuracy. This method was applied in an Arabic speech recognition system. This last is based on the one hand, on a fuzzy clustering (application of the fuzzy c-means algorithm) and of another share, on a segmentation at base of the genetic algorithms. De nombreuses expériences ont déjà montré qu'une forte amélioration du taux de reconnaissance des systèmes MMC (Modèles de Markov Cachés) traditionnels est observée lorsque plus de données d'apprentissage sont utilisées. En revanche, l'augmentation du nombre de données d'apprentissage pour les modèles hybrides MMC/RNA (Modèles de Markov cachés/Réseaux de Neurones Artificiels) s'accompagne d'une forte augmentation du temps nécessaire à l'apprentissage des modèles, mais pas ou peu des performances du système. Pour pallier cette limitation, nous rapportons dans ce papier les résultats obtenus avec une nouvelle méthode d'apprentissage basée sur la fusion de données. Cette méthode a été appliquée dans un système de reconnaissance de la parole arabe. Ce dernier est basé d'une part, sur une segmentation floue (application de l'algorithme c-moyennes floues) et d'une autre part, sur une segmentation à base des algorithmes génétiques.
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