A Multiagent Architecture for 3d Rendering Optimization

González-Morcillo, Carlos; Weiß, Gerhard; Vallejo, David; Jiménez-Linares, Luis; Castro-Schez, J. J.

doi:10.1080/08839511003715212

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…The scalability of cloud resources according to the real time demand has made them popular for rendering animation files [5][6][7][8][9][10]. Cloud render farm services are PaaS (Platform-as-a-Service) type of cloud services that provide the entire platform for the animators to render the animation files using the cloud resources [11][12][13][14][15][16]. In order to render the animation files, the animator has to just schedule the frames that are to be rendered onto the render nodes of the chosen cloud render farm services.…”

Section: Introductionmentioning

confidence: 99%

Ranking cloud render farm services for a multi criteria recommender system

Annette

Banu

2018

Sādhanā

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Recommender systems that recommend ideal services or items to the online users are a very useful tool for both the users and the businesses. Usually for recommending services, multiple attributes of the services are evaluated and these types of recommender systems that evaluate multiple attributes are called multi criteria recommender systems. In these types of multi criteria recommender systems the ranking of services plays a major role. This work is focused on ranking the cloud render farm services which are of the PaaS (Platform-as-a-Service) type of cloud services that provide the entire platform for the animators to render the files using the cloud resources. This work identifies the Quality of Service (QoS) attributes that are important for the animators for selecting a cloud render farm service. The QoS values of four different real time cloud render farm services were collected by conducting real time experiments using the files of the ''Big Buck Bunny'', an open-source animated film project and were ranked using three Multi-Criteria Decision Making (MCDM) methods, namely the AHP (Analytical Hierarchical Process), TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) and SAW (Simple Additive Weighting). The analysis of the ranks obtained using the three different MCDM methods provide many useful insights and conclusions.

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Section: Introductionmentioning

confidence: 99%

Ranking cloud render farm services for a multi criteria recommender system

Annette

Banu

2018

Sādhanā

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“…The rendering phase is often considered as a bottleneck in photorealistic projects in which one image may need hours of rendering in a modern workstation. The rendering system used in this work can be easily used through distributed rendering approaches [11].…”

Section: Sign Language Representationmentioning

confidence: 99%

Methodology for developing an advanced communications system for the Deaf in a new domain

López-Ludeña¹,

González-Morcillo²,

López³

et al. 2014

Knowledge-Based Systems

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A methodology for developing an advanced communications system for the Deaf in a new domain is presented in this paper. This methodology is a user-centred design approach consisting of four main steps: requirement analysis, parallel corpus generation, technology adaptation to the new domain, and finally, system evaluation. During the requirement analysis, both the user and technical requirements are evaluated and defined. For generating the parallel corpus, it is necessary to collect Spanish sentences in the new domain and translate them into LSE (Lengua de Signos Española: Spanish Sign Language). LSE is represented by glosses and using video recordings. This corpus is used for training the two main modules of the advanced communications system to the new domain: the spoken Spanish into the LSE translation module and the Spanish generation from the LSE module. The main aspects to be generated are the vocabularies for both languages (Spanish words and signs), and the knowledge for translating in both directions. Finally, the field evaluation is carried out with deaf people using the advanced communications system to interact with hearing people in several scenarios. In this evaluation, the paper proposes several objective and subjective measurements for evaluating the performance. In this paper, the new considered domain is about dialogues in a hotel reception. Using this methodology, the system was developed in several months, obtaining very good performance: good translation rates (10% Sign Error Rate) with small processing times, allowing face-to-face dialogues.

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Section: Sign Language Representationmentioning

confidence: 99%