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Готові списки джерел за темами / Deployment models

Добірка наукової літератури з теми "Deployment models"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 13 лютого 2022

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Статті в журналах з теми "Deployment models":

1

B. Patel, Prof Hiral, and Prof Nirali Kansara. "Cloud Computing Deployment Models: A Comparative Study." International Journal of Innovative Research in Computer Science & Technology 9, no. 2 (March 2021): 45–50. http://dx.doi.org/10.21276/ijircst.2021.9.2.8.

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2

Howick, R. S., and M. Pidd. "Sales force deployment models." European Journal of Operational Research 48, no. 3 (October 1990): 295–310. http://dx.doi.org/10.1016/0377-2217(90)90413-6.

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3

Sriningsih, Riry, Muhammad Subhan, and Minora Longgom Nasution. "Analysis of torch deployment models." Journal of Physics: Conference Series 1317 (October 2019): 012013. http://dx.doi.org/10.1088/1742-6596/1317/1/012013.

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4

Rimbaud, Loup, Frédéric Fabre, Julien Papaïx, Benoît Moury, Christian Lannou, Luke G. Barrett, and Peter H. Thrall. "Models of Plant Resistance Deployment." Annual Review of Phytopathology 59, no. 1 (August 25, 2021): 125–52. http://dx.doi.org/10.1146/annurev-phyto-020620-122134.

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Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

5

BUSHEHRIAN, OMID. "SOFTWARE PERFORMANCE ENGINEERING BY SIMULATED-BASED OBJECT DEPLOYMENT." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (March 2013): 211–21. http://dx.doi.org/10.1142/s0218194013500058.

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The object deployment of a distributed software has a great impact on its performance. In this paper an analytical model for performance evaluation of different object deployments, is presented. The key advantage of the proposed model over the traditional Queuing Network models is the usefulness in the deployment optimization when the search space is huge and automatic instantiation of Queuing performance models corresponding to an object deployment is costly. Since our model produces an optimal deployment corresponding to each input load separately, the runtime behavior of the software corresponding to each input load should be profiled using simulation first. In this paper a translation scheme for generating the simulate-able Labeled Transition Systems (LTS) from scenarios is also presented. Moreover, two deployment algorithms (a GA-based and an INLP-based) are implemented and the results are compared.

6

Perakis, Anastassions N., and Nikiforos Papadakis. "Fleet deployment optimization models. Part 1." Maritime Policy & Management 14, no. 2 (January 1987): 127–44. http://dx.doi.org/10.1080/03088838700000015.

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7

Vinayak, Kalluri, and Rambabu Kodali. "Benchmarking the quality function deployment models." Benchmarking: An International Journal 20, no. 6 (October 21, 2013): 825–54. http://dx.doi.org/10.1108/bij-07-2011-0052.

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8

Eid, Mustafa I. M., Ibrahim M. Al-Jabri, and M. Sadiq Sohail. "Selection of Cloud Delivery and Deployment Models." International Journal of Decision Support System Technology 10, no. 4 (October 2018): 17–32. http://dx.doi.org/10.4018/ijdsst.2018100102.

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Research interests on cloud computing adoption and its effectiveness in terms of cost and time has been increasing. However, one of the challenging decisions facing management in adopting cloud services is taking on the right combinations of cloud service delivery and deployment models. A comprehensive review of literature revealed a lack of research addressing this selection decision problem. To fill this research gap, this article proposes an expert system approach for managers to decide on the right combination of service delivery and deployment model selection. The article first proposes a rule-based expert system prototype, which provides advice based on a set of factors that represent the organizational conditions and requirements pertaining to cloud computing adoption. Next, the authors evaluate the system prototype. Lastly, the article concludes with a discussion of the results, its practical implications, limitations, and further research directions.

9

Kim, Kwang-Jae, Herbert Moskowitz, Anoop Dhingra, and Gerald Evans. "Fuzzy multicriteria models for quality function deployment." European Journal of Operational Research 121, no. 3 (March 2000): 504–18. http://dx.doi.org/10.1016/s0377-2217(99)00048-x.

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10

BALASUBRAMANIAN, KRISHNAKUMAR, ANIRUDDHA GOKHALE, YUEHUA LIN, JING ZHANG, and JEFF GRAY. "WEAVING DEPLOYMENT ASPECTS INTO DOMAIN-SPECIFIC MODELS." International Journal of Software Engineering and Knowledge Engineering 16, no. 03 (June 2006): 403–24. http://dx.doi.org/10.1142/s021819400600280x.

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Domain-specific models increase the level of abstraction used to develop large-scale component-based systems. Model-driven development (MDD) approaches (e.g., Model-Integrated Computing and Model-Driven Architecture) emphasize the use of models at all stages of system development. Decomposing problems using MDD approaches may result in a separation of the artifacts in a way that impedes comprehension. For example, a single concern (such as deployment of a distributed system) may crosscut different orthogonal activities (such as component specification, interaction, packaging and planning). To keep track of all entities associated with a component, and to ensure that the constraints for the system as a whole are not violated, a purely model-driven approach imposes extra effort, thereby negating some of the benefits of MDD. This paper provides three contributions to the study of applying aspect-oriented techniques to address the crosscutting challenges of model-driven component-based distributed systems development. First, we identify the sources of crosscutting concerns that typically arise in model-driven development of component-based systems. Second, we describe how aspect-oriented model weaving helps modularize these crosscutting concerns using model transformations. Third, we describe how we have applied model weaving using a tool called the Constraint-Specification Aspect Weaver (C-SAW) in the context of the Platform-Independent Component Modeling Language (PICML), which is a domain-specific modeling language for developing component-based systems. A case study of a joint-emergency response system is presented to express the challenges in modeling a typical distributed system. Our experience shows that model weaving is an effective and scalable technique for dealing with crosscutting aspects of component-based systems development.

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Дисертації з теми "Deployment models":

1

Puntenney, Michael C. "Optimization models for military aircraft deployment." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27190.

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Amilitary aircraft deployment problem from the United States Transportation Command is modeled as a generalized transportation problem with side constraints and solved using a general purpose linear programming package. The model involves the assignment of military units and material to aircraft and th assignment of aircraft to missions in order to appraise the utility and to determine the assets required for preliminary military operation plans. A transformation of this model which aggregates variables relating to the early or late delivery of requirements is also described. A specialized algorithm which separates an instance of the model into subgroups of independent subproblems is also explored. Lastly, an integer rounding model is described which converts continious solutions to integer in order to facilitate implementation of the former models whith an existing post-solution processor. Excellent quality solutions are provided for problems involving nine routes, 80 movement requirements distributed across two cargo classes involving 200,000 short tons of freight, and 250 aircraft using four different aircraft types for each of 12 time periods. The problem, which has the potential of having over 10,000 variables, is reduced significantly using variable reduction and the aggregation transformation. The reduced problem requires approximately 1,000 variables and 300 constraints and solutions are obtainable in under 14 minutes using the General Algebraic Modeling System on an 80286-based personal computer

2

Duval, Thierry. "Models for design, implementation and deployment of 3D Collaborative Virtual Environments." Habilitation à diriger des recherches, Université Rennes 1, 2012. http://tel.archives-ouvertes.fr/tel-00764830.

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This work aims at providing some cues in order to address the essential requirements about the design of 3D Collaborative Virtual Environments (CVE). We have identified six essential topics that must be addressed when designing a CVE. For each of them, we present a state of the art about the solutions that can address this topic, then we show our own contributions: how we improve existing solutions and what are our new propositions. 1 - Choosing a model for the distribution of a CVE We need a distribution model to distribute as efficiently as possible the content of a CVE among all the nodes involved in its execution, including the machines of the distant users. Our proposition is to allow CVE designers to mix in a same CVE the three main distribution models usually encountered: centralized on a server, totally replicated on each site, or distributed according to a hybrid distribution model. 2 - Choosing a model for the synchronization of these nodes To maintain consistency between all the nodes involved in the execution of a CVE, we must choose between a strong synchronization or a relaxed one, or an in-between solution. Our proposition is to manage some temporary relaxation of the synchronization due to network breakdowns, with several synchronization groups of users, making them aware of these network breakdowns, and to allow some shared objects to migrate from one site to another. 3 - Adapting the Virtual Environment to various hardware systems VR applications must be adapted to the software and to the hardware input and output devices that are available at run-time, in order to be able to deploy a CVE onto di fferent kinds of hardware and software. Our solution is the PAC-C3D software architectural model which is able to deal with the three main distribution modes encountered in CVE. 4 - Designing interaction and collaboration in the VE Expressing the interactive and collaborative capabilities of the content of a CVE goes one step beyond geometric modeling, by adding interactive and collaborative features to virtual objects. We propose a unified model of dialog between interactive objects and interaction tools, with an extension to Collada in order to describe interactive and collaborative properties of these interactive objects and interaction tools. 5 - Choosing the best metaphors for collaborative interactions Most of the time single-user interaction tools and metaphors are not adapted to off er effi cient collaboration between users of a CVE. We adapt some of these tools and metaphors to collaborative interactions, and we propose new really collaborative metaphors to enhance real multi-user collaborative interactions, with dedicated collaborative feedback. 6 - Embedding the users' physical workspaces within the CVE Taking into account users' physical workspaces makes it possible to adapt a CVE to the hardware input and output devices of the users, and to make them aware of their physical limitations and of those of the other users, for better interaction and collaboration. We propose the Immersive Interactive Virtual Cabin (IIVC) concept to embed such 3D representations in CVE.

3

Barreto, Gómez Tirso Leonardo. "Technological learning in energy optimisation models and deployment of emerging technologies /." Zürich, 2001. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14151.

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4

Avital, Ittai. "Chance-constrained missile-procurement and deployment models for Naval Surface Warfare /." Diss., Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FAvital.pdf.

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5

John, Meenu Mary. "Design Methods and Processes for ML/DL models." Licentiate thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-45026.

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Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, companies are increasingly using Artificial Intelligence (AI) in systems, along with electronics and software. Nevertheless, the end-to-end process of developing, deploying and evolving ML and DL models in companies brings some challenges related to the design and scaling of these models. For example, access to and availability of data is often challenging, and activities such as collecting, cleaning, preprocessing, and storing data, as well as training, deploying and monitoring the model(s) are complex. Regardless of the level of expertise and/or access to data scientists, companies in all embedded systems domain struggle to build high-performing models due to a lack of established and systematic design methods and processes. Objective: The overall objective is to establish systematic and structured design methods and processes for the end-to-end process of developing, deploying and successfully evolving ML/DL models. Method: To achieve the objective, we conducted our research in close collaboration with companies in the embedded systems domain using different empirical research methods such as case study, action research and literature review. Results and Conclusions: This research provides six main results: First, it identifies the activities that companies undertake in parallel to develop, deploy and evolve ML/DL models, and the challenges associated with them. Second, it presents a conceptual framework for the continuous delivery of ML/DL models to accelerate AI-driven business in companies. Third, it presents a framework based on current literature to accelerate the end-to-end deployment process and advance knowledge on how to integrate, deploy and operationalize ML/DL models. Fourth, it develops a generic framework with five architectural alternatives for deploying ML/DL models at the edge. These architectural alternatives range from a centralized architecture that prioritizes (re)training in the cloud to a decentralized architecture that prioritizes (re)training at the edge. Fifth, it identifies key factors to help companies decide which architecture to choose for deploying ML/DL models. Finally, it explores how MLOps, as a practice that brings together data scientist teams and operations, ensures the continuous delivery and evolution of models.

6

Theres, Michael J. "Models for comparing air-only and sea/air transportation of wartime deployment cargo." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA358943.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, December 1998.
"December 1998." Thesis advisor(s): R. Kevin Wood. Includes bibliographical references (p. 55-56). Also available online.

7

Li, Pin. "A Systematic Methodology for Developing Robust Prognostic Models Suitable for Large-Scale Deployment." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593268220645085.

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8

Yang, Zhe. "Coexistence, Deployment and Business Models of Heterogeneous Wireless Systems Incorporating High Altitude Platforms." Doctoral thesis, Blekinge Tekniska Högskola, Avdelningen för elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00551.

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The increased demand for broadband communications has led to the rapid development of the conventional terrestrial and satellite wireless communications systems. One of the main challenges to next generation wireless systems is to deliver high-capacity and cost-efficient solutions to cope with an increasing usage of broadband services and applications. In the recent years, an emerging competitive system has attracted the attention for providing wireless broadband communications and other services based on quasi-stationary aerial platforms operating in the stratosphere known by high altitude platforms (HAPs), and located 17-22 km above the earth surface. This solution has been described by the International Telecommunication Union (ITU) as "a new and long anticipated technology that can revolutionize the telecommunication industry''. The HAP systems provide important advantages such as low cost, high elevation angles, low propagation delay, easy and incremental deployment, flexibility in operation, broad coverage, broadcast and broadband capability, ability to move around in emergency situations, etc. Therefore, they have been proposed by ITU for the provision of fixed, mobile services and applications, e.g. the third generation (3G) services licensed by ITU and backbone link for terrestrial networks in remote areas. This thesis explores and investigates the wireless communication and techno-economic performance of terrestrial systems and HAPs. An overview of research and development on aerial platforms worldwide is given. Coexistence performance and techniques of heterogeneous systems to provide broadband wireless communications based on Worldwide Interoperability Microwave Access (WiMAX) are investigated. A heterogeneous scenario is developed to examine the coexistence performance of heterogeneous systems. The capacity and deployment aspects of HAPs are analyzed, and further compared with terrestrial Universal Mobile Telecommunications Systems (UMTS) through techno-economic studies including a proposed partnership based business model for HAPs. Performance of wireless sensor network applications via HAPs is also investigated, and shows the high potential of HAPs for large-area and long-endurance surveillance and emergency applications. The thesis shows that communications from the aerial platforms provide the best features of both terrestrial and satellite systems. HAPs can effectively coexist in a heterogeneous radio environment, and are competitive solutions in urban and suburban scenarios in terms of capacity, coverage and business perspective. This makes HAP a viable competitor and complement to conventional terrestrial infrastructures and satellite systems.

9

Khajeh-Hosseini, Ali. "Supporting system deployment decisions in public clouds." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/3412.

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Decisions to deploy IT systems on public Infrastructure-as-a-Service clouds can be complicated as evaluating the benefits, risks and costs of using such clouds is not straightforward. The aim of this project was to investigate the challenges that enterprises face when making system deployment decisions in public clouds, and to develop vendor-neutral tools to inform decision makers during this process. Three tools were developed to support decision makers: 1. Cloud Suitability Checklist: a simple list of questions to provide a rapid assessment of the suitability of public IaaS clouds for a specific IT system. 2. Benefits and Risks Assessment tool: a spreadsheet that includes the general benefits and risks of using public clouds; this provides a starting point for risk assessment and helps organisations start discussions about cloud adoption. 3. Elastic Cost Modelling: a tool that enables decision makers to model their system deployment options in public clouds and forecast their costs. These three tools collectively enable decision makers to investigate the benefits, risks and costs of using public clouds, and effectively support them in making system deployment decisions. Data was collected from five case studies and hundreds of users to evaluate the effectiveness of the tools. This data showed that the cost effectiveness of using public clouds is situation dependent rather than universally less expensive than traditional forms of IT provisioning. Running systems on the cloud using a traditional 'always on' approach can be less cost effective than on-premise servers, and the elastic nature of the cloud has to be considered if costs are to be reduced. Decision makers have to model the variations in resource usage and their systems' deployment options to obtain accurate cost estimates. Performing upfront cost modelling is beneficial as there can be significant cost differences between different cloud providers, and different deployment options within a single cloud. During such modelling exercises, the variations in a system's load (over time) must be taken into account to produce more accurate cost estimates, and the notion of elasticity patterns that is presented in this thesis provides one simple way to do this.

10

Islam, Kazi Mohammed Saiful. "Spatial dynamic queueing models for the daily deployment of airtankers for forest fire control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq35194.pdf.

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Книги з теми "Deployment models":

1

Puntenney, Michael C. Optimization models for military aircraft deployment. Monterey, California: Naval Postgraduate School, 1989.

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2

Bento, Alberto M., and Anil Aggarwal. Cloud computing service and deployment models: Layers and management. Hershey, PA: Business Science Reference, 2013.

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3

Saranga, Haritha. Optimal deployment of parallel teams in new product development. Bangalore: Indian Institute of Management Bangalore, 2008.

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4

Theres, Michael J. Models for comparing air-only and sea/air transportation of wartime deployment cargo. Monterey, Calif: Naval Postgraduate School, 1998.

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5

Karle-Komes, Nicole. Anwenderintegration in die Produktentwicklung: Generierung von Innovationsideen durch die Interaktion von Hersteller und Anwender innovativer industrieller Produkte. Frankfurt am Main: P. Lang, 1997.

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6

Dailey, Daniel J. Smart Trek: A model deployment initiative. [Olympia, Wash.]: Washington State Dept. of Transportation, 2001.

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7

Lloyd, Mark. Tactics of modern warfare: Rapid deployment in the 20th century. London: B. Trodd Pub. House, 1991.

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8

Wisecarver, Michelle M. Deployment consequences: A review of the literature and integration of findings into a model of retention. Arlington, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 2006.

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9

Lloyd, Mark. Tactics of modern warfare: Rapid deployment in the 20th century / Mark Lloyd. New York: Mallard, 1991.

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10

Lloyd, Mark. Tactics of modern warfare: Rapid deployment in the 20th century / Mark Lloyd. New York: Mallard, 1991.

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Частини книг з теми "Deployment models":

1

Towill, D. R., and J. E. Cherrington. "Learning Curve Models." In A Systems Approach to AMT Deployment, 57–75. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3406-0_4.

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2

Singh, Pramod. "Model Deployment and Challenges." In Deploy Machine Learning Models to Production, 55–66. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6546-8_2.

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3

Singh, Pramod. "Machine Learning Deployment Using Docker." In Deploy Machine Learning Models to Production, 91–126. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6546-8_4.

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4

Singh, Pramod. "Machine Learning Deployment Using Kubernetes." In Deploy Machine Learning Models to Production, 127–46. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6546-8_5.

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5

Johnsen, Einar Broch, Rudolf Schlatte, and S. Lizeth Tapia Tarifa. "Deployment Variability in Delta-Oriented Models." In Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change, 304–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45234-9_22.

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6

Maritan, Davide. "Quality Function Deployment (QFD): Definitions, History and Models." In Practical Manual of Quality Function Deployment, 1–32. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08521-0_1.

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7

Singh, Pramod. "Machine Learning Deployment as a Web Service." In Deploy Machine Learning Models to Production, 67–90. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6546-8_3.

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8

Ferry, Nicolas, and Arnor Solberg. "Models@Runtime for Continuous Design and Deployment." In Model-Driven Development and Operation of Multi-Cloud Applications, 81–94. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46031-4_9.

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9

van der Aalst, W. M. P., and B. F. van Dongen. "Discovering Workflow Performance Models from Timed Logs." In Engineering and Deployment of Cooperative Information Systems, 45–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45785-2_4.

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10

Wild, Karoline, Uwe Breitenbücher, Kálmán Képes, Frank Leymann, and Benjamin Weder. "Decentralized Cross-organizational Application Deployment Automation: An Approach for Generating Deployment Choreographies Based on Declarative Deployment Models." In Advanced Information Systems Engineering, 20–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49435-3_2.

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Тези доповідей конференцій з теми "Deployment models":

1

Eder, Johannes, Andreas Bahya, Sebastian Voss, Alexandru Ipatiov, and Maged Khalil. "From Deployment to Platform Exploration." In MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3239372.3239385.

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2

Andreasson, Johan, Naoya Machida, Masashi Tsushima, John Griffin, and Peter Sundström. "Deployment of high-fidelity vehicle models for accurate real-time simulation." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp1612478.

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3

Ferry, Nicolas, and Phu H. Nguyen. "Towards Model-Based Continuous Deployment of Secure IoT Systems." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00093.

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4

Song, Hui, Rustem Dautov, Nicolas Ferry, Arnor Solberg, and Franck Fleurey. "Model-based fleet deployment of edge computing applications." In MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3365438.3410951.

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Saidi, Salah Eddine, Nicolas Pernet, Yves Sorel, and Abir Ben Khaled. "Acceleration of FMU Co-Simulation On Multi-core Architectures." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124106.

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6

Teleman, Ylva, Pieter Dermont, Hak Jun Kim, and Kil Sang Jang. "Rankine Cycles, Modeling and Control." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124113.

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Kim, Eunkyeong, Tatsurou Yashiki, Fumiyuki Suzuki, Yukinori Katagiri, and Takuya Yoshida. "Thermal Deformation Analysis Using Modelica." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124121.

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Hua, Shan, Fabian Reuß, Manuel Lindauer, Jochen Stopper, and Christoph van Treeck. "Validated Modelica Building Package for Energy Performance Simulation for Educational and Teaching Purposes." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124129.

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Dermont, Pieter, Dirk Limperich, Johan Windahl, Katrin Prölss, and Carsten Kübler. "Advances of Zero Flow Simulation of Air Conditioning Systems using Modelica." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp16124139.

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Hirano, Yutaka. "Research of Model Matching Control of Torque Vectoring Differential Gear System." In Deployment of high-fidelity vehicle models for accurate real-time simulation. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/ecp1612415.

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Звіти організацій з теми "Deployment models":

1

Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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Анотація:

We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.

2

Hsueh, Gary, David Czerwinski, Cristian Poliziani, Terris Becker, Alexandre Hughes, Peter Chen, and Melissa Benn. Using BEAM Software to Simulate the Introduction of On-Demand, Automated, and Electric Shuttles for Last Mile Connectivity in Santa Clara County. Mineta Transportation Institute, January 2021. http://dx.doi.org/10.31979/mti.2021.1822.

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Despite growing interest in low-speed automated shuttles, pilot deployments have only just begun in a few places in the U.S., and there is a lack of studies that estimate the impacts of a widespread deployment of automated shuttles designed to supplement existing transit networks. This project estimated the potential impacts of automated shuttles based on a deployment scenario generated for a sample geographic area: Santa Clara County, California. The project identified sample deployment markets within Santa Clara County using a GIS screening exercise; tested the mode share changes of an automated shuttle deployment scenario using BEAM, an open-source beta software developed at the Lawrence Berkeley National Laboratory to run traffic simulations with MATSim; elaborated the model outputs within the R environment; and then estimated the related impacts. The main findings have been that the BEAM software, despite still being in its beta version, was able to model a scenario with the automated shuttle service: this report illustrates the potential of the software and the lessons learned. Regarding transportation aspects, the model estimated automated shuttle use throughout the county, with a higher rate of use in the downtown San José area. The shuttles would be preferred mainly by people who had been using gasoline-powered ride hail vehicles for A-to-B trips or going to the bus stop, as well as walking trips and a few car trips directed to public transport stops. As a result, the shuttles contributed to a small decrease in emissions of air pollutants, provided a competitive solution for short trips, and increased the overall use of the public transport system. The shuttles also presented a solution for short night trips—mainly between midnight and 2 am—when there are not many options for moving between points A and B. The conclusion is that the automated shuttle service is a good solution in certain contexts and can increase public transit ridership overall.

3

Kirwan, Jr, Lipphardt A. D., and B. L. Jr. Model Assessment and Deployment Strategies for Drifting Instruments. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada612097.

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4

Kirwan, Jr, Lipphardt A. D., and B. L. Jr. Model Assessment and Deployment Strategies for Drifting Instruments. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada573090.

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5

Lawphongpanich, S., and R. E. Rosenthal. A multi-model deployment planning problem. Final report. Office of Scientific and Technical Information (OSTI), September 1989. http://dx.doi.org/10.2172/541931.

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6

Denholm, P., E. Drury, and R. Margolis. Solar Deployment System (SolarDS) Model: Documentation and Sample Results. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/967192.

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7

Cohen, Stuart M., Jonathon Becker, David A. Bielen, Maxwell Brown, Wesley J. Cole, Kelly P. Eurek, Allister Frazier, et al. Regional Energy Deployment System (ReEDS) Model Documentation: Version 2018. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1505935.

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8

Cole, Wesley, Maxwell Brown, Kelly Eurek, Jonathon Becker, Ilya Chernyakhovskiy, Stuart Cohen, Allister Frazier, et al. Regional Energy Deployment System (ReEDS) Model Documentation: Version 2019. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1606151.

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9

Eurek, Kelly, Wesley Cole, David Bielen, Nate Blair, Stuart Cohen, Bethany Frew, Jonathan Ho, et al. Regional Energy Deployment System (ReEDS) Model Documentation: Version 2016. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1332909.

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10

Volkova, Svitana, David Stracuzzi, Jenifer Shafer, Jaideep Ray, and Laura Pullum. Robustness and Validation of Model and Digital Twins Deployment. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1770631.

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