Nonlinear control design within the high level modeling framework for an engine cold start scenario

Author(s):  
Andreas Hansen ◽  
J. Karl Hedrick
Author(s):  
Marielba Zacarias ◽  
Rodrigo Magalhaes ◽  
Artur Caetano ◽  
H. Sofia Pinto ◽  
Jose Tribolet

Human beings are, by nature, self-aware beings. This capacity lets us know who we are, how we do things, and what we (and others) are doing at any particular moment. In organizations, self-awareness is an essential prerequisite for effective action, decision-making, and learning processes. However, it must be built and maintained by continuous interactions among their members. This chapter lays out the foundations of a comprehensive high-level modeling framework as a means for enhancing organizational self-awareness. The modeling framework encompasses an architecture and ontology, which puts together human, social, and organizational approaches with modeling frameworks coming from the computer sciences and IS/IT fields. The proposed approach is illustrated with two example applications which use the finer-grained concepts of the framework. An analysis of the implications of this approach and issues to be addressed is provided.


Author(s):  
Leonardo Rezende Juracy ◽  
Matheus Trevisan Moreira ◽  
Alexandre de Amory Morais ◽  
Alexandre F. Hampel ◽  
Fernando Gehm Moraes

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.


2014 ◽  
Vol 599-601 ◽  
pp. 530-533
Author(s):  
Hong Hao Wang ◽  
Hui Quan Wang ◽  
Zhong He Jin

Due to the complex timing sequence of NAND flash, a unified design process is urgently required to guarantee the reliability of storage system of nano-satellite. Unified Modeling Language (UML) is a widely used high level modeling language for object-oriented design. This paper adopts the UML as the design and modelling tool in the low level storage system design to elaborate the UML application in each phase of design in detail. The result shows taking UML as the modelling tool results in a clear and unambiguity design, which promotes the reliability and quality of software. At last, the feasibility of object-oriented implementation in C is presented.


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