scholarly journals Future Mobility

2013 ◽  
Vol 135 (03) ◽  
pp. S18-S24 ◽  
Author(s):  
Dimitar Filev ◽  
Jianbo Lu ◽  
Davor Hrovat
Keyword(s):  
Cloud Computing ◽  
Control Strategies ◽  
Predictive Diagnosis ◽  
Automotive Control ◽  
Control Approach ◽  
Computing Paradigm ◽  
Vehicle Data ◽  
Control Units ◽  
And Performance ◽  
Line Setting

This article presents an automotive control approach for information-rich future mobility. It integrates in-vehicle networked controls with cloud computing accessible through a wireless network to elevate current on-board controls to a new level for additional benefits and performance. Outsourcing computation-intensive tasks to a cloud-computing server is an extension of the current server-based concierge/infotainment type features. While in-vehicle controls remain essential for safety critical and real-time functionality, the cloud-computing paradigm offers another degree of freedom for control system design. In future vehicle controls, the cloud can be used for very demanding computations that otherwise cannot be accomplished by on-board electronic control units (ECUs), especially for information-intensive tasks. The so-called local-simple-remote-complex vehicle control strategies are likely to unlock the potential of implementing methods and tools that are presently used only in an off-line setting. The cloud can also be used as a storage place to record current and historic vehicle data that can be used for predictive diagnosis and prognostics of the vehicle health.

Rationale for Use of Cloud Computing

2015 ◽  
pp. 2166-2197
Author(s):  
Amir Zeid ◽  
Ahmed Shawish ◽  
Maria Salama
Keyword(s):  
Cloud Computing ◽  
Cost Benefit Analysis ◽  
Service Selection ◽  
Cost Benefit ◽  
Quality Parameters ◽  
Cloud Services ◽  
Computing Paradigm ◽  
Model Combining ◽  
Quality Aspects ◽  
And Performance

Cloud Computing is the most promising computing paradigm that provides flexible resource allocation on demand with the promise of realizing elastic, Internet-accessible, computing on a pay-as-you-go basis. With the growth and expansion of the Cloud services and participation of various services providers, the description of quality parameters and measurement units start to diversify and sometime contradict. Such ambiguity does not only result in the rise of various Quality of Service (QoS) interoperability problems but also in the distraction of the services consumers who find themselves unable to match quality requirements with the providers' offerings. Yet, employing the available QoS models that cover certain quality aspects while neglecting others drive consumers to perform their service selection based only on cost-benefit analysis and performance evaluation, without being able to perform subjective selection based on a comprehensive set of well-defined quality aspects. This chapter presents a novel QoS ontology that combines and defines all of the existing quality aspects in a unified way to efficiently overcome all existing diversities. Using such an ontology, a comprehensive broad QoS model combining all quality-related parameters of both service providers and consumers for different Cloud platforms is presented. The chapter also provides a mathematical model that formulates the Cloud Computing service provider selection optimization problem based on QoS guarantees. The validation of the provided model is addressed in the chapter through extensive simulation studies conducted on benchmark data of Content Delivery Network providers. The studies report the efficient matching of the model with the market-oriented different platform characteristics.

Rationale for Use of Cloud Computing

2014 ◽  
pp. 24-56
Author(s):  
Amir Zeid ◽  
Ahmed Shawish ◽  
Maria Salama
Keyword(s):  
Cloud Computing ◽  
Cost Benefit Analysis ◽  
Service Selection ◽  
Cost Benefit ◽  
Quality Parameters ◽  
Cloud Services ◽  
Computing Paradigm ◽  
Model Combining ◽  
Quality Aspects ◽  
And Performance

Cloud Computing is the most promising computing paradigm that provides flexible resource allocation on demand with the promise of realizing elastic, Internet-accessible, computing on a pay-as-you-go basis. With the growth and expansion of the Cloud services and participation of various services providers, the description of quality parameters and measurement units start to diversify and sometime contradict. Such ambiguity does not only result in the rise of various Quality of Service (QoS) interoperability problems but also in the distraction of the services consumers who find themselves unable to match quality requirements with the providers’ offerings. Yet, employing the available QoS models that cover certain quality aspects while neglecting others drive consumers to perform their service selection based only on cost-benefit analysis and performance evaluation, without being able to perform subjective selection based on a comprehensive set of well-defined quality aspects. This chapter presents a novel QoS ontology that combines and defines all of the existing quality aspects in a unified way to efficiently overcome all existing diversities. Using such an ontology, a comprehensive broad QoS model combining all quality-related parameters of both service providers and consumers for different Cloud platforms is presented. The chapter also provides a mathematical model that formulates the Cloud Computing service provider selection optimization problem based on QoS guarantees. The validation of the provided model is addressed in the chapter through extensive simulation studies conducted on benchmark data of Content Delivery Network providers. The studies report the efficient matching of the model with the market-oriented different platform characteristics.

Effects of control strategy on the activation of unwanted intrusive thoughts

2020 ◽  
Author(s):  
Sicong Liu ◽  
Jonathan Folstein ◽  
Lawrence Gregory Appelbaum ◽  
Gershon Tenenbaum
Keyword(s):  
Control Strategies ◽  
Working Memory Capacity ◽  
Intrusive Thoughts ◽  
Control Process ◽  
Event Related Potentials ◽  
Human Beings ◽  
Opposite Pattern ◽  
Thought Control ◽  
Related Potentials ◽  
And Performance

Although the unwanted intrusive thoughts (UITs) exist widely in human beings and show similar characteristics between clinical and nonclinical forms, its control process remains unclear. Thoughts of choking under pressure, particularly among high-achieving athletes, represent a meaningful UIT type due to their psychological and performance-related impact. Taking a dynamic view of UIT control process, this study tested the effect of thought-control strategies among sub-elite to elite athletes, applied to individualized choking thoughts. Ninety athletes recollected recent athletic choking experiences prior to being randomized into one of three thought control interventions using strategies of either acceptance, passive monitoring (control), or suppression. To control for individual differences, athletes’ working memory capacity was measured and modeled as a covariate at baseline. The activation of choking thoughts during and after the intervention was gauged through multiple measurement approaches including conscious presence in mind, priming, and event-related potentials (P3b and N400 amplitudes). Results indicated that, relative to the control, suppression led to enhanced priming and reduced conscious presence of choking thoughts, whereas acceptance resulted in an opposite pattern of reduced priming and increased conscious presence of choking thoughts. In addition, thought-related stimuli elicited less negative-going N400 amplitudes and more positive-going P3b amplitudes than control stimuli. These findings advance understandings of the control mechanism underpinning UITs, and generate applied implications regarding UIT control in high-risk populations such as those with athletic expertise.

scholarly journals Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

2020 ◽  
Vol 10 (24) ◽  
pp. 9148
Author(s):  
Germán Moltó ◽  
Diana M. Naranjo ◽  
J. Damian Segrelles
Keyword(s):  
Cloud Computing ◽  
Learning Analytics ◽  
Cloud Provider ◽  
Online Students ◽  
Computing Services ◽  
Hands On ◽  
Data Processing Tool ◽  
Amazon Web Services ◽  
Course Length ◽  
And Performance

Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.

Optimal Control of a General Environmental Space

1986 ◽  
Vol 108 (4) ◽  
pp. 330-339 ◽  
Author(s):  
M. A. Townsend ◽  
D. B. Cherchas ◽  
A. Abdelmessih
Keyword(s):  
Optimal Control ◽  
Maximum Principle ◽  
Moisture Content ◽  
Control Strategies ◽  
Switching Control ◽  
The Maximum Principle ◽  
Environmental Space ◽  
And Performance

This study considers the optimal control of dry bulb temperature and moisture content in a single zone, to be accomplished in such a way as to be implementable in any zone of a multi-zone system. Optimality is determined in terms of appropriate cost and performance functions and subject to practical limits using the maximum principle. Several candidate optimal control strategies are investigated. It is shown that a bang-bang switching control which is theoretically periodic is a least cost practical control. In addition, specific attributes of this class of problem are explored.

scholarly journals A Fast Optimization Method for Reliability and Performance of Cloud Services Composition Application

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhao Wu ◽  
Naixue Xiong ◽  
Yannong Huang ◽  
Qiong Gu ◽  
Chunyang Hu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
State Space ◽  
Generating Function ◽  
Performance Optimization ◽  
Optimization Method ◽  
Cloud Services ◽  
Services Composition ◽  
Fast Optimization ◽  
And Performance ◽  
Universal Generating Function

At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.

660MW Single Auxiliary Ultra-Supercritical Unit Main Stream’s Temperature Control Strategy Optimization Studies

2014 ◽  
Vol 909 ◽  
pp. 317-322
Author(s):  
Huan Pao Huang ◽  
Ji An Yu ◽  
Qian Su ◽  
Lei Wang
Keyword(s):  
Temperature Control ◽  
Control Strategy ◽  
Control Strategies ◽  
Dynamic Parameter ◽  
Pilot Project ◽  
Smith Predictor ◽  
Temperature Control System ◽  
Performance Requirements ◽  
And Performance ◽  
Adjustable Range

2 × 660MW ultra-supercritical units of O'Brien Power Plant are single configuration of auxiliary pilot project, due to the higher its parameters and performance requirements, it need better control strategies to ensure safe and economical operation. Against traditional cascade PID main steam’s temperature control system delaying large, this article proposed control strategy based on Smith estimated. Main steam’s temperature controlled object inert zone mathematical model can be showed by multi-volume model, and use the improved system for large inertia Smith Predictor to make dynamic parameter control systems improvements. Simulation results of the simulation machine show that: Optimization emperor steam temperature control is in an adjustable range and the policy in separate auxiliary units is feasible.

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