Four tier architecture of component selection process using clustering

2015 ◽  
Vol 1 (2/3/4) ◽  
pp. 155
Author(s):  
Jagdeep Kaur ◽  
Pradeep Tomar
Keyword(s):  
Selection Process ◽  
Component Selection

scholarly journals Design of Aerodynamic Ball Levitation Laboratory Plant

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1950
Author(s):  
Tomáš Tkáčik ◽  
Milan Tkáčik ◽  
Slávka Jadlovská ◽  
Anna Jadlovská
Keyword(s):  
Mathematical Model ◽  
Printed Circuit Board ◽  
Least Squares Method ◽  
Selection Process ◽  
Nonlinear Least Squares ◽  
Circuit Board ◽  
Component Selection ◽  
Physical Plant ◽  
Nonlinear Least Squares Method ◽  
Physical Construction

This paper presents the development of a new Aerodynamic Ball Levitation Laboratory Plant at the Center of Modern Control Techniques and Industrial Informatics (CMCT&II). The entire design process of the plant is described, including the component selection process, the physical construction of the plant, the design of a printed circuit board (PCB) powered by a microcontroller, and the implementation of its firmware. A parametric mathematical model of the laboratory plant is created, whose parameters are then estimated using a nonlinear least-squares method based on acquired experimental data. The Kalman filter and the optimal state-space feedback control are designed based on the obtained mathematical model. The designed controller is then validated using the physical plant.

scholarly journals A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 384
Author(s):  
Mohammad Reza Jabbarpour ◽  
Ali Mohammad Saghiri ◽  
Mehdi Sookhak
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Selection Process ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Component Selection ◽  
Wide Range ◽  
Selection Framework

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.

The Computational Complexity of and Approximation Algorithms for Variants of the Component Selection Problem

2018 ◽  
Vol 29 (07) ◽  
pp. 1231-1245
Author(s):  
Mostafa Nouri-Baygi
Keyword(s):  
Computational Complexity ◽  
Approximation Algorithms ◽  
Selection Process ◽  
Selection Problem ◽  
Software Systems ◽  
Minimal Set ◽  
Component Selection ◽  
Np Complete ◽  
Complete Process ◽  
General Component

In the past decades, there has been a burst of activity to simplify implementation of complex software systems. The solution framework in software engineering community for this problem is called component-based software design (CBSD), whereas in the modeling and simulation community it is called composability. Composability is a complex feature due to the challenges of creating components, selecting combinations of components, and integrating the selected components. In this paper, we address the challenge through the analysis of Component Selection (CS), the NP-complete process of selecting a minimal set of components to satisfy a set of objectives. Due to the computational complexity of CS, we consider approximation algorithms that make the component selection process practical. We define three variations of CS and present good approximation algorithms to find near optimal solutions. In spite of our creation of approximable variants of Component Selection, we prove that the general Component Selection problem is inapproximable.

scholarly journals An Elite Model for COTS Component Selection Process

2017 ◽  
Vol 02 (01) ◽  
pp. 18-24
Author(s):  
Asif Irshad Khan
Keyword(s):  
Selection Process ◽  
Component Selection

Method for Computational Implementation of Components Selection System

2015 ◽  
Author(s):  
Claudio J. Weber ◽  
Gilberto F. M. de Souza ◽  
Erick M. P. Hidalgo ◽  
Mateus Mayer
Keyword(s):  
Selection Process ◽  
Decision Matrix ◽  
Production Chain ◽  
Dependency Matrix ◽  
Component Selection ◽  
Technical Requirements ◽  
Computational Implementation ◽  
Application Mode ◽  
Process Work ◽  
Two Stages

The tools used to select mechanical components in a design process work separately, do not communicate with each other, and do not focus the selection process of all the components in a product as one, with all its interrelationships and the effects that one choice have over other to which it is connected, the effect over the production chain, and the supply logistic. To assist in this process a method for component selection is being proposed to be implemented in an expert system (ES) that can select all the components in one integrated form without requiring the use of other tools. The method is organized in two stages, the first one defines the classification and organization of components based on ontology. The second step, the information is organized as a matrix of decision for each component group. In order to allow the information use by the ES, they need to be expressed in a metric way. The influence information of a component over another one, which the first is connected to is empirical, undocumented, and is related to the application mode. These influences generate technical requirements on the borderer components, so they are connected and structured by a dependency matrix. The information of decision matrix and influences are used for creating a knowledge base, based on rules to select the components. As a result the selected components are shown in a structure as a tree, where all selected components for the project are seen, how they connect, the influence that one causes over the other, the justification of selection, what criteria and emphasis are used in the selection, which requirements is necessary in the parts to be manufactured, and which components may not be used and why. In order to show how the method works, it will be used the design of a machine’s transmission.

A comparison of the United Kingdom Clinical Aptitude Test (UK-CAT) with a traditional admission selection process

2009 ◽  
pp. 1-6 ◽  
Author(s):  
Nishan Fernando ◽  
Gordon Prescott ◽  
Jennifer Cleland ◽  
Kathryn Greaves ◽  
Hamish McKenzie
Keyword(s):  
United Kingdom ◽  
Selection Process ◽  
Aptitude Test ◽  
The United Kingdom
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