Stochastic Methods Applied to Structural Mechanics

Uncertainty modelling with random variables motivates the adoption of advanced PTM for reliability analysis to solve problems of mechanical systems. Probabilistic transformation method (PTM) is readily applicable when the function between the input and the output of the system is explicit. When these functions are implicit, a technique is proposed that combines finite element analysis (FEA) and probabilistic transformation method (PTM) that is based on the numerical simulations of the finite element analysis (FEA) and the probabilistic transformation method (PTM) using an interface between finite element software and Matlab. Structure problems are treated with the proposed technique, and the obtained results are compared to those obtained by the reference Monte Carlo method. A second aim of this work is to develop an algorithm of global optimization using the local method SQP. The proposed approach MSQP is tested on test functions comparing with other methods, and it is used to resolve a structural problem under reliability constraints.

Stochastic Methods for Estimation and Problem Solving in Engineering

In this chapter, the author presents stochastic methods in aviation. The stochastic methods are presented as methods of decision making (DM) of operators of air navigation systems (ANS) in risk and uncertainly. The ANS is presented as a socio-technical system (STS). Analysis influences the factors of professional and non-professional activities on DM of STS's operators. The author made an analysis of the International Civil Aviation Organization (ICAO) documents on risk assessment. To determine the quantitative characteristics of risk levels, models for DM by the operator of the aviation system under risk and uncertainty have been developed. The author demonstrates some interesting advantages offered by the new methodology of DM in STS and forecasting the behavior of the operator in an emergency situation (ES) for using models of DM in risk and uncertainty.

Factors Influencing Patients' Satisfaction

In complex systems, such as healthcare, patient satisfaction is identified as the patient experience of care and has been referred to as the “indispensable outcome.” The main goals of ED practitioners are the patient satisfaction along with maintaining optimal outcomes. Patient satisfaction has become a very important outcome measure when assessing healthcare systems performance. Nevertheless, it is a complex confusing concept. Some providers suggest several activities in order to enhance the satisfaction without being sure if these actions really improve satisfaction or no. Also, patient satisfaction enhancement activities should not conflict with cost efficacy. Several factors fall under patient satisfaction. Interviews with physicians prove that patients have specific expectations during a clinical encounter; being aware of these expectations, physicians can fulfill patient satisfaction. The goal of this chapter is to determine the contribution and effect of these factors in influencing patient satisfaction.

A New Technique for Optimization of Product Acceptance Process in Terms of Misclassification Probability

A product acceptance process is an inspecting one in statistical quality control or reliability tests, which are used to make decisions about accepting or rejecting lots of products to be submitted. This process is important for industrial and business purposes of quality management. To determine the optimal parameters of the product acceptance process under parametric uncertainty of underlying lifetime models (in terms of misclassification probability), a new optimization technique is proposed. The most popular lifetime distribution used in the field of product acceptance is a two-parameter Weibull distribution, with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Therefore, the situations are also considered when both Weibull distribution parameters are unknown. An illustrative numerical example is given.

Utilization of Social Networks for Human Resources Purposes

Social media is revolutionizing the way people connect and share information. LinkedIn, Facebook, YouTube, Twitter, and other social media are changing the way we interact, and many organizations are struggling to respond. Given the potential risks and benefits of social media in the workplace, it is critical for managers to develop policies and procedures governing its appropriate use. This chapter identifies key issues and poses strategic questions to help guide managers in making more informed decisions when navigating social media issues in their organizations. After a brief introduction, current most popular social websites and tools are described concisely. Relationships between social media and human resources are then discussed. Utilizing social media in organizations are not without risks, which are thoroughly talked about further along with the benefits of such websites for recruitment. Finally, recommendations are made for companies that are considering utilizing social media and for companies that have already benefitted from such networks to improve their strategies.

Stochastic Methods in Microsystems Engineering

The main objective of this chapter is to present a stochastic modeling and simulation methodology for estimating the variation of functional parameters of MEMS devices as a result of fabrication tolerances. The approach used for achieving this objective is to implement in the simulation process the variations of the geometrical parameters caused by the fabrication dispersion as random variables and to model the influence of these variations on the performance of the simulated device. The proposed method is demonstrated using two test structures: a micro-com resonator and a passive micromixer. In each example there are underlined important characteristics of the proposed simulation method: the ability to predict variation ranges of functional parameters, the ability to improve the design in function of the fabrication process, and the possibility of obtaining more precise results than the traditional deterministic methods.

Stochastic Approximation-Based Transport Profiling for Big Data Movement Over Dedicated Connections

High-performance networks featuring advance bandwidth reservation have been developed and deployed to support big data transfer in extreme-scale scientific applications. The performance of such big data transfer largely depends on the transport protocols being used. For a given protocol in a given network environment, different parameter settings may lead to different performance, and oftentimes the default settings do not yield the best performance. It is, however, impractical to conduct an exhaustive search in the large parameter space of transport protocols for a set of suitable parameter values. This chapter proposes a stochastic approximation-based transport profiler, namely FastProf, to quickly determine the optimal operational zone of a protocol over dedicated connections. The proposed method is evaluated using both emulations based on real-life measurements and experiments over physical connections. The results show that FastProf significantly reduces the profiling overhead while achieving a comparable level of transport performance with the exhaustive search-based approach.

Stability of Linear Systems With Multitask Right-Hand Member

To study the dynamics of mechanical systems and to define the construction parameters and control laws, it is necessary to have computational models accurately describing properties of real mechanisms. From a mathematical point of view, the computational models of mechanical systems are actually the systems of differential equations. These models can contain equations that also describe non-mechanical phenomena. In this chapter, the problems of stability and asymptotic stability conditions for the motion of mechanical systems with holonomic and non-holonomic constraints are under consideration. Stability analysis for the systems of differential equations is given in term of the second Lyapunov's method. With the use of the set-theoretic approach, the necessary and sufficient conditions for stability and asymptotic stability of zero solution of the considered system are formulated. The proposed approaches can be used to study the stability of the motion for robot manipulators, transport, space, and socio-economic systems.

Combination of Stochastic Methods for Solving ELD Problem of Thermal Power Generation System

This chapter reports a hybrid optimization technique, a combination of stochastic methods – particle swarm optimization (PSO) and ant colony optimization (ACO), which is applied to find economic dispatch schedule and minimum generation cost for convex and non-convex power generation system simulated in MATLAB. A 40-generator system is considered here with combinations of valve point loading, ramp rate limit, and prohibited operating zone. The output is also noted when transmission loss is taken into consideration. The results are found better than those of many other hybrid methods. Considering the quality of the solution obtained and nature of convergence, PSO-ACO may be accepted as a good alternative for solving ELD problems of varying complexity. Though PSO has been extensively used in ELD problems for its flexibility, robustness, and fast convergence, it often produces suboptimal solution due to its premature convergence. ACO, on the other hand, known for its good global exploration feature, imparts better balance between local and global search when combined with PSO.