Michele Pappalardo

Methodology development of human task simulation as PLM solution related to OCRA ergonomic analysis

In the current demanding global marketplace, ensuring that human fit, form and function are comprehensively addressed, is becoming an increasingly important aspect of design and, in particular, obliges the most important automotive industries to develop more flexible assembly lines and better methods for PLM solution. In the meantime, designers attempt to elaborate product development methodologies that conform health and safety standards while still maximizing the productivity. The aim of this work consists in developing a methodology based on preventive ergonomics and feasibility analyses of assembly tasks, simulating a work cell, in which acts a digital human model (manikin), in order to maximize human safety and performance and analyze manikin interaction in the virtual environment. In ergonomic analyses the OCRA protocol will be used, evaluating different involvement degrees of upper limb segments. The methodology is carried out by ergonomic tool of DELMIA software, using Digital Human Models technology.

Studying the influence of cognitive load on driver's performances by a Fuzzy analysis of Lane Keeping in a drive simulation.

Abstract The psycho-physical state of a driver has been widely recognized as the crucial point in any issue concerning the development of models headed to improve the vehicle safety, either inherent and active, so much so that almost all the new in-vehicle technology, currently developing at a rapid rate, introduces devices to continuously monitor the driver. This paper describes the architecture of a real time, performance-based, driver monitoring system able to detect the decrease in driver performances due to driver distraction, fatigue, sleepiness and alcohol or drugs ingestion. The system processes the instantaneous lateral position of the vehicle on the road. This allows to work out an index of the lane keeping precision by means of the lateral position standard deviation (SDLP). This latter and the road environment complexity has been processed by a fuzzy inference system that has, as an output, a score reflecting the drivers ability to maintain adequate lane-tracking movements for a given road scenario. Fuzzy membership functions and inference rules has been based and optimized on data obtained on 12 subjects performing driving simulation under both baseline condition and two different cognitive overload situations induced by different secondary tasks, one with visual distraction, the other characterized by a pure cognitive load, respectively. Aim of the work is to attain to a black-box type devices that could both provide warnings or reminding in case of risky driving and encourage the driver to improve his behavior. Advantages would also come for parents of novice drivers promptly alerted for improper driving and even for the car insurance companies that could reward safe drivers.

Measure of independence in soft design

Abstract In this paper is analysed the problem, using soft models, of soft dependence of parameters in design systems. A new form of computing, called Soft Computing , is recently used in many emerging disciplines because it is tolerant to imprecision, uncertainty and partial truth. The Soft Computing uses many disciplines as Bayesian inference and maximum entropy method . The logic relationship that ties the different elements can be defined more easily using the axioms of soft design emanating from MinEnt principle . The fundamental axiom of design is: valid design has minimum values of information and depends on a finite and limited number of independent, or soft dependent , parameters.

A fuzzy design evaluation based on Taguchi quality approach

A fuzzy method to handle vagueness and imprecision in the description of requirements for multiattribute decision making problem is presented. This method is applied to design of an apron conveyors to collect and transfer scraps. The aggregation function for the overall evaluation is obtained utilising the Taguchi loss functions.

Complexity in Energy Policy: A Fuzzy Logic Methodology

This Twenty-first century global systems such as climate change models, energy systems, and international trade, have traditionally trusted conventional logic and mathematics to reduce complexity. The 2008 failing of conventional predictive models (in all economical, political, and social spheres) proved that a new approach is needed to understand and predict the behavior of man-made complex systems. This paper is an attempt to introduce a fuzzy logic methodology for formulating energy policies. A new definition of an energy system is given based on its fuzzy functionality and complex inter-relational properties. Instead of crisp numbers, fuzzy values are proposed to be defined and used for evaluating policy parameters. Unknown or poorly known factors can be taken into account through adding fuzzy constants to otherwise linear equations established between the system parameters. Fuzziness of the variables are transferred from the inputs to the outputs without being unduly magnified or eliminated. Results of a fuzzy model of energy policy can be expressed in fuzzy values, reflecting the realities and providing flexibility in implementation

FIDELITY: Fuzzy Inferential Diagnostic Engine for on-LIne supporT to phYsicians

A diagnostic engine for supporting physicians in analyzing symptoms and anamnesis of patients is proposed.

Fuzzy-logic application in the structural optimisation of a support plate for electrical accumulator in a motor vehicle

Abstract In this paper, we analyse the design of a support plate for the accumulator of an electric motor vehicle. The support is an integral part of motor vehicle chassis. Therefore, geometrical configuration and boundary conditions require careful optimisation research of both function and structural behaviour, since lightness and dimension problems in the presence of dynamic stresses due to external factors have to be considered. Since these factors are complex and not homogeneous, the problem requires multi-criteria analysis. The presence of factors that are not precisely computable calls for fuzzy-logic application to optimisation problems, because fuzzy-logic is non-standard logic, particularly suitable for making choices in structural design. In plate optimisation, in fact, not numerically quantifiable characteristics such as a part’s workability, numerically determinable structural values such as stresses and strains, and analytically calculable properties such as weight come into play. These four parameters become the domain of fuzzy membership functions, by which we will extract membership grade values (co-domain). Design variables (domain) are plate thickness, ashlar’s number on the plate and stiffening ashlar’s depth. In our research, we characterise fuzzy correlation between parameters and required characteristics in order to determine, according to non-standard logics, the best topological configuration which corresponds to the optimisation of individualised characteristics in conformity with design constraints. Results show value improvement in stress and strain in comparison with the not yet optimised plate and small reduction in workability, whereas the mass is almost the same.

Designing in many-valued logic

The analysis described is based on the many-valued logic of Lukasiewitcz (1970). It leads to the construction of a simple design model when the analysis cannot be based upon a two-valued logic. The reference is based on the semantics of Kripke, immersion in a definite possible world, and on the process of verification and confirmation of Carnap. The example given is based on the statistics of Dempster and Shafer.

MaxEnt Principle for Handling Uncertainty with Qualitative Values

Bayesian mathematical model is the oldest method for modelling subjective degree of belief. If we have probabilistic measures with unknown values, then we must choose a different and appropriate model. The belief functions are a bridge between various models handling different forms of uncertainty. The conjunctive rule of Bayes builds a new set of a posteriori probability when two independent and accepted sets of random variable make inference. When two pieces of evidence are accepted with unknown values, the Dempster‐Shafer’s rule suggests a model for fusion of different degree of belief. In this paper we want to submit the use of MaxEnt principle for modelling the belief. Dealing with non‐Bayesian sets, in which the piece of evidence represents the belief instead of the knowledge, the MaxEnt principle gives a tool to reduce the number of subsets representing the frame of discernment. The fusion a focal set with a set of max entropy cause a Bayesian approximation reducing mass function to a probabilistic...

Introduction to the Information in Metric Space

The idea of information, in the classic theories of Fisher and Wiener‐Shannon, is mutual or relative information only on probabilistic and repetitive events. The idea of information is larger than the probability. The Wiener‐Shannon’s axioms can be extended to the non‐probabilistic and repetitive events. It is possible to introduce a Theory of Information for events not connected to the probability. On the basis of so called Laplace’s Principle of insufficient knowledge, and from the MaxEnt Principle the MaxInf Principle is developed for choosing solutions in absence of knowledge. In this paper the information is applied in numeric analysis as method for to put in reciprocal relation data with polynomial functions.