Interval-based Techniques Research Articles

Substrate Selection Framework for Organic Thin-Film Transistor Based on Flexibility and Reliability Issues

Flexibility is an important feature of organic thin-film transistor (OTFT) technology. The need for flexibility adds challenges to the mechanical reliability of the OTFTs. This work focuses on mechanical and thermal failure mechanisms in an OTFT and their relation with the substrate properties. It is observed that four material parameters of the substrate, namely, Young’s modulus, thermal conductivity, coefficient of thermal expansion, and glass transition temperature, are crucial in determining the reliability and flexibility of the OTFT. Further more, using an interval-based technique for order preference by similarity to ideal solution (TOPSIS) technique, five polymer substrates including polycarbonate (PC), polyethylene naphthalate (PEN), polyethylene terephthalate (PET), polyimide (PI), and polyethersulfone (PES) are analyzed. It is concluded that PI is the most suitable substrate material for OTFTs to improve flexibility and reliability.

Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information system and multi agent systems (GIS and MASs, respectively). We also propose an approach for dynamic task allocation and establishing collaboration among agents based on contract net protocol (CNP) and interval-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which consider uncertainty in natural hazards information during agents’ decision-making. The decision-making weights were calculated by analytic hierarchy process (AHP). In order to implement the system, earthquake environment was simulated and the damage of the buildings and a number of injuries were calculated in Tehran’s District 3: 23%, 37%, 24% and 16% of buildings were in slight, moderate, extensive and completely vulnerable classes, respectively. The number of injured persons was calculated to be 17,238. Numerical results in 27 scenarios showed that the proposed method is more accurate than the CNP method in the terms of USAR operational time (at least 13% decrease) and the number of human fatalities (at least 9% decrease). In interval uncertainty analysis of our proposed simulated system, the lower and upper bounds of uncertain responses are evaluated. The overall results showed that considering uncertainty in task allocation can be a highly advantageous in the disaster environment. Such systems can be used to manage and prepare for natural hazards.

Threat assessment for driver assistance systems using interval-based techniques

AbstractWe present a threat assessment algorithm for driver assistance systems in which mathematical vehicle and driver models are used to explicitly account for the vehicle and driver behavior. For the application considered in this paper, requirements that the vehicle stays on the road while operating in a stable operating region are expressed as constraints on the vehicle states which need to be satisfied over a finite time horizon. The threat assessment problem is then formulated as a constraint satisfaction problem, solved through interval-based techniques considering bounded uncertainty in the measurements. Experimental results demonstrate the algorithms ability to detect unsafe operation in advance.

Improving inter-block backtracking with interval Newton

Inter-block backtracking (IBB) computes all the solutions of sparse systems of nonlinear equations over the reals. This algorithm, introduced by Bliek et al. (1998) handles a system of equations previously decomposed into a set of (small) k ×k sub-systems, called blocks. Partial solutions are computed in the different blocks in a certain order and combined together to obtain the set of global solutions. When solutions inside blocks are computed with interval-based techniques, IBB can be viewed as a new interval-based algorithm for solving decomposed systems of nonlinear equations. Previous implementations used Ilog Solver and its IlcInterval library as a black box, which implied several strong limitations. New versions come from the integration of IBB with the interval-based library Ibex. IBB is now reliable (no solution is lost) while still gaining at least one order of magnitude w.r.t. solving the entire system. On a sample of benchmarks, we have compared several variants of IBB that differ in the way the contraction/filtering is performed inside blocks and is shared between blocks. We have observed that the use of interval Newton inside blocks has the most positive impact on the robustness and performance of IBB. This modifies the influence of other features, such as intelligent backtracking. Also, an incremental variant of inter-block filtering makes this feature more often fruitful.

An interval-based technique for FE model updating

Model updating techniques are largely used in civil and mechanical engineering to obtain reliable FE models. The model parameters are iteratively adjusted until the model response matches the measured structural response within a given tolerance. The task is made difficult by the uncertainty that affects both the structural response and the model parameters. In this work, the uncertainty is taken into account by a proper formulation of the problem in the framework of interval analysis. Uncertainty is replaced by interval numbers, functions are replaced by their thin or thick natural extension and the inclusion theorem is exploited to find the problem solution. Sample applications are illustrated using a modal representation of the structure. A numerical example is used to discuss the potential of the proposed method. A case study is solved to demonstrate the advantages of the method with respect to conventional updating techniques.

Real-Time Application of Interval Methods for Robust Control of Dynamical Systems

The influence of bounded uncertainties in parameters and initial conditions of dynamical systems can be analyzed efficiently with the help of interval methods. In engineering, interval arithmetic tools are usually applied offline to verify, analyze, and design dynamical systems. In this contribution, we describe interval-based techniques for online model-predictive control of uncertain continuous-time dynamical systems. These techniques employ verified solution algorithms for sets of differential-algebraic equations which are implemented in the solver ValEncIA-IVP. Using ValEncIA-IVP, we derive procedures for feedforward control, trajectory planning, and guaranteed state and parameter estimation. The real-time applicability of these procedures is demonstrated by experimental results for a control-oriented, finite-dimensional model of a distributed heating system.

Uncertainty analysis of a neural network used for fatigue lifetime prediction

The application of interval set techniques to the quantification of uncertainty in a neural network regression model of fatigue lifetime is considered. Bayesian evidence training was implemented to train a series of multi-layer perceptron networks on experimental fatigue life measurements in glass fibre composite sandwich materials. A set of independent measurements conducted 2 months after the training session, and at intermediate fatigue loading levels, was used to provide a rigorous test of the generalisation capacity of the networks. The robustness of the networks to uncertainty in the input data was investigated using an interval-based technique. It is demonstrated that the interval approach allowed for an alternative to probabilistic-based confidence bounds of prediction accuracy. In addition, the technique provided an alternative network selection tool, and also allowed for an alternative to estimating the lifetime prediction error that was found to be a significant improvement over the Bayesian-derived estimate of confidence bound.

Power cables' thermal protection by interval simulation of imprecise dynamical systems

The embedding of advanced simulation techniques in power cables enables improved thermal protection because of higher accuracy, adaptiveness and flexibility. In particular, they make possible (i) the accurate solution of differential equations describing the cables thermal dynamics and (ii) the adoption of the resulting solution in the accomplishment of dedicated protective functions. However, the use of model-based protective systems is exposed to the uncertainty affecting some model components (e.g. weather along the line route, thermophysical properties of the soil, cable parameters). When uncertainty can be described in terms of probability distribution, well-known techniques, such as Monte Carlo, are used to simulate the system behaviour. On the other hand, when the description of uncertainty in probabilistic terms is unfeasible or problematic, nonprobabilistic alternatives should be taken into consideration. This paper will discuss and compare three interval-based techniques as alternatives to probabilistic methods in the simulation of power cable dynamics. The experimental session will assess the interval-based approaches by simulating the thermal behaviour of medium voltage power cables.

Interval methods for modeling uncertainty in RC timing analysis

The authors propose representing uncertain parameters as intervals and present a theoretical framework based on interval algebra for manipulating these ranges. To illustrate this methodology, they modify an existing RC analysis algorithm (Crystal's PR-Slope model) to create one which computes worst-case delay bounds when given uncertain input parameters. They provide proofs of correctness for the approach and test its performance. Two alternate interval-based techniques which produce even tighter bounds than the original approach are also presented. When compared to Monte Carlo simulation, the interval methods are more precise and significantly faster.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>