DEFINITION Sensor networks allow for micro-monitoring of different phenomena of interest in arbitrary physical environments. With this unique capability, sensor networks can capture the events in the real world as they happen in the form of high-resolution spatiotemporal data of various modalities, and provide the opportunity for real-time querying and analysis of the data for immediate respons...

The most formidable challenge in assembling a Smart Grid is the integration of a high penetration of renewables. Demand Response, a largely promising concept, is increasingly discussed as a means to cope with the intermittent and uncertain renewables. In this paper, we propose a dynamic market mechanism that reaches the market equilibrium through continuous negotiations between key market playe...

We study optimal monetary policy when the empirical evidence leaves the policymaker uncertain whether the true data-generating process is given by a model with sticky wages or a model with search and matching frictions in the labor market. Unless the policymaker is almost certain about the search and matching model being the correct data-generating process, the policymaker chooses to stabilize ...

Uncertain databases are used in some fields to store both certain and uncertain data. When uncertainty is represented with the theory of belief functions, uncertain databases are assumed to be evidential. In this paper, we suggest a new method to quantify the source degree of dependence in order to enrich its evidential database by adding this dependence information. Enriching evidential databa...

This paper is motivated by the fact that nearly half of U.S. college students drop out without earning a bachelor’s degree. Its objective is to quantify how much uncertainty college entrants face about their graduation outcomes. To do so, we develop a quantitative model of college choice. The innovation is to model in detail how students progress towards a college degree. The model is calibrate...

In many applications, data are often imperfect, incomplete or more generally uncertain. This imperfection has to be integrated into the learning process as an information in itself. The E2M decision trees is a methodology that provides predictions from uncertain data modelled by belief functions. In this paper, the problem of rubber quality prediction is presented with a belief function modelli...

This paper is concerned with solving large scale linear fractional multiple objective programming (LSLFMOP) problems with chance constraints. Channce constraints involve random parameters in the right-hand sides. These random right-hand sides are considered to be statistically independent random variable. The main features of the proposed solution procedure are based on chanceconstrained techni...

The uncertainty theory solves problems with uncertain data. Often to perform arithmetic operations on uncertain data, the calculations on intervals are necessary. Interval arithmetic uses traditional mathematics in the calculations on intervals. There are many methods that solve the problems of uncertain data presented in the form of intervals, each of them can give in some cases different resu...

Recently, Ning & Kearfott derived a formula for the interval enclosure of the solution set of linear systems of equations with uncertain data ranging in intervals, in the case when the coeecient matrix is an H-matrix. The enclosure is optimal when the midpoint matrix is diagonal, and when the midpoint is the identity, it reduces to the optimal method for enclosing preconditioned systems found b...