Managing time-awareness in modularized processes

Software and Systems Modeling - Managing temporal process constraints in a suitable way is crucial for long-running business processes in many application domains. However, proper support of...     

Model-free data interpretation for continuous monitoring of complex structures

Civil engineering structures are difficult to model accurately and this challenge is compounded when structures are built in uncertain environments. As consequence, their real behavior is hard to predict; such difficulties have important effects on the reliability of damage detection. Such situations encourage the enhancement of traditional approximate structural assessments through in-service measurements and interpretation of monitoring data. While some proposals have recently been made, in general, no current methodology for detection of anomalous behavior from measurement data can be reliably applied to complex structures in practical situations.This paper presents two new methodologies for model-free data interpretation to identify and localize anomalous behavior in civil engineering structures. Two statistical methods (i) moving principal component analysis and (ii) moving correlation analysis have been demonstrated to be useful for damage detection during continuous static monitoring of civil structures.The algorithms are designed to learn characteristics of time series generated by sensor data during a period called the initialization phase where the structure is assumed to behave normally. This phase subsequently helps identify those behaviors which can be classified as anomalous. In this way the new methodologies can effectively identify anomalous behaviors without explicit (and costly) knowledge of structural characteristics such as geometry and models of behavior. The methodologies have been tested on numerically simulated elements with sensors at a range of damage severities. A comparative study with wavelet and other statistical analyses demonstrates superior performance for identifying the presence of damage.     

Hyper temporal networks

Simple Temporal Networks (STNs) provide a powerful and general tool for representing conjunctions of maximum delay constraints over ordered pairs of temporal variables. In this paper we introduce Hyper Temporal Networks (HyTNs), a strict generalization of STNs, to overcome the limitation of considering only conjunctions of constraints but maintaining a practical efficiency in the consistency check of the instances. In a Hyper Temporal Network a single temporal hyperarc constraint may be defined as a set of two or more maximum delay constraints which is satisfied when at least one of these delay constraints is satisfied. HyTNs are meant as a light generalization of STNs offering an interesting compromise. On one side, there exist practical pseudo-polynomial time algorithms for checking consistency and computing feasible schedules for HyTNs. On the other side, HyTNs offer a more powerful model accommodating natural constraints that cannot be expressed by STNs like “Trigger off exactly δ min before (after) the occurrence of the first (last) event in a set.”, which are used to represent synchronization events in some process aware information systems/workflow models proposed in the literature.     

An algorithm for the selection of high‐contrast color sets

Nominal color coding is the aesthetic and functional use of color to convey qualitative information in graphical environments. The specification of high‐contrast color sets is a fundamental step in this process. We formulate the color‐coding problem here as a combinatorial optimization problem on graphs and present an algorithm that performs well and does not require that the function used to code the similarity between colors be a distance function. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 132–138, 1999     

Continuous-Action Q-Learning

This paper presents a Q-learning method that works in continuous domains. Other characteristics of our approach are the use of an incremental topology preserving map (ITPM) to partition the input space, and the incorporation of bias to initialize the learning process. A unit of the ITPM represents a limited region of the input space and maps it onto the Q-values of M possible discrete actions. The resulting continuous action is an average of the discrete actions of the winning unit weighted by their Q-values. Then, TD() updates the Q-values of the discrete actions according to their contribution. Units are created incrementally and their associated Q-values are initialized by means of domain knowledge. Experimental results in robotics domains show the superiority of the proposed continuous-action Q-learning over the standard discrete-action version in terms of both asymptotic performance and speed of learning. The paper also reports a comparison of discounted-reward against average-reward Q-learning in an infinite horizon robotics task.