Hybrid Methods for Detection and Identification of Faults in Dynamic Systems

Since the 1960s, when automation became essential to productivity, methods for the detection and identification of faults have been proposed. Physical systems are diversified and can be mechanical, electrical, pneumatic, electronic, or a combination of these. In addition, real plants have a large number of these devices, which are for its own operation, sensoring or control. Therefore the solutions given for detection of faults are generally very specific or particular. This paper aims to describe and analyze two hybrid methods of detection and fault identification based on residue and to check whether their inclusion with other methods, combining different techniques, can produce a better fault detection and identification system. The methods use the state observers for the generation of residues, which serve for the detection and identification and the set called the bank of signatures to identify the faults. Thereafter, the methods use different approaches to diagnose the fault: the first uses the approach of the mean square error, and the second uses a decision tree.     

Employing ai methods to control the behavior of animated interface agents

Life - like characters are increasingly gaining the attention of researchers and commercial developers of user interfaces . A strong argument in favor of using such characters in the interface is the rich repertoire of options they offer , enabling the emulation of communication styles common in human - human dialog . This contribution presents a framework for the development of presentation agents , which can be used for a broad range of applications including personalized information delivery fromthe WWW .     

Contract signing, optimism, and advantage

A contract signing protocol lets two parties exchange digital signatures on a pre-agreed text. Optimistic contract signing protocols enable the signers to do so without invoking a trusted third party. However, an adjudicating third party remains available should one or both signers seek timely resolution. We analyze optimistic contract signing protocols using a game-theoretic approach and prove a fundamental impossibility result: in any fair, optimistic, timely protocol, an optimistic player yields an advantage to the opponent. The proof relies on a careful characterization of optimistic play that postpones communication to the third party.     

A novel deep network architecture for reconstructing RGB facial images from thermal for face recognition

Multimedia Tools and Applications - This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face...     

An evaluation of pure spectrum-based fault localization techniques for large-scale software systems

Pure spectrum-based fault localization (SBFL) is a well-studied statistical debugging technique that only takes a set of test cases (some failing and some passing) and their code coverage as input and produces a ranked list of suspicious program elements to help the developer identify the location of a bug that causes a failed test case. Studies show that pure SBFL techniques produce good ranked lists for small programs. However, our previous study based on the iBugs benchmark that uses the Aspect J repository shows that, for realistic programs, the accuracy of the ranked list is not suitable for human developers. In this paper, we confirm this based on a combined empirical evaluation with the iBugs and the Defects4 J benchmark. Our experiments show that, on average, at most ∼40%, ∼80%, and ∼90% of the bugs can be localized reliably within the first 10, 100, and 1000 ranked lines, respectively, in the Defects4 J benchmark. To reliably localize 90% of the bugs with the best performing SBFL metric D^∗, ∼450 lines have to be inspected by the developer. For human developers, this remains unsuitable, although the results improve compared with the results for the Aspect J benchmark. Based on this study, we can clearly see the need to go beyond pure SBFL and take other information, such as information from the bug report or from version history of the code lines, into consideration.     

Intelligent environment recognition and prediction for NDT inspection through autonomous climbing robot

This paper presents a novel approach to environment mapping prediction with focus on autonomous climbing robot to NDT (Non-Destructive Technique) inspection. In industrial installations, the inspection of non-planar surfaces requires that NDT probes passe on whole surface, while the autonomous robot navigates over an unknown environment based only on its perception abilities. However, the path planning of inspection is not a trivial task specially when there is no precise information about environment. In this work, a special kind of climbing robot is used to inspect large metallic surfaces such as spherical pressure vessels used to store Liquified Petroleum Gas (LPG). The robot has adherence skills that allow it to safely navigate through the internal and external surface of the vessel. As a result, robot mobility suffers from hard magnetic adhesion constraints. A new approach is proposed to environment detailed prediction, including specific characteristic (like weld beads and plates) of inspected surface. The goal is the automatic extraction of some environment characteristics to predict the storage tank dimensions and robot localization, based on a group of 3D perception sources (laser rangefinder, light detection and ranging and depth camera) mounted over a rolling platform to improve its reach. The environment prediction is carried out after the robot visually detects two or more weld beads corners. A multi-measuring environment is firstly build by Fuzzy data fusion of the different perception measurements allowing to estimate plates and weld beads based on design and safety standards. Virtual and real experiments are carried out to illustrate proposed method performance.