A modelling-oriented scheme for control chart pattern recognition

Control charts are graphical tools that monitor and assess the performance of production processes, revealing abnormal (deterministic) disturbances when there is a fault. Simple patterns belonging to one of six types can be observed when a fault is occurring, and a Normal pattern when the process is performing under its intended conditions. Machine Learning algorithms have been implemented in this research to enable automatic identification of simple patterns. Two pattern generation schemes (PGS) for synthesising patterns are proposed in this work. These PGSs ensure generality, randomness, and comparability, as well as allowing the further categorisation of the studied patterns. One of these PGSs was developed for processes that fulfil the NIID (Normally, identically and independently distributed) condition, and the other for three first-order lagged time series models. This last PGS was used as base to generate patterns of feedback-controlled processes. Using the three aforementioned processes, control chart pattern recognition (CCPR) systems for these process types were proposed and studied. Furthermore, taking the recognition accuracy as a performance measure, the arrangement of input factors that achieved the highest accuracies for each of the CCPR systems was determined. Furthermore, a CCPR system for feedback-controlled processes was developed

Affective feedback: an investigation into the role of emotions in the information seeking process

User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with respect to the cognitive and situational levels of interaction that occurs between the user and the retrieval system. However, apart from real-life problems and information objects, users interact with intentions, motivations and feelings, which can be seen as critical aspects of cognition and decision-making. The study presented in this paper serves as a starting point to the exploration of the role of emotions in the information seeking process. Results show that the latter not only interweave with different physiological, psychological and cognitive processes, but also form distinctive patterns, according to specific task, and according to specific user

Advanced Diagnostics of Position Sensors for the Actuation Systems of High-Speed Tilting Trains

Trains tilting permits a train to travel at a high speed while maintaining an acceptable passenger ride quality with respect to the lateral acceleration, and the consequent lateral force, received by the passengers when the train travels on a curved track at a speed in excess of the balance speed built into the curve geometry. The tilting of a train carbody is performed by a control and actuation system which operates as a closed servoloop accepting the commands from the train control system, generating the torque necessary to tilt the carbody with respect to the bogie and measuring the tilt angle to close the control loop. Measurement of the tilt angle of each train vehicle is performed by two sensors located in the front and rear part of the vehicle. Since a correct tilt angle measurement is vital for the system operation and for ensuring a safe ride, in case of discrepancy between the signals of the two tilt angle sensors of any vehicle, the tilting operation is disabled and the train speed is reduced. An innovative tilt angle sensors health management system is herein presented that makes intelligent use of all available information to allow detection of malfunctioning of an individual tilt angle sensor, thereby enabling a continued operation of the tilting system and a high speed travel after a sensor failure occurs

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Learning, Categorization, Rule Formation, and Prediction by Fuzzy Neural Networks

National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-91-J-4100, N00014-92-J-4015) Air Force Office of Scientific Research (90-0083, N00014-92-J-4015

NASA Center for Intelligent Robotic Systems for Space Exploration

NASA's program for the civilian exploration of space is a challenge to scientists and engineers to help maintain and further develop the United States' position of leadership in a focused sphere of space activity. Such an ambitious plan requires the contribution and further development of many scientific and technological fields. One research area essential for the success of these space exploration programs is Intelligent Robotic Systems. These systems represent a class of autonomous and semi-autonomous machines that can perform human-like functions with or without human interaction. They are fundamental for activities too hazardous for humans or too distant or complex for remote telemanipulation. To meet this challenge, Rensselaer Polytechnic Institute (RPI) has established an Engineering Research Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The Center was created with a five year $5.5 million grant from NASA submitted by a team of the Robotics and Automation Laboratories. The Robotics and Automation Laboratories of RPI are the result of the merger of the Robotics and Automation Laboratory of the Department of Electrical, Computer, and Systems Engineering (ECSE) and the Research Laboratory for Kinematics and Robotic Mechanisms of the Department of Mechanical Engineering, Aeronautical Engineering, and Mechanics (ME,AE,&M), in 1987. This report is an examination of the activities that are centered at CIRSSE