Feedback linearization of differential-algebraic systems and force and position control of manipulators

The question of realization and feedback linearization of a class of differential-algebraic system is considered. Based on nonlinear inversion of an input-output map, an analytical expression for the constraint force vector satisfying the algebraic constraints is derived. In this derivation, certain requirements on the relative degree of the output variables are relaxed. Using a new representation of the system in an extended state space, a control law is derived for the independent control of the chosen output variables satisfying algebraic constraints. These results are applied for the position and force control of robotic manipulators. Simulation results are presented for a three-link robotic arm with revolute joints. It is shown that in the closed-loop system, precise position and force trajectory control is accomplished in spite of uncertainty in the robot parameters.     

Editorial

Pattern Analysis and Applications -     

Pulsed laser deposition of Y2O3 : Eu thin film phosphors

Thin films of Y₂O₃ : Eu cathodoluminescent (CL) phosphors were deposited using pulsed laser deposition using deposition temperature between 250°C and 800°C, O₂ pressures between residual vacuum (2×10⁻⁵ Torr) and 6 Torr, and post annealing up to 1200° for 1 h in air. The CL efficiency of the best thin film was about one third that of the starting powder. The brightness and efficiency of the thin films improved as the deposition temperature, O₂ pressure and post annealing temperature were increased, except that O₂ pressures above 600 mTorr did not significantly improve the CL properties. At deposition temperatures >600°C, the surface morphology changed from a smooth film to a nodular deposit for O₂ pressures >200 mTorr, with nodule dimensions ≈100 nm. Simultaneously, the CL properties improved dramatically because of enhanced optical scattering out of the thin film. Optical scattering was discussed in terms of anomalous diffraction. The CL properties also improved dramatically with high temperature post annealing. This effect was interpreted in terms of improved crystallinity and activation of the Eu. The low brightness and efficiency of thin films versus powder was affected by depletion of the Eu in the thin films owing to the deposition process.     

CRT and LCD monitor and process materials evaluated for environmental improvement

Decision rules have been developed and applied to the bills of materials of a color cathode‐ray tube (CRT) and a liquid‐crystal‐display (LCD) desktop monitor to determine which product and process materials will be evaluated in an environmental life‐cycle assessment. Materials of significant mass, of technological importance, and of potential environmental impact are targeted. The list of materials identified are those for which life‐cycle inventory data will be obtained for the materials extraction and materials processing life‐cycle stages of a CRT and an LCD. Additionally, materials identified will also be used to represent life‐cycle impact in terms of resource consumption, as well as surrogates for occupational health impacts.     

An Upper Bound on the Loss from Approximate Optimal-Value Functions

Many reinforcement learning approaches can be formulated using the theory of Markov decision processes and the associated method of dynamic programming (DP). The value of this theoretical understanding, however, is tempered by many practical concerns. One important question is whether DP-based approaches that use function approximation rather than lookup tables can avoid catastrophic effects on performance. This note presents a result of Bertsekas (1987) which guarantees that small errors in the approximation of a task's optimal value function cannot produce arbitrarily bad performance when actions are selected by a greedy policy. We derive an upper bound on performance loss that is slightly tighter than that in Bertsekas (1987), and we show the extension of the bound to Q-learning (Watkins, 1989). These results provide a partial theoretical rationale for the approximation of value functions, an issue of great practical importance in reinforcement learning.     

Adaptive solutions to the mutual exclusion problem

Summary Algorithms for mutual exclusion that adapt to the current degree of contention are developed. Afilter and a leader election algorithm form the basic building blocks. The algorithms achieve system response times that are independent of the total number of processes and governed instead by the current degree of contention. The final algorithm achieves a constant amortized system response time. Manhoi Choy was born in 1967 in Hong Kong. He received his B.Sc. in Electrical and Electronic Engineerings from the University of Hong Kong in 1989, and his M.Sc. in Computer Science from the University of California at Santa Barbara in 1991. Currently, he is working on his Ph.D. in Computer Science at the University of California at Santa Barbara. His research interests are in the areas of parallel and distributed systems, and distributed algorithms. Ambuj K. Singh is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara. He received a Ph.D. in Computer Science from the University of Texas at Austin in 1989, an M.S. in Computer Science from Iowa State University in 1984, and a B.Tech. from the Indian Institute of Technology at Kharagpur in 1982. His research interests are in the areas of adaptive resource allocation, concurrent program development, and distributed shared memory.A preliminary version of the paper appeared in the 12th Annual ACM Symposium on Principles of Distributed ComputingWork supported in part by NSF grants CCR-9008628 and CCR-9223094     

Approaches to Multisensor Data Fusion in Target Tracking: A Survey

The tracking of objects using distributed multiple sensors is an important field of work in the application areas of autonomous robotics, military applications, and mobile systems. In this survey, we review a number of computationally intelligent methods that are used for developing robust tracking schemes through sensor data fusion. The survey discusses the application of the various algorithms at different layers of the JDL model and highlights the weaknesses and strengths of the approaches in the context of different applications     

Ubiquitous computing: connecting Pervasive computing through Semantic Web

Ubiquitous computing refers to building a global computing environment where seamless and invisible access to computing resources is provided to the user. Pervasive computing deals with acquiring context knowledge from the environment and providing dynamic, proactive and context-aware services to the user. A Ubiquitous computing environment is created by sharing knowledge and information between Pervasive computing environments. In this paper we propose a framework that uses the potential of the Semantic Web to weave Pervasive computing environments into a Ubiquitous computing environment. We discuss how the collaboration of these Pervasive environments can create an effective Ubiquitous computing environment referred herein as the Integrated Global Pervasive Computing Framework (IGPF). We test the effectiveness of the Ubiquitous environment through a small scenario from a prototype system that we have implemented over this framework to handle medical emergency scenario.     

Frequency-truncated system norms

Some applications require norm computation of frequency-truncated systems. A typical frequency-truncated system is one whose frequency response is rational in certain frequency bands and is zero in others. This note explains how to compute the L² norm of such systems.     

Considering the fault dependency concept with debugging time lag in software reliability growth modeling using a power function of testing time

Since the early 1970s tremendous growth has been seen in the research of software reliability growth modeling.In general, software reliability growth models (SRGMs) are applicable to the late stages of testing in software development and they can provide useful information about how to improve the reliability of software products.A number of SRGMs have been proposed in the literature to represent time-dependent fault identification/removal phenomenon;still new models are being proposed that could fit a greater number of reliability growth curves.Often,it is assumed that detected faults axe immediately corrected when mathematical models are developed.This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault,the skill and experience of the personnel,the size of the debugging team,the technique,and so on.Thus,the detected fault need not be immediately removed,and it may lag the fault detection process by a delay effect factor.In this paper,we first review how different software reliability growth models have been developed,where fault detection process is dependent not only on the number of residual fault content but also on the testing time,and see how these models can be reinterpreted as the delayed fault detection model by using a delay effect factor.Based on the power function of the testing time concept,we propose four new SRGMs that assume the presence of two types of faults in the software:leading and dependent faults.Leading faults are those that can be removed upon a failure being observed.However,dependent faults are masked by leading faults and can only be removed after the corresponding leading fault has been removed with a debugging time lag.These models have been tested on real software error data to show its goodness of fit,predictive validity and applicability.