Recognition and analysis of local structure in polycrystalline configurations

A method is described for obtaining information about the local order existing in monoatomic model solids or real materials based on their atomistic configurations. An efficient algorithmic implementation is provided. The shape of the polyhedra formed by ‘relevant’ neighbors of each atom enter a pattern recognition method to resolve the type of the (usually non-ideal) crystal structure to which atoms surrounded by their relevant neighbors belong: hexagonal close-packed, face-centered cubic or body-centered cubic. Further, this approach allows for the analysis of icosahedral structure which preferably occurs in amorphous solids. Results of a molecular dynamics computer simulation illustrate how this method can be applied to contribute to an understanding of the mechanical and structural properties of solids (i) undergoing a steady shear stress and (ii) upon increasing temperature.     

A method for improved distribution concentration in thetime-frequency analysis of multicomponent signals using the L-Wignerdistribution

The energy location in the Cohen class of time-frequency distributions is analyzed. If the instantaneous frequency is linear, then only the Wigner distribution produces the ideal energy concentration. The scaled version of the Wigner distribution (L-Wigner distribution), is used to improve the time-frequency representation of signals with nonlinear instantaneous frequencies. In the case of multicomponent signals, the cross terms, appearing in the Wigner distribution and in the L-Wigner distribution, can be easily removed or reduced in a computationally very efficient way. The theory is illustrated on the numerical examples with multicomponent noisy signals     

Using stochastic learning automata for job scheduling in distributed processing systems

A stochastic model for decentralized control of job scheduling in distributed processing systems is presented. The basic decisionmaker of this model, replicated at each host, consists of a Stochastic Learning Automaton (SLA). Because of the environment under consideration, various practical enhancements to the basic SLA model are developed. It is shown by simulation that the overall performance of the system improves over the basic SLA model. Comparisons to some known analytical models and other algorithms are also made. These comparisons illustrate that the SLA based system performs much better than the M/M/1 and Fractional Assignment analytical models and is reasonably close to the best analytical bound. The range of effectiveness of the SLA algorithm with respect to several others is noted. What is significant about this work is that we have illustrated that it is possible to design a learning controller that is able to dynamically acquire relevant job scheduling information by a process of trial and error, and use that information to provide good performance. Other advantages of the algorithm are its low execution overhead, its potential reliability because it is decentralized, and its ability to recover gracefully from random events such as surges of new jobs entering the system. It is important to note that almost all other scheduling algorithms are tested only under equilibrium conditions.     

Highly concentrated time-frequency distributions: pseudo quantumsignal representation

Distributions that are highly concentrated in the time-frequency plane are presented. Since the idea for these distributions originated from the Wigner representation in the quantum mechanics, a review of this representation is done in the first part of the paper. Abstracting the physical sense of the quantum mechanics representation, we defined the “pseudo quantum” signal representation. On the basis of a signal, the “pseudo wave function” with the corresponding “pseudo particle” having the “pseudo momentum” _p=ℏ_fω is generated. By varying the value of ℏ_f, one is in a position to influence the concentration of the “pseudo quantum” (time-“pseudo momentum”) signal's presentation while keeping its most important time-frequency properties invariant. From this reflection, an efficient distribution for the time-frequency (time-“pseudo momentum”) signal analysis is obtained. This distribution produces as high a concentration in the time-frequency (time-“pseudo momentum”) plane as the L-Wigner distribution; however, it may satisfy the marginal properties. The theory is illustrated with examples     

Robust L-estimation based forms of signal transforms and time-frequency representations

The L-estimation based signal transforms and time-frequency (TF) representations are introduced by considering the corresponding minimization problems in the Huber (1981, 1998) estimation theory. The standard signal transforms follow as the maximum likelihood solutions for the Gaussian additive noise environment. For signals corrupted by an impulse noise, the median-based transforms produce robust estimates of the non-noisy signal transforms. When the input noise is a mixture of Gaussian and impulse noise, the L-estimation-based signal transforms can outperform other estimates. In quadratic and higher order TF analysis, the resulting noise is inherently a mixture of the Gaussian input noise and an impulse noise component. In this case, the L-estimation-based signal representations can produce the best results. These transforms and TF representations give the standard and the median-based forms as special cases. A procedure for parameter selection in the L-estimation is proposed. The theory is illustrated and checked numerically.     

Integration of equation- and signal-based models in transient analysis of electric energy systems

The paper addresses analytical and practical aspects of integration of equation-based (classical) and signal-derived [artificial neural network (ANN)] dynamic models for transient analysis of large-scale dynamical systems. Our signal-based part is based on two ANNs, and is derived from measurements at boundary points. In this paper, we describe this hybrid modeling technique, and focus on: 1) a least square-based mechanism for on-line correction of dynamic variable predictions that is based on actual operating conditions; 2) the resilience of the algorithm to missing measurements due to failed communication links; and 3) a complete two-way interaction between the differential-algebraic equation based subsystem and the ANN-based subsystem. The paper demonstrates the feasibility of implementing our approach in standard power system software by integrating the ANN-based model with the transient analysis toolbox from Matlab. We illustrate capabilities of the proposed approach for transient analysis on a benchmark multi-machine example derived from the New England/New-York interconnected power system.     

离散随机系统的包含原理

提出线性时变离散随机系统的包含原理.根据系统包含的定义,给出系统状态观测 与反馈控制闭环系统的约束条件和聚集条件,使具有重叠结构和模型降阶系统的反馈设计或 LQG设计有充分的选择.     

Applications of Ellipsoidal Approximations to Polyhedral Sets in Power System Optimization

The paper presents a computational method that approximates feasible sets specified by linear or convex inequalities. This numerically efficient approach to power system optimization is based on computational geometry of multidimensional ellipsoids and is potentially applicable to problems with high dimensions, as it builds on recent advances in convex optimization. In an important application, it provides ranges in which nodal (generator) injections can vary without violating operational constraints in security analysis. The model is applied to two important problems in deregulated power systems: optimal economic dispatch (OED) and calculation of locational marginal prices (LMPs) in a day-ahead power market. Optimization problem with convex (ellipsoid-based) constraints is solved by a linear matrix inequality (LMI)-based procedure. The method is verified on the benchmark example with 68 buses, 16 generators, and 86 lines.      

Priority inheritance in soft real-time databases

Due to resource sharing among tasks, priority inversion can occur during priority-driven preemptive scheduling. In this work, we investigate solutions to the priority inversion problem in a soft real-time database environment where two-phhse locking is employed for concurrency control. We examine two basic schemes for addressing the priority inversion problem, one based on priority inheritance and the other based on priority abort. We also study a new scheme, called conditional priority inheritance, which attempts to capitalize on the advantages of each of the two basic schemes. In contrast with previous results obtained in real-time operating systems, our performance studies, conducted on an actual real-time database testbed, indicate that the basic priority inheritance protocol is inappropriate for solving the priority inversion problem in real-time database systems. We identify the reasons for this performance. We also show that the conditional priority inheritance scheme and the priority abort scheme perform well for a wide range of system workloads.This work was supported by the National Science Foundation under Grant IRI-8908693 and by the U.S. Office of Naval Research under Grant N00014-85-K0398.A previous version of this paper appeared in Real-Time Systems Symposium, Dec. 1991.