Learning extended FSA from software: An empirical assessment

A number of techniques that infer finite state automata from execution traces have been used to support test and analysis activities. Some of these techniques can produce automata that integrate information about the data-flow, that is, they also represent how data values affect the operations executed by programs.The integration of information about operation sequences and data values into a unique model is indeed conceptually useful to accurately represent the behavior of a program. However, it is still unclear whether handling heterogeneous types of information, such as operation sequences and data values, necessarily produces higher quality models or not.In this paper, we present an empirical comparative study between techniques that infer simple automata and techniques that infer automata extended with information about data-flow. We investigate the effectiveness of these techniques when applied to traces with different levels of sparseness, produced by different software systems. To the best of our knowledge this is the first work that quantifies both the effect of adding data-flow information within automata and the effectiveness of the techniques when varying sparseness of traces.     

Prediction of time-varying metrology delay for dEWMA and RLS-LT controllers

This paper investigates how to adaptively predict the time-varying metrology delay that can realistically occur in the semiconductor manufacturing practice. In the presence of metrology delays, the expected asymptotic double exponentially weighted moving average (dEWMA) control output, by using the EWMA and recursive least squares prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are equivalent, and six cases are addressed. Within the context of time-varying metrology delay, a new time update scheme to the recursive least squares-linear trend (RLS-LT) controller, combined with zone tests and the moving average (MA) control chart, is proposed. Simulated single input–single output (SISO) run-to-run processes subject to two time-varying metrology delay scenarios are used to assess the effectiveness of the proposed controller.     

A general bayes rule and its application to nonlinear estimation

This paper consists of two main results, a general Bayes rule, and a general Bucy representation theorem. The general Bayes rule is a natural generalization of the elementary Bayes rule:

$\text{P(A}\text{B)}\text{P(A)}\text{=}\text{P(B}\text{A)}\text{P(B)}$

. The general Bucy representation theorem plays a central role in nonlinear estimation theory as does the Bucy theorem in nonlinear filtering. A simple and direct proof of the general Bucy representation theorem is obtained by the application of the general Bayes rule.     

High-performance fully depleted silicon nanowire (diameter /spl les/ 5 nm) gate-all-around CMOS devices

This paper demonstrates gate-all-around (GAA) n- and p-FETs on a silicon-on-insulator with /spl les/ 5-nm-diameter laterally formed Si nanowire channel. Alternating phase shift mask lithography and self-limiting oxidation techniques were utilized to form 140- to 1000-nm-long nanowires, followed by FET fabrication. The devices exhibit excellent electrostatic control, e.g., near ideal subthreshold slope (/spl sim/ 63 mV/dec), low drain-induced barrier lowering (/spl sim/ 10 mV/V), and with I/sub ON//I/sub OFF/ ratio of /spl sim/10/sup 6/. High drive currents of /spl sim/ 1.5 and /spl sim/1.0 mA//spl mu/m were achieved for 180-nm-long nand p-FETs, respectively. It is verified that the threshold voltage of GAA FETs is independent of substrate bias due to the complete electrostatic shielding of the channel body.     

Simulation of interconnect inductive impact in the presence of process variations in 90 nm and beyond

The on-chip inductive impact on signal integrity has been a problem for designs in deep-submicrometer technologies. The inductive impact increases the clock skew, max timing, and noise of bus signals. In this letter, circuit simulations using silicon-validated macromodels show that there is a significant inductive impact on the signal max timing (/spl sim/ 10% pushout versus RC delay) and noise (/spl sim/2/spl times/RC noise). In nanometer technologies, process variations have become a concern. Results show that device and interconnect process variations add /spl sim/ 3% to the RLC max-timing impact. However, their impact on the RLC signal noise is not appreciable. Finally, inductive impact in 65- and 45-nm technologies is investigated, which indicates that the inductance impact will not diminish as technology scales.     

Competitive Effects of Trichloroethylene on Cr(VI) Removal by Zero-Valent Iron

Eleven columns were set up under various groundwater geochemistry conditions to investigate the competitive effect of trichloroethylene (TCE) on hexavalent chromium [Cr(VI)] removal by zero-valent iron (Fe0). They were found to be electron competitors in the redox reactions. In the presence of TCE, the Cr(VI) removal capacities of Fe0 were decreased by about 40% when compared with their respective Cr(VI) removal capacities with identical groundwater geochemistry but without TCE. The specific reaction rate constant (kSA) of TCE was decreased by about 50% when Cr(VI) was singly applied. The kSA of TCE was further decreased by 75% in the presence of both Cr(VI) and carbonate. However, there was no apparent effect on the kSA of TCE when Cr(VI), hardness and carbonate were all present. It revealed that TCE was a stronger electron competitor of Cr(VI) and the degradation of TCE became more favorable when both hardness and carbonate were present. This suggests that the passivated precipitates formed on the Fe0 surface in the presence of both hardness and carbonate may significantly affect the Cr(VI) removal by Fe0 but has insignificant effect on the TCE removal.     

Refinement computations of electromagnetic fields using FE and meshless methods

A refinement algorithm for electromagnetic field computations using a combination of finite element and meshless methods is introduced. Bridging scales are used to separate the finite element and meshless shape functions to make the refinement hierarchical and to uphold the mathematical properties such as consistency and linear independence for all the bases. To facilitate the application of the proposed algorithm, details about the node addition, requirements for the node distribution, and relationships between the finite element and meshless shape functions, as well as the determination of the stop criterion are also fully addressed. Primary numerical results are reported to demonstrate and validate the applicability and advantages of the proposed algorithm over traditional ones.     

Recursive estimation methods for discrete systems

In system identification, the error evolution is composed of two decoupled parts: one is the identifying information on the current estimation residual, while the other is past arithmetic errors. Previous recursive algorithms only considered how to update current prediction errors. Up to now, research has mostly been based on recursive least-squares (RLS) methods. In this note, a general recursive identification method is proposed for discrete systems. Using this new algorithm, a recursive empirical frequency-domain optimal parameter (REFOP) estimate is established. The REFOP method has the advantage of resisting disturbance noise. Some simulations are included to illustrate the new method's reliability.     

Deriving a transient stability index by neural networks for power-system security assessment

This paper proposes an approach for establishing a transient stability classifier and derives a continuous transient stability index, using a three-layer feed-forward artificial neural network (ANN), for on-line security assessment in large power systems. With the derived stability index, a novel classification scheme creating an ‘indeterminate’ class, is introduced to minimize misclassifications and to improve the reliability of the classification results. Several post-fault abstract attributes about the system generators’ acceleration rates and kinetic energies provide the basis for the stability classification. In order to derive the transient stability index, a semi-supervised backpropagation (BP) learning algorithm, making use of a specially defined error function, is developed. The proposed approach can not only distinguish whether a power system is stable or unstable, on the basis of the specific post-fault attributes, but can also provide a relative stability quantifier. Furthermore, as the number of the selected abstract attributes is independent of the system size, the methodology of the proposed approach can realistically be applied to large power systems. The 10-unit 39-bus New England power system is employed to demonstrate the proposed approach. The numerical results show that the ANN-based classifier can assess the transient stability reasonably well.