Discretization of Continuous Time Systems by Using Laguerre functions (original) (raw)

A Direct Devirtualization Technique with the Code Patching Mechanism

2002

This paper presents a direct devirtualization technique for a language such as Java with dynamic class loading. The implemetation of this technique is easy. For a given dynamic method call, a compiler generates the inlined code of the method, together with the code of making the dynamic call. Only the inlined code is actually executed until our assumption about the devirtualization becomes invalidated, at which time the compiler performs code patching to make the code of dynamic call executed subsequently. This technique does not require complicated implementations such as deoptimization to recompile the method that is active on the stack. Since this technique prevents some optimizations across the merge point between the inlined code and the dynamic call, we have furthermore proposed optimization techniques effectively. We made some experiments to understand the effectiveness and characteristics of the devirtualization techniques in our Java Just-In-Time compiler. To summarize our result, we improved the execution performance of SPECjvm98 and SPECjbb2000 ranging from 0% to 181% (with the geometric mean of 24%).

Joint Inference of Temporal Relations Identification with Markov Logic

Transactions of the Japanese Society for Artificial Intelligence, 2009

Recent work on temporal relation identification has focused on three types of relations between events: temporal relations between an event and a time expression, between a pair of events and between an event and the document creation time. These types of relations have mostly been identified in isolation by event pairwise comparison. However, this approach neglects logical constraints between temporal relations of different types that we believe to be helpful. We therefore propose a Markov Logic model that jointly identifies relations of all three relation types simultaneously. By evaluating our model on the TempEval data we show that this approach leads to about 2% higher accuracy for all three types of relations-and to the best results for the task when compared to those of other machine learning based systems.

Discrete-Time Noncausal Linear Periodically Time-Varying Scaling for Robustness Analysis and Controller Synthesis

2013

Since modeling of real plants inevitably gives rise to modeling errors regarded as uncertainties, considering robustness for the uncertainties is important in actual control problems. For tackling issues of analyzing robust stability of closed-loop systems in a less conservative fashion, the μ-analysis method is known to be effective. As an alternative approach to robust stability analysis, on the other hand, discrete-time noncausal linear periodically time-varying (LPTV) scaling has been proposed recently. This approach can be naturally introduced through the lifting-based treatment of systems, and the associated conservativeness can be reduced by increasing the period of lifting. This thesis is concerned with this lifting-based scaling approach. In this thesis, we first review the definition and properties of noncausal LPTV scaling. This scaling approach is a generalization of the conventional causal linear time-invariant (LTI) scaling, and coincides with the latter scaling when w...

Identification of System Characteristics of a Power System with Time Series Data

IEEJ Transactions on Power and Energy

Understanding actual characteristics of a power system with recorded time series data is of great importance, for example, to improve the performance of the system. Although system identification is a well-known technique to achieve this goal, its applicability to a certain system should be examined for the particular case because its accuracy highly depends on the inherent characteristics of the system. While many papers have discussed application of a system identification technique to a power system, few papers have examined its applicability to the actual data of a power system. This paper presents a new system identification method to estimate characteristics of a power system while using output of intermittent generators or fluctuating loads as an external disturbance. The method employs cross spectra and coherence as a key factor in the identification; it estimates a transfer function of a power system, contribution of observed disturbance to total disturbance, etc. The method is applied to time series data of two model systems: simulation results and measured data of an isolated power system with diesel generators. The study gives satisfactory results; implication on the accuracy of the method is discussed through the sample studies.