MONAA: A Tool for Timed Pattern Matching with Automata-Based Acceleration

We present monaa, a monitoring tool over a real-time property specified by either a timed automaton or a timed regular expression. It implements a timed pattern matching algorithm that combines 1) features suited for online monitoring, and 2) acceleration by automata-based skipping. Our experiments demonstrate monaa's performance advantage, especially in online usage.Comment: Published in: 2018 IEEE Workshop on Monitoring and Testing of Cyber-Physical Systems (MT-CPS

Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces

We present a method to extract a weighted finite automaton (WFA) from a recurrent neural network (RNN). Our algorithm is based on the WFA learning algorithm by Balle and Mohri, which is in turn an extension of Angluin's classic \lstar algorithm. Our technical novelty is in the use of \emph{regression} methods for the so-called equivalence queries, thus exploiting the internal state space of an RNN to prioritize counterexample candidates. This way we achieve a quantitative/weighted extension of the recent work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally evaluate the accuracy, expressivity and efficiency of the extracted WFAs.Comment: AAAI 2020. We are preparing to distribute the implementatio

Structure Based Compact Model for Output Capacitance of Trench Field-Plate MOSFET to Enable Power Loss Prediction

We propose a structure based compact model for out-put capacitance (Coss) of trench Field-Plate MOSFET. Ap-propriate equations were considered for Coss curves in three regions. Output charge (Qoss) and stored energy (Eoss) that were calculated by the proposed model corre-sponded very well to TCAD results. In assumption of 10 A and 2 MHz operation, conduction loss of 1.0 W and out-put charge loss of 1.26 W were estimated.2017 International Conference on Solid State Devices and Materials (SSDM2017), Sendai International Center, Sendai, Japan, September 19-22, 201