Mitsubishi Electric

About: Mitsubishi Electric is a company organization based out in Ratingen, Germany. It is known for research contribution in the topics: Signal & Voltage. The organization has 23024 authors who have published 27591 publications receiving 255671 citations. The organization is also known as: Mitsubishi Electric Corporation & Mitsubishi Denki K.K..

Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression

Abstract: This paper presents novel machine-learning-based methods for estimating the state of charge (SoC) of lithium-ion batteries, which use the Gaussian process regression (GPR) framework. The measured battery parameters, such as voltage, current, and temperature, are used as inputs for regular GPR, whereas the SoC estimate at the previous sample is fed back and incorporated into the input vector for recurrent GPR. The proposed methods consist of two parts. In the first part, training is performed wherein the optimal hyperparameters of a chosen kernel function are determined to model data properties. In the second part, online SoC estimation is carried out according to the trained model. One of the practical advantages of a GPR framework is to quantify estimation uncertainty and, hence, to enable reliability assessment of the battery SoC estimate. The performance of the proposed methods is evaluated by using a simulated dataset and two experimental datasets, one with constant and the other with dynamic charge and discharge currents. The simulations and experimental results show the superiority of the proposed methods in comparison to state-of-the-art techniques including a support vector machine, a relevance vector machine, and a neural network.

Shift register circuit and image display apparatus equipped with the same

Abstract: Malfunction caused by leakage current of the transistor and shift in threshold voltage is prevented in the shift register in which the signal can be shifted bi-directionally. The bi-directional unit shift register includes a first transistor Q 1 for providing a first clock signal CLK to an output terminal OUT, a second transistor Q 2 for discharging the output terminal OUT based on a second clock signal, third and fourth transistors Q 3 , Q 4 for providing first and second voltage signals Vn, Vr complementary to each other to a first node, which is a gate node of the first transistor Q 1 , and a fifth transistor Q 5 connected between the first node and the output terminal OUT. The fifth transistor Q 5 is in an electrically conducted state based on the first clock signal CLK when the gate of the transistor Q 1 is at L (Low) level.

Mining features for sequence classification

Abstract: Classi cation algorithms are di cult to apply to sequential examples, such as plan executions or text, because there is a vast number of potentially useful features for describing each example. Past work on feature selection has focused on searching the space of all subsets of the available features which is intractable for large feature sets. We adapt data mining techniques to act as a preprocessor to select features for standard classi cation algorithms such as Naive Bayes and Winnow. We apply our algorithm to the task of predicting whether or not a plan will succeed or fail, during plan execution. The features produced by our algorithm improve classi cation accuracy by 10-50% in our experiments. Submitted to IJCAI'99. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1998 201 Broadway, Cambridge, Massachusetts 02139 Rensselaer Polytechnic Institute University of Rochester Publication History:{ 1. First printing, TR-98-22, December 1998

Face recognition system using local autocorrelations and multiscale integration

Abstract: In this paper we investigate the performance of a technique for face recognition based on the computation of 25 local autocorrelation coefficients. We use a large database of 11,600 frontal facial images of 116 persons, organized in training and test sets, for evaluation. Autocorrelation coefficients are computationally inexpensive, inherently shift-invariant and quite robust against changes in facial expression. We focus on the difficult problem of recognizing a large number of known human faces while rejecting other, unknown faces which lie quite close in pattern space. A multiresolution system achieves a recognition rate of 95%, while falsely accepting only 1.5% of unknown faces. It operates at a speed of about one face per second. Without rejection of unknown faces, we obtain a peak recognition rate of 99.9%. The good performance indicates that local autocorrelation coefficients have a surprisingly high information content.

Using plan recognition in human-computer collaboration

Abstract: Human-computer collaboration provides a practical and useful application for plan recognition techniques. We describe a plan recognition algorithm which is tractable by virtue of exploiting properties of the collaborative setting, namely: the focus of attention, the use of partially elaborated hierarchical plans, and the possibility of asking for clarification. We demonstrate how the addition of our plan recognition algorithm to an implemented collaborative system reduces the amount of communication required from the user.