This paper shows a parameter identification scheme for speed sensorless vector controlled induction motor using output inter-sampling approach and experimentally evaluates this method. It is well known that both the rotor speed and the rotor time-constant of induction motors cannot be identified under steady state operation. This is because vector control system of induction motors consists of ...

In this paper, we propose a new document clustering method based on the K-means method (kmeans). In our method, we allow only finite candidate vectors to be representative vectors of kmeans. We also propose a method for constructing these candidate vectors using documents that have the same word. We participated in NTCIR-4 WEB Task D (Topic Classification Task) and experimentally compared our m...

Because of its wide range of substrates, caspase-3, a main executioner among apoptosis-related caspases, is thought to have many unknown substrates that have remained unidentified. This report describes our predictive method to facilitate the discovery of novel caspase-3 substrates. To develop a more reliable prediction method, we specifically examined improvement of the data quantity and quali...

A technique to enhance the response and performance of Brillouin distributed fiber sensors is proposed and experimentally validated. The method consists in creating a multi-frequency pump pulse interacting with a matching multi-frequency continuous-wave probe. To avoid nonlinear cross-interaction between spectral lines, the method requires that the distinct pump pulse components and temporal tr...

This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is def...

We consider the problem of feature-efficient prediction – a setting where features have costs and the learner is limited by a budget constraint on the total cost of the features it can examine in test time. We focus on solving this problem with boosting by optimizing the choice of base learners in the training phase and stopping the boosting process when the learner’s budget runs out. We experi...