We propose a novel phrase extraction system to generate a phrase dictionary for predictive input methods from a large corpus. This system extracts phrases after counting n-grams so that it can be easily maintained, tuned, and re-executed independently. We developed a rule-based filter based on part-of-speech (POS) patterns to extract Japanese phrases. Our experiment shows usefulness of our syst...

A new linear predictive method is presented in this study. The method, Linear Prediction with Linear Extrapolation (LPLE), reformulates the computation of linear prediction by combining the preceding values of sample x(n) into consecutive sample pairs (i.e., x(n-2i), x(n-2i+1)). Each of these pairs determines a regression line the value of which at time instant n is used as a data sample in the...

Most of predictive maintenance technologies are inaccessible to small scale and medium scale industries due to their demanding cost. This paper proposes a predictive maintenance policy using failure mode effect and criticality analysis (FMECA) and non-homogeneous Poisson process (NHPP) models which require minimal use of advanced monitoring technologies and sophisticated data acquisition system...

In predictive control, a quadratic program (QP) needs to be solved at each sampling instant. We present a new warm-start strategy to solve a QP with an interior-point method whose data is slightly perturbed from the previous QP. In this strategy, an initial guess of the unknown variables in the perturbed problem is determined from the computed solution of the previous problem. We demonstrate th...

A closed-loop power control (CLPC) scheme with a multistep (indicating multiple prediction steps) linear autoregressive predictor is presented. The proposed CLPC relies on low-rate sample vector based autoregressive prediction. Compared to currently available predictive CLCP schemes, it demonstrates particularly robust performance in the presence of large loop delays and channel estimation errors.

Constructing a complete human haplotype map is helpful when associating complex diseases with their related SNPs. Unfortunately, the number of SNPs is very large and it is costly to sequence many individuals. Therefore, it is desirable to reduce the number of SNPs that should be sequenced to a small number of informative representatives called tag SNPs. In this paper, we propose a new linear al...

Compression is a process, in which the given size of data is compressed to a smaller size. Storing and sending images to its original form can present a problem in terms of storage space and transmission speed. Compression is efficient for storing and transmission purpose. Based on the reconstruction quality, compression can be lossy or lossless. Images used for biomedical research or astrophys...

The design of most process control is essentially a dynamic multi-objective optimization problem (Meadowcroft et al., 1992), sometimes with nonlinear characters, and in which both economic benefit and social benefit should be considered. Commonly speaking, there are contradictory objectives such as quantity of products, quality of products, safety of manufacturing, cost of manufacturing, enviro...

Predictive models are critical for risk adjustment in clinical research. Evaluation of supervised learning models often focuses on predictive model discrimination, sometimes neglecting the assessment of their calibration. Recent research in machine learning has shown the benefits of calibrating predictive models, which becomes especially important when probability estimates are used for clinica...

Abstract A new fast and efficient algorithm for the solution of the dynamic optimization problem resulted from the implementation of a model predictive control (MPC) framework in highly nonlinear dynamic systems is presented. The sequence of the optimal control actions is obtained through the solution of the parameterized set of the Karush-KuhnTucker (KKT) optimality conditions for the nonlinea...