We have developed an automated framework for performance evaluation of curved-surface range image segmentation algorithms. Enhancements over our previous work include automated training of parameter values, correcting the artifact problem in K T scanner images, and acquisition of images of the same scenes from different range scanners. The image dataset includes planar, spherical, cylindrical, ...

Over recent years, the model-based parameter tuning of computational systems has become an emergent research topic. When the considered systems are subject of stochastic effects, the parameter tuning can be seen as a noisy optimization task, as often faced in production engineering. In this paper, the current research on parameter optimization is discussed based on experiences from an engineeri...

Recent literature provides many computational and modeling approaches for covariance matrices estimation in a penalized Gaussian graphical models but relatively little study has been carried out on the choice of the tuning parameter. This paper tries to fill this gap by focusing on the problem of shrinkage parameter selection when estimating sparse precision matrices using the penalized likelih...

In the conventional two degree freedom (TDF)-internal model controller (IMC) design, obtaining suitable value of tuning parameter is much more complex and difficult. In this paper, the approach of genetic algorithm (GA) is proposed to obtain optimized value of tuning parameter used in TDF-IMC controller design. For TDF-IMC controller design, 2 nd order reduced-model is obtained using the Routh ...

This paper deals with the experimental control of a rotating active magnetic bearing (AMB) system using PID-type fuzzy controllers (PIDFCs) with parameter adaptive methods. There are three kinds of parameter adaptive methods, including fuzzy tuner, function tuner, and relative rate observer, have been proposed in literatures for tuning the coefficients of PIDFCs. However, only a simulation comp...

Over recent years, the model-based parameter tuning of computational systems has become an emergent research topic. When the considered systems are subject of stochastic effects, the parameter tuning can be seen as a noisy optimization task, as often faced in production engineering. In this paper, the current research on parameter optimization is discussed based on experiences from an engineeri...

Based on parameter self-tuning fuzzy PID controller, a fuzzy inference method is utilized to realize automatic regulating PID parameter, and the application of the controller in a system is studied with MATLAB in this paper. The combination of PID controller system and fuzzy controller system combines the convenience of PID control together with the flexibility of fuzzy control, and takes advan...

The Terrain-Based Genetic Algorithm (TBGA) is a self-tuning version of the traditional Cellular Genetic Algorithm (CGA). In a TBGA, various combinations of parameter values appear in different physical locations of the population, forming a sort of terrain in which individual solutions evolve. We compare the performance of the TBGA against that of the CGA on a known suite of problems. Our resul...

In this work, we study parameter tuning towards the M2 metric, the standard metric for automatic grammar error correction (GEC) tasks. After implementing M2 as a scorer in the Moses tuning framework, we investigate interactions of dense and sparse features, different optimizers, and tuning strategies for the CoNLL-2014 shared task. We notice erratic behavior when optimizing sparse feature weigh...

Application auto-tuning has produced excellent results in a wide range of computing domains. Yet adapting an application to use this technology remains a predominately manual and labor intensive process. This paper explores first steps towards reducing adoption cost by focusing on two tasks: parameter identification and range selection. We show how these traditionally manual tasks can be automa...