Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Managing Change towards Lean Enterprises

Abstract: Encouraging experiences from a case study show that when lean manufacturing is implemented as an innovation process, and social stimulation games are applied, the resulting new systems are both economic and organizational successes The lead time of change is short and resistance to change is avoided The lean principles can spill over to other business processes, which ultimately creates a “lean” enterprise To guide this development, top‐down change management should be replaced by “lean” visions and guidelines Under this umbrella, the individual development projects should unfold as consciously managed innovation processes that encourage bottom‐up creativity and learning

Extending the Smodels system with cardinality and weight constraints

Abstract: The Smodels system is one of the state-of-the-art implementations of stable model computation for normal logic programs. In order to enable more realistic applications, the basic modeling language of normal programs has been extended with new constructs including cardinality and weight constraints and corresponding implementation techniques have been developed. This paper summarizes the extensions that have been included in the system, demonstrates their use, provides basic application methodology, illustrates the current level of performance of the system, and compares it to state-of-the-art satisfiability checkers.

The FastICA Algorithm Revisited: Convergence Analysis

Abstract: The fast independent component analysis (FastICA) algorithm is one of the most popular methods to solve problems in ICA and blind source separation. It has been shown experimentally that it outperforms most of the commonly used ICA algorithms in convergence speed. A rigorous local convergence analysis has been presented only for the so-called one-unit case, in which just one of the rows of the separating matrix is considered. However, in the FastICA algorithm, there is also an explicit normalization step, and it may be questioned whether the extra rotation caused by the normalization will affect the convergence speed. The purpose of this paper is to show that this is not the case and the good convergence properties of the one-unit case are also shared by the full algorithm with symmetrical normalization. A local convergence analysis is given for the general case, and the global behavior is illustrated numerically for two sources and two mixtures in several typical cases

On the strain-hardening parameters of metals

Abstract: The applicability of the Ludwik, Hollomon, Swift and Voce equations in describing the stress - strain curves of metals was investigated. Calculated uniform strain values were found to depend on the equation used. Even when the Hollomon equation gave a high linear correlation coefficient in log-log coordinates, the strain-hardening exponent n could give an erroneous uniform strain. The equation with the lowest standard error of estimate gave the uniform strain nearest to the value obtained by direct measurement from the load-elongation curve.