Helsinki University of Technology

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

Modeling of compound semiconductors: Analytical bond-order potential for Ga, As, and GaAs

Abstract: An analytical bond-order potential for GaAs is presented, that allows one to model a wide range of properties of GaAs compound structures, as well as the pure phases of gallium and arsenide, including nonequilibrium configurations. The functional form is based on the bond-order scheme as devised by Abell-Tersoff and Brenner, while a systematic fitting scheme starting from the Pauling relation is used for determining all adjustable parameters. Reference data were taken from experiments if available, or computed by self-consistent total-energy calculations within the local density-functional theory otherwise. For fitting the parameters, only structural data of the metallic phases of gallium and arsenide as well as those of different GaAs phases were used. A number of tests on point defect properties, surface properties, and melting behavior have been performed afterward in order to validate the accuracy and transferability of the potential model, but were not part of the fitting procedure. While point defect properties and surfaces with low As content are found to be in good agreement with literature data, the description of As-rich surface reconstructions is not satisfactory. In the case of molten GaAs we find support for a structural model based on experiment that indicates a polymerized arsenic phase in the melt.

Bankruptcy analysis with self-organizing maps in learning metrics

Abstract: We introduce a method for deriving a metric, locally based on the Fisher information matrix, into the data space. A self-organizing map (SOM) is computed in the new metric to explore financial statements of enterprises. The metric measures local distances in terms of changes in the distribution of an auxiliary random variable that reflects what is important in the data. In this paper the variable indicates bankruptcy within the next few years. The conditional density of the auxiliary variable is first estimated, and the change in the estimate resulting from local displacements in the primary data space is measured using the Fisher information matrix. When a self-organizing map is computed in the new metric it still visualizes the data space in a topology-preserving fashion, but represents the (local) directions in which the probability of bankruptcy changes the most.

Adaptation of Motor Parameters in Sensorless PMSM Drives

Abstract: This paper proposes an online method for the estimation of the stator resistance and the permanent-magnet (PM) flux in sensorless PM synchronous motor drives. An adaptive observer augmented with a high-frequency signal injection technique is used for sensorless control. The observer contains excess information that is not used for the speed and position estimation. This information is used for the adaptation of the motor parameters: At low speeds, the stator resistance is estimated, whereas at medium and high speeds, the PM flux is estimated. Small-signal analysis is carried out to investigate the proposed method. The convergence of the parameter estimates is shown by simulations and laboratory experiments. The stator resistance adaptation works down to zero speed in sensorless control.

Influence of polymer-pore interactions on translocation.

Abstract: We investigate the influence of polymer-pore interactions on the translocation dynamics using Langevin dynamics simulations. An attractive interaction can greatly improve the translocation probability. At the same time, it also increases the translocation time slowly for a weak attraction while an exponential dependence is observed for a strong attraction. For fixed driving force and chain length the histogram of translocation time has a transition from Gaussian distribution to long-tailed distribution with increasing attraction. Under a weak driving force and a strong attractive force, both the translocation time and the residence time in the pore show a nonmonotonic behavior as a function of the chain length. Our simulations results are in good agreement with recent experimental data.