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.

Biomass measurement online: the performance of in situ measurements and software sensors

Abstract: Biomass measurement is one of the most critical measurements in biotechnological processes. The technologies developed for the measurement of biomass in situ have developed over the years. Because it has been over 10 years since the last review concentrating on practical issues concerning biomass measurements, it is time to evaluate recent developments in the field. This review concentrates on the applications of dielectric spectroscopy, optical density, infrared spectroscopy, and fluorescence for in situ measurement of biomass. The advantages offered by these methods and an economic way of estimating biomass concentration, the software sensors, are considered.

Spectral Characteristics of Light Harvesting, Electron Injection, and Steady-State Charge Collection in Pressed TiO2 Dye Solar Cells

Abstract: The factors that limit photocurrent in dye solar cells (DSC) were studied by incident-photon-to-collected-electron efficiency (ηIPCE), optical, and photovoltaic measurements. Nanostructured TiO2 photoelectrodes were prepared by compression technique on glass substrates, and half of them were given an additional heat treatment at 450 °C. The spectral absorbed-photon-to-collected-electron efficiency (ηAPCE) of the cells was determined as a function of the photoelectrode film thickness (d) and direction of illumination and analyzed in terms of electron injection (ηINJ) and collection (ηCOL) efficiency. The cells with pressed-only photoelectrodes gave significantly lower photocurrents yet their ηAPCE, and thus ηCOL, increased significantly with increasing d. To analyze this result quantitatively, methods were formulated based on the standard diffusion model of electron transport in nanostructured photoelectrodes for the factorization of experimental ηAPCE data into ηINJ and ηCOL parts and subsequent estimatio...

Validating Bayesian Inference Algorithms with Simulation-Based Calibration

Abstract: Verifying the correctness of Bayesian computation is challenging. This is especially true for complex models that are common in practice, as these require sophisticated model implementations and algorithms. In this paper we introduce \emph{simulation-based calibration} (SBC), a general procedure for validating inferences from Bayesian algorithms capable of generating posterior samples. This procedure not only identifies inaccurate computation and inconsistencies in model implementations but also provides graphical summaries that can indicate the nature of the problems that arise. We argue that SBC is a critical part of a robust Bayesian workflow, as well as being a useful tool for those developing computational algorithms and statistical software.

Automatic identification of bird species based on sinusoidal modeling of syllables

Abstract: Syllables are elementary building blocks of bird song. In the sounds of many songbirds, a large class of syllables can be approximated as amplitude and frequency varying brief sinusoidal pulses. We test how well bird species can be recognized by comparing simple sinusoidal representations of isolated syllables. Results are encouraging and show that, with limited sets of bird species, a recognizer based on this signal model may already be sufficient.