Jukka-Pekka Skön

Neural Networks for the Prediction of Species-Specific Plot Volumes Using Airborne Laser Scanning and Aerial Photographs

Parametric and nonparametric modeling methods have been widely used for the estimation of forest attributes from airborne laser-scanning data and aerial photographs. However, the methods adopted suffered from complex remote-sensed data structures involving high dimensions, nonlinear relationships, different statistical distributions, and outliers. In this context, artificial neural networks (ANNs) are of interest as they have many clear benefits over conventional modeling methods and could then enhance the accuracy of current forest-inventory methods. This paper examines the ability of common ANN modeling techniques for the prediction of species-specific forest attributes, as exemplified here with the prediction stem volumes (cubic meters per hectare) at the field plot and forest stand levels. Three modeling methods were evaluated, namely, the multilayer perceptron (MLP), support vector regression (SVR), and self-organizing map, and intercompared with the corresponding nonparametric k most similar neighbor method using cross-validated statistical performance indexes. To decrease the number of model-input variables, a multiobjective input-selection method based on genetic algorithm is adopted. The numerical results obtained in the study suggest that ANNs are appropriate and accurate methods for the assessment of species-specific forest attributes, which can be used as alternatives to multivariate linear regression and nonparametric nearest neighbor models. Among the ANN models, SVR and MLP provide the best choices for prediction purposes as they yielded high prediction accuracies for species-specific tree volumes throughout.

Energy consumption and air quality monitoring system

Reducing energy consumption, cutting greenhouse gas emissions and elimination energy wastage are among the main goals in the European Union (EU). The building sector is the largest user of energy and CO2 emitter in the EU, estimated at approximately 40% of the total consumption. However, the question is, how improving energy efficiency in buildings affects on indoor air quality? We have started to research effects of energy efficiency construction on indoor air quality. This paper describes a basic idea of energy consumption and indoor air quality monitoring system developed as a part of the Finnish AsTEKa-project. The first prototype of this monitoring system was presented in the Kuopio housing fair 2010.

Characterisation of the chemical and biological properties of molecules with QSAR/QSPR and chemical grouping, and its application to a group of alkyl ethers

This study presents a QSAR/QSPR modelling and chemical grouping (read-across) approach to provide information on the biological properties of a group of aliphatic ethers, with accurate biological predictions restricted to those physico-chemical and (eco)toxicological properties where the performance of QSAR/QSPR has been shown to be acceptable. The mathematical methods used ranged from multivariate regression models to PLS (partial least-squares), SVM (support vector machines) and Sammons mapping. A novel grouping approach, based on a set of key descriptors, has been proposed to give a compact picture of the structural and biological properties of the compounds, and to provide a more mechanistic basis for the interpretations of chemical groups. Besides being a straightforward case study, the paper also exemplifies the capabilities and limitations of the methods in predictive toxicology on a more general level.

System architecture of customized intelligent lighting control and indoor environment monitoring system for persons with mild cognitive impairment or dementia

A customized intelligent lighting control combined with an indoor environment monitoring system is presented as a novel system architecture for the help of elderly, especially for people with dementia. Bluish light, which affects human circadian rhythm, is the key element of this study aiming to find ways to enhance patient wellbeing and reduce nursing workload. Moreover, thermal comfort of occupants is monitored and discussed.