Analysis of Homophony with Higher Order Markov Chains

The purpose of diploma paper is the development of application for the analysis of polyphonic musical pieces (especially from the view of vertical time progress of voices or homophony), together with the suitable theoretical study of the field in question. Beside the basic characteristics of sound simple harmonic motion and within the framework of consonance also aliquote tones and scales are described. All this is the source of motivation for the detailed study of consonance and more possible views of homophony. Music information retrieval is a strong tool for quantitative study of music. A few possible approaches are mentioned; however, mathematical tool of Markov chains which is a good starting point for the study of homophonic compositions is put out especially. The procedure which is concentrated on the use of higher order Markov chains and is used for the analysis is also described. This is what enables the consideration for history in musical progress and, consequently, a wider view. The elaborated application with the described method analyzes the composition written in MusicXML format. In this procedure the analysis takes suitable theoretical background into consideration. In this way the application is an effective resource in different musicological approaches to the study of homophony. Analysis, together with commented results, is made on the example of a concrete music piece. In the end some more possible starting points for further work are stated

Road surface condition forecasting from historical data and weather forecasts

The dissertation presents a new method for construction of hierarchical regression models by combining the predetermined groups of data based on the knowledge of compatibility and/or similarity of models. Regression models are widely used in many areas. The simplest, linear regression models are often chosen because of their robustness. In this work we consider a case in which the data can be split into multiple subsets on which the predicted outcome is linear. While such data cannot be modelled with a single linear model, inducing a separate linear model for each subset would yield inaccurate models due to small samples. This problem can be nicely framed in the theory of bias-variance decomposition of error. A single model would have an unacceptably high bias, while separate models would have high variances. Based on this theoretical justification, we developed a method for hierarchical construction of linear models, which starts with separate models for each subgroup and then iteratively merges them until the effect of decreased variance due to larger data samples available for each model begins to be overcome by the increased bias due to treating a non-linear relation as linear. We tested the method on controlled synthetic data, which proved the correctness of our approach. The method was then tested on the data on road meteorology: we were able to successfully predict the road surface temperature for several hours ahead. This result is interesting for the field of road meteorology as it shows that it is possible to construct models with good forecasting accuracy with statistical methods alone. The advantage of such modelling compared to physical models based on energy balance equation is that they do not require any knowledge about the road construction properties. Their weakness is that they require past data from road weather station at a particular location. To overcome this problem, we investigated a problem in which the data for several locations is available and the task is to find a predictive model for a new location based on the known physical, but easily obtainable similarities between this and other locations, such as sky visibility and similar. We first checked that the attributes that we chose to describe locations are indeed correlated with coefficients from regression models. Based on positive findings of this study, we defined a modelling technique that can construct a linear model for the location based on linear models for other locations. We again first empirically tested the method on synthetic data constructed in such a way that it fulfills the assumptions of the method, and then on the actual data from the road weather stations. As expected, the accuracy of such models is below those constructed from the actual data, yet still quite in the acceptable range for its potential practical use. Results of the dissertation may find practical use. Forecasts of road weather conditions are a valuable resource for drivers, as well as for road maintenance services, in particular in winter. More accurate forecasts can provide safer roads while cutting down the maintenance costs and minimizing the environmental damage from over-salting

Decision support system for winter maintenance of the motorways in Slovenia

Weather has a significant impact on road safety, as evidenced by the many events of the past winters in Slovenia. Weather information systems provide important support to drivers and road maintenance services, increase road safety and reduce the cost of winter maintenance."br" In winter 2015/2016, the Slovenian Motorway Company’s Road Weather Information System (RWIS DARS) was upgraded with additional relevant functionalities that promoted the existing RWIS into the Maintenance Decision Support System (MDSS). MDSS is a tool that utilizes weather forecasts and observations to assist managers in making appropriate decisions to best utilize resources when planning for and treating snow and ice."br" A thermal mapping of the road network was conducted on Slovenian motorways. The thermal mapping data were primarily used in calculating the high-resolution route-based forecasts of road temperature and road conditions throughout the motorway network. On the basis of these forecasts, the system proposes treatments for the implementation of winter service (i.e. ploughing/gritting, gritting materials, time and location) and anticipates their impacts according to the forecasted road temperature and road conditions."br" DARS MDSS, which will be presented in the paper proposed, is developed on the latest technologies and it incorporates the knowledge and experience of the profession and users

Integrating nowcasting with crisis management and risk prevention in a transnational and interdisciplinary framework

This paper presents the recent WWRP/WMO Forecast Demonstration Project INCA-CE (INtegrating nowCAsting for Central Europe) co-funded by the European Union. Twenty-four partners of national and regional hydro-meteorological services, national and regional crisis and disaster management centers, and authorities for road management world-wide have participated in INCA-CE for international cooperation on nowcasting development, interdisciplinary cooperation for nowcasting applications and transnational cooperation for nowcasting services. INCA-CE has implemented the nowcasting system INCA at the project partner countries, applied INCA nowcasting in civil protection, operational hydrology and road safety, and improved the INCA system based on the end user's requirements. The main difference to other similar projects is that end user's involvement and the improvements involve the whole end user value chain. The project has developed several ideas for end users on how to interpret nowcasting products (INCA-SWING) and on how to deal with the nowcasting products in their working practice (INCA-MCPEX and ISW). INCA-CE is also oriented strongly to transnational cooperation in nowcasting development and implementation, in easy access to a homogenized set of nowcasting products from those INCA providers to end users in the region, and in the transnational use of real-time products by end users in cases of high impact weather across borders