Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Effect of Earnings Volatility on Cost of Debt: The case of Swedish Limited Companies

The paper empirically tests the relationship between earnings volatility and cost of debt with a sample of more than 77,000 Swedish limited companies over the period 2006 to 2013 observing more than 677,000 firm years. As called upon by many researchers recently that there is very limited evidence of the association between earnings volatility and cost of debt this paper contributes greatly to the existing literature of earnings quality and debt contracts, especially on the consequence of earnings quality in the debt market. Earnings volatility is a proxy used for earnings quality while cost of debt is a component of debt contract. After controlling for firms’ profitability, liquidity, solvency, cashflow volatility, accruals volatility, sales volatility, business risk, financial risk and size this paper studies the effect of earnings volatility measured by standard deviation of Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA) on Cost of Debt. Overall finding suggests that lenders in Sweden does take earnings volatility into consideration while determining cost of debt for borrowers. But a deeper analysis of various industries suggest earnings volatility is not consistently used by lenders across all the industries. Lenders in Sweden are rather more sensitive to borrowers’ financial risk across all the industries. It may also be stated that larger borrowers tend to secure loans at a lower interest rate, the results are consistent with majority of the industries. Swedish debt market appears to be well prepared for financial crises as the debt crisis seems to have no or little adverse effect borrowers’ cost of capital. This study is the only empirical evidence to study the association between earnings volatility and cost of debt. Prior indirect research suggests earnings volatility has a negative effect on cost debt (i.e. an increase in earnings volatility will increase firm’s cost of debt). Our direct evidence from the Swedish debt market is consistent for some industries including media, real estate activities, transportation & warehousing, and other consumer services.