Methods for the analysis of two dimensional measurement data of paper web

This work is devoted to the problem of reconstructing the basis weight structure at paper web with black{box techniques. The data that is analyzed comes from a real paper machine and is collected by an o®-line scanner. The principal mathematical tool used in this work is Autoregressive Moving Average (ARMA) modelling. When coupled with the Discrete Fourier Transform (DFT), it gives a very flexible and interesting tool for analyzing properties of the paper web. Both ARMA and DFT are independently used to represent the given signal in a simplified version of our algorithm, but the final goal is to combine the two together. Ljung-Box Q-statistic lack-of-fit test combined with the Root Mean Squared Error coefficient gives a tool to separate significant signals from noise.

Quantile Estimation in Non-Stationary Streaming Data

We provide an incremental quantile estimator for Non-stationary Streaming Data. We propose a method for simultaneous estimation of multiple quantiles corresponding to the given probability levels from streaming data. Due to the limitations of the memory, it is not feasible to compute the quantiles by storing the data. So estimating the quantiles as the data pass by is the only possibility. This can be effective in network measurement. To provide the minimum of the mean-squared error of the estimation, we use parabolic approximation and for comparison we simulate the results for different number of runs and using both linear and parabolic approximations.

A novel approachfor assessing the fatigue strength of ultrasonic impact treated welded structures.

It is commonly observed that complex fabricated structures subject tofatigue loading fail at the welded joints. Some problems can be corrected by proper detail design but fatigue performance can also be improved using post-weld improvement methods. In general, improvement methods can be divided into two main groups: weld geometry modification methods and residual stress modification methods. The former remove weld toe defects and/or reduce the stress concentrationwhile the latter introduce compressive stress fields in the area where fatigue cracks are likely to initiate. Ultrasonic impact treatment (UIT) is a novel post-weld treatment method that influences both the residual stress distribution andimproves the local geometry of the weld. The structural fatigue strength of non-load carrying attachments in the as-welded condition has been experimentally compared to the structural fatigue strength of ultrasonic impact treated welds. Longitudinal attachment specimens made of two thicknesses of steel S355 J0 have been tested for determining the efficiency of ultrasonic impacttreatment. Treated welds were found to have about 50% greater structural fatigue strength, when the slope of the S-N-curve is three. High mean stress fatigue testing based on the Ohta-method decreased the degree of weld improvement only 19%. This indicated that the method could be also applied for large fabricated structures operating under high reactive residual stresses equilibrated within the volume of the structure. The thickness of specimens has no significant effect tothe structural fatigue strength. The fatigue class difference between 5 mm and 8 mm specimen was only 8%. It was hypothesized that the UIT method added a significant crack initiation period to the total fatigue life of the welded joints. Crack initiation life was estimated by a local strain approach. Material parameters were defined using a modified Uniform Material Law developed in Germany. Finite element analysis and X-ray diffraction were used to define, respectively, the stress concentration and mean stress. The theoretical fatigue life was found to have good accuracy comparing to experimental fatigue tests.The predictive behaviour of the local strain approach combined with the uniformmaterial law was excellent for the joint types and conditions studied in this work.

The Anchoring Bias of Investors and the 52-Week High Momentum Strategy in the OMX Helsinki Stock Exchange: Psychological Approach to Market Efficiency

The behavioural finance literature expects systematic and significant deviations from efficiency to persist in securities markets due to behavioural and cognitive biases of investors. These behavioural models attempt to explain the coexistence of intermediate-term momentum and long-term reversals in stock returns based on the systematic violations of rational behaviour of investors. The study investigates the anchoring bias of investors and the profitability of the 52-week momentum strategy (GH henceforward). The relatively highly volatile OMX Helsinki stock exchange is a suitable market for examining the momentum effect, since international investors tend to realise their positions first from the furthest security markets by the time of market turbulence. Empirical data is collected from Thomson Reuters Datastream and the OMX Nordic website. The objective of the study is to provide a throughout research by formulating a self-financing GH momentum portfolio. First, the seasonality of the strategy is examined by taking the January effect into account and researching abnormal returns in long-term. The results indicate that the GH strategy is subject to significantly negative revenues in January, but the strategy is not prone to reversals in long-term. Then the predictive proxies of momentum returns are investigated in terms of acquisition prices and 52-week high statistics as anchors. The results show that the acquisition prices do not have explanatory power over the GH strategy’s abnormal returns. Finally, the efficacy of the GH strategy is examined after taking transaction costs into account, finding that the robust abnormal returns remain statistically significant despite the transaction costs. As a conclusion, the relative distance between a stock’s current price and its 52-week high statistic explains the profits of momentum investing to a high degree. The results indicate that intermediateterm momentum and long-term reversals are separate phenomena. This presents a challenge to current behavioural theories, which model these aspects of stock returns as subsequent components of how securities markets respond to relevant information.