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This paper explores the relationship between speaker normalization and dialectal identity in sociolinguistic data, examining a database of vowel formants collected from 88 monolingual American English speakers in Michigan's Upper Peninsula. Audio recordings of Finnish- and Italian-heritage American English speakers reading a passage and a word list were normalized using two normalization procedures. These algorithms are based on different concepts of normalization: Lobanov, which models normalization as based on experience with individual talkers, and Labov ANAE, which models normalization as based on experience with scale-factors inherent in acoustic resonators of all kinds. The two procedures yielded different results; while the Labov ANAE method reveals a cluster shifting of low and back vowels that correlated with heritage, the Lobanov procedure seems to eliminate this sociolinguistic variation. The difference between the two procedures lies in how they treat relations between formant changes, suggesting that dimensions of variation in the vowel space may be treated differently by different normalization procedures, raising the question of how anatomical variation and dialectal variation interact in the real world. The structure of the sociolinguistic effects found with the Labov ANAE normalized data, but not in the Lobanov normalized data, suggest that the Lobanov normalization does over-normalize formant measures and remove sociolinguistically relevant information. [ABSTRACT FROM AUTHOR]

Atmospheric aerosol microphysical processes are a significant source of uncertainty in predicting climate change. Specifically, aerosol nucleation, emissions, and growth rates, which are simulated in chemical transport models to predict the particle size distribution, are not understood well. However, long-term size distribution measurements made at several ground-based sites across Europe implicitly contain information about the processes that created those size distributions. This work aims to extract that information by developing and applying an inverse technique to constrain aerosol emissions as well as nucleation and growth rates based on hourly size distribution measurements. We developed an inverse method based upon process control theory into an online estimation technique to scale aerosol emissions, growth, and nucleation so that the model-measurement bias in three measured aerosol properties exponentially decays. The properties, which are calculated from the measured and predicted size distributions, used to constrain aerosol nucleation, emission, and growth rates are the number of particles with diameter between 3 nm and 6 nm, the number with diameter greater than 10 nm, and the total dry volume of aerosol (N3-6, N10, Vdry), respectively. In this paper, we focus on developing and applying the estimation methodology in a zero-dimensional "box" model as a proof-of- concept before applying it to a three-dimensional simulation in subsequent work. The methodology is first tested on a dataset of synthetic and perfect measurements that span diverse environments in which the true particle emissions, growth, and nucleation rates are known. The inverse technique accurately estimates the aerosol microphysical process rates with an average and maximum error of 2 % and 13 %, respectively. Next, we investigate the effect that measurement noise has on the estimated rates. The method is robust to typical instrument noise in the aerosol properties as there is a negligible increase in bias of the estimated process rates. Finally, the methodology is applied to long-term datasets of in-situ size distribution measurements in Western Europe from May 2006 through June 2007. At Melpitz, Germany and Hyytiälä, Finland, the average diurnal profiles of estimated 3 nm particle formation rates are reasonable, having peaks near noon local time with average peak values of 1 and 0.15 cm(-3) s(-1), respectively. The normalized absolute error in estimated N3-6, N10, and Vdry at three European measurement sites is less than 15 %, showing that the estimation framework developed here has potential to decrease model-measurement bias while constraining uncertain aerosol microphysical processes. [ABSTRACT FROM AUTHOR]

In Finland, customer class load profiles are used extensively in distribution network calculation. State estimation systems, for example, use the load profiles to estimate the state of the network. Load profiles are also needed to predict future loads in distribution network planning. In general, customer class load profiles are obtained through sampling in load research projects. Currently, in Finland, customer classification is based on the uncertain customer information found in the customer information system. Customer information, such as customer type, heating solution, and tariff, is used to connect the customers with corresponding customer class load profiles. Now that the automatic meter-reading systems are becoming more common, customer classification and load profiling could be done according to actual consumption data. This paper proposes the use of the ISODATA algorithm for customer classification. The proposed customer classification and load profiling method also includes temperature dependency correction and outlier filtering. The method is demonstrated in this paper by studying a set of 660 hourly metered customers. [ABSTRACT FROM AUTHOR]

Mobile services differ from other services because of their temporal and spatial attributes. Mobile services additionally differ from each other in their value-added to the end-user. Some services—such as emailing and voice—are more business oriented. On the other hand, various free-time oriented services are provided in new smartphones, such as imaging and music playback. The present paper studies how mobile services are used in different contexts. For this, the paper develops a specialized algorithm that can be used with handset-based usage data acquired straight from end-users in an established panel study process. Educated guesses can be drawn on the user context based on the developed algorithm. In the present exercise usage contexts were divided into home, office and “on the move”. The algorithm is used with exemplary data from Finland and the UK covering 324 consumers in 2006. More than 70% of contextual use cases are correctly classified based on raw data. According to exemplary results particularly multimedia services are used “on the move”, whereas legacy mobile services experience more evenly distributed usage across all contexts. The algorithm that identifies context based on raw data provides a new angle to mobile end-user research. In the future, the accuracy of the algorithm will be improved with the integration of seamless cell-id logging and GPS data. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a method for classifying the severity of individual convective storms with real-time weather radar and lightning location data. The algorithm is based on a statistically initialized fuzzy logic model with human-oriented linguistic inference rules. When combined with an object-oriented convective storm tracking algorithm, the severity classification uses the past severity values in addition to the current state of the storm. Furthermore, the proposed method can be customized to correspond to the user-specific needs of different end user groups. The membership functions of the fuzzy logic model are initialized using the statistical analysis of various storm attributes derived from radar and lightning data, which potentially allows the adaptation of the model to different climates. The statistically initialized severity classification is also stable with respect to systematic errors in the measurements. To adjust the model to the Finnish climate, approximately 40 000 storms were tracked with a weather radar-based object-oriented convective storm tracking algorithm. The tracking was performed over a relatively large study area, with the composite of eight C-band weather radars, enabling also the analysis of relatively long-lived intense storms, such as mesoscale convective systems. In addition, the presented work illustrates the statistical characteristics of convective storms in Finland. The study also discusses the importance of careful data quality control when conducting a statistical analysis with an object-oriented storm tracking algorithm. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Maritime traffic in the Gulf of Finland has grown remarkably during the 2000s. This increase has an impact on die environment and exposes it to risks. These problems should be controlled to guarantee sustainable development and the welfare of inhabitants in die area. A method for estimating die impact of ship-originated air emissions on the environment is to calculate their environmental externalities which are a part of the total marginal social costs of shipping. The internalization of externalities as a control metiiod of transport would comply with the polluter pays principle and act as a fair traffic control metiiod between transport modes. In this paper, we present the results of C02, NOx, SOx and PM emissions originating from ships and tiieir externalities in die Gulf of Finland up to 2015. The calculation algorithm developed for this study produces emission estimates per annum and converts them into externalities. We focus on passenger, tanker, general cargo, Ro-Ro, container and bulk vessel ship types representing almost 90% of the total NOx emissions of shipping in the area. Scenario modelling is a method for estimating die effects of forthcoming or planned regulations and helps with targeting emission abatement actions to maximize tiieir profit. The results of the calculation algorithm show that externalities can be used as a consultative tool for transport-related decision-making. The costs are given at the price levels of the year 2000. The total external cost of ship-originated C02, NOx, SOx and PM emissions in the Gulf of Finland was almost €175 million in 2007. Due to increased traffic volumes, diese costs will increase to nearly €214 million in 2015. The majority of externalities are produced by C02 emissions. If we deduct C02 externalities from the results, we get total externalities of €57 million in 2007. Following eight years (2015), externalities would be 28% or €41 million lower. This would be as a result of regulation reducing the sulphur content of marine fuels. Regulating SOx and PM emissions will slow down the increasing trend of shipborne externalities in the Gulf of Finland; however, die externalities are still growing. In order to achieve a downward trend, die two major compounds resulting in externalities must be reduced, which requires strict actions to lower shipborne C02 and NOx emissions. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm to estimate the onset of seasonal snow-melt using space-borne microwave radiometer data. We have earlier developed a simple model called a Channel Difference Algorithm (CDA) to estimate the beginning of the snow-melt. The new algorithm, the SOM Detection Algorithm (SDA), is based on the use of an artificial neural network system a called Self-Organizing Map (SOM). The purpose of this research is to develop a robust and simple algorithm feasible for operative use. The algorithm is tested using SSM/I data with hydrological predictions as reference data. The reference data covers two winters, 1997 and 1998, and is for the boreal forest zone in Finland. The results are promising. The SDA is able to estimate the beginning of the final snow-melt well, especially if the snow water equivalent exhibits large values. Using low-pass filtering for the SDA estimated time series, the estimation can be improved. [ABSTRACT FROM AUTHOR]

• Customizing general frequent pattern mining algorithms to efficient Top-Rank-K ones. • Employing Dynamic Minimum Support Threshold Raising strategy to ensure efficiency. • Outperforming BTK algorithm with a 90% runtime improvement. • Experiments on real and synthetic datasets including Connect and Retail. Mining frequent patterns (FP) is an essential task in data mining. The parameter required for this task is typically the minimum support threshold. Tuning this parameter to a suitable value is a difficult task, especially for inexperienced users. Thus, the Top-Rank-K frequent patterns mining problem was introduced. It requires the user to input an easily-evaluated parameter, K , in order to obtain the set of all frequent patterns from the most frequent to the K th rank of frequency. In this paper, we customize three general Frequent Pattern Mining (FPM) algorithms, namely FIN, PrePost, and PrePost+, to develop specialized Top-Rank-K FP mining algorithms: TK_FIN, TK_PrePost, and TK_PrePost+. The Dynamic Minimum Support Raising strategy is applied on these algorithms to ensure efficiency. Experimentally, we evaluate the performance of these algorithms against an original, efficient, Top-Rank-K algorithm, BTK. The three presented algorithms perform 90% better than BTK in most of the experiments, with respect to runtime. Between the three Top-Rank-K FPM algorithms we present, TK_FIN achieves the best performance from both runtime and memory consumption perspectives. [ABSTRACT FROM AUTHOR]

• Linear loss factors penalize one HVDC line over the other. • Piecewise-linear loss factors better represent quadratic loss functions. • Piecewise-linear loss factors allow for a better distribution of power flows. • HVDC loss factors only disproportionately increase AC losses. • HVDC and AC loss factors lead to losses minimization. In the Nordic countries (Sweden, Norway, Finland and Denmark), many interconnectors are formed by long High-Voltage Direct-Current (HVDC) lines. Every year, the operation of such interconnectors costs millions of Euros to Transmission System Operators (TSOs) due to the high amount of losses that are not considered while clearing the market. To counteract this problem, Nordic TSOs (Svenska kraftnät - Sweden, Statnett - Norway, Fingrid - Finland, Energinet - Denmark) have proposed to introduce linear HVDC loss factors in the market clearing algorithm. The assessment of such a measure requires a detailed model of the system under investigation. In this paper we develop and introduce a detailed market model of the Nordic countries and we analyze the impact of different loss factor formulations. We show that linear loss factors penalize one HVDC line over the other, and this can jeopardize revenues of merchant HVDC lines. In this regard, we propose piecewise-linear loss factors: a simple to implement but highly effective solution. Moreover, we demonstrate how the introduction of only HVDC loss factors is a partial solution, since it disproportionately increases the AC losses. Our results show that the inclusion of AC loss factors can eliminate this problem. [ABSTRACT FROM AUTHOR]