Numerical simulation of ambient seismic wavefield modification caused by pore-fluid effects in an oil reservoir

We have modeled numerically the seismic response of a poroelastic inclusion with properties applicable to an oil reservoir that interacts with an ambient wavefield. The model includes wave-induced fluid flow caused by pressure differences between mesoscopic-scale (i.e., in the order of centimeters to meters) heterogeneities. We used a viscoelastic approximation on the macroscopic scale to implement the attenuation and dispersion resulting from this mesoscopic-scale theory in numerical simulations of wave propagation on the kilometer scale. This upscaling method includes finite-element modeling of wave-induced fluid flow to determine effective seismic properties of the poroelastic media, such as attenuation of P- and S-waves. The fitted, equivalent, viscoelastic behavior is implemented in finite-difference wave propagation simulations. With this two-stage process, we model numerically the quasi-poroelastic wave-propagation on the kilometer scale and study the impact of fluid properties and fluid saturation on the modeled seismic amplitudes. In particular, we addressed the question of whether poroelastic effects within an oil reservoir may be a plausible explanation for low-frequency ambient wavefield modifications observed at oil fields in recent years. Our results indicate that ambient wavefield modification is expected to occur for oil reservoirs exhibiting high attenuation. Whether or not such modifications can be detected in surface recordings, however, will depend on acquisition design and noise mitigation processing as well as site-specific conditions, such as the geologic complexity of the subsurface, the nature of the ambient wavefield, and the amount of surface noise.

Development of a Traffic Control Devices and Pavement Markings Manual for Iowa's Cities and Counties with a Survery of Common Practices, April 2001

Transportation agencies in Iowa are responsible for a significant public investment with the installation and maintenance of traffic control devices and pavement markings. Included in this investment are thousands of signs and other inventory items, equipment, facilities, and staff. The proper application of traffic control devices and pavement markings is critical to public safety on streets and highways, and local governments have a prescribed responsibility under the Code of Iowa to properly manage these assets. This research report addresses current traffic control and pavement marking application, maintenance, and management in Iowa.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.

Evaluating the Relationship between the Driver and Roadway to Address Rural Intersection Safety using the SHRP 2 Naturalistic Driving Study Data, RB05-013, 2016

Rural intersections account for 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Transportation agencies have traditionally implemented countermeasures to address rural intersection crashes but frequently do not understand the dynamic interaction between the driver and roadway and the driver factors leading to these types of crashes. The Second Strategic Highway Research Program (SHRP 2) conducted a large-scale naturalistic driving study (NDS) using instrumented vehicles. The study has provided a significant amount of on-road driving data for a range of drivers. The present study utilizes the SHRP 2 NDS data as well as SHRP 2 Roadway Information Database (RID) data to observe driver behavior at rural intersections first hand using video, vehicle kinematics, and roadway data to determine how roadway, driver, environmental, and vehicle factors interact to affect driver safety at rural intersections. A model of driver braking behavior was developed using a dataset of vehicle activity traces for several rural stop-controlled intersections. The model was developed using the point at which a driver reacts to the upcoming intersection by initiating braking as its dependent variable, with the driver’s age, type and direction of turning movement, and countermeasure presence as independent variables. Countermeasures such as on-pavement signing and overhead flashing beacons were found to increase the braking point distance, a finding that provides insight into the countermeasures’ effect on safety at rural intersections. The results of this model can lead to better roadway design, more informed selection of traffic control and countermeasures, and targeted information that can inform policy decisions. Additionally, a model of gap acceptance was attempted but was ultimately not developed due to the small size of the dataset. However, a protocol for data reduction for a gap acceptance model was determined. This protocol can be utilized in future studies to develop a gap acceptance model that would provide additional insight into the roadway, vehicle, environmental, and driver factors that play a role in whether a driver accepts or rejects a gap.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Characteristics of 2D convective structures in Catalonia (NE Spain): an analysis using radar data and GIS

Flood simulation studies use spatial-temporal rainfall data input into distributed hydrological models. A correct description of rainfall in space and in time contributes to improvements on hydrological modelling and design. This work is focused on the analysis of 2-D convective structures (rain cells), whose contribution is especially significant in most flood events. The objective of this paper is to provide statistical descriptors and distribution functions for convective structure characteristics of precipitation systems producing floods in Catalonia (NE Spain). To achieve this purpose heavy rainfall events recorded between 1996 and 2000 have been analysed. By means of weather radar, and applying 2-D radar algorithms a distinction between convective and stratiform precipitation is made. These data are introduced and analyzed with a GIS. In a first step different groups of connected pixels with convective precipitation are identified. Only convective structures with an area greater than 32 km2 are selected. Then, geometric characteristics (area, perimeter, orientation and dimensions of the ellipse), and rainfall statistics (maximum, mean, minimum, range, standard deviation, and sum) of these structures are obtained and stored in a database. Finally, descriptive statistics for selected characteristics are calculated and statistical distributions are fitted to the observed frequency distributions. Statistical analyses reveal that the Generalized Pareto distribution for the area and the Generalized Extreme Value distribution for the perimeter, dimensions, orientation and mean areal precipitation are the statistical distributions that best fit the observed ones of these parameters. The statistical descriptors and the probability distribution functions obtained are of direct use as an input in spatial rainfall generators.

Visual Measurement and Modelling of Tool Wear in Rough Turning

Huolimatta korkeasta automaatioasteesta sorvausteollisuudessa, muutama keskeinen ongelma estää sorvauksen täydellisen automatisoinnin. Yksi näistä ongelmista on työkalun kuluminen. Tämä työ keskittyy toteuttamaan automaattisen järjestelmän kulumisen, erityisesti viistekulumisen, mittaukseen konenäön avulla. Kulumisen mittausjärjestelmä poistaa manuaalisen mittauksen tarpeen ja minimoi ajan, joka käytetään työkalun kulumisen mittaukseen. Mittauksen lisäksi tutkitaan kulumisen mallinnusta sekä ennustamista. Automaattinen mittausjärjestelmä sijoitettiin sorvin sisälle ja järjestelmä integroitiin onnistuneesti ulkopuolisten järjestelmien kanssa. Tehdyt kokeet osoittivat, että mittausjärjestelmä kykenee mittaamaan työkalun kulumisen järjestelmän oikeassa ympäristössä. Mittausjärjestelmä pystyy myös kestämään häiriöitä, jotka ovat konenäköjärjestelmille yleisiä. Työkalun kulumista mallinnusta tutkittiin useilla eri menetelmillä. Näihin kuuluivat muiden muassa neuroverkot ja tukivektoriregressio. Kokeet osoittivat, että tutkitut mallit pystyivät ennustamaan työkalun kulumisasteen käytetyn ajan perusteella. Parhaan tuloksen antoivat neuroverkot Bayesiläisellä regularisoinnilla.

A System-Level Simulation Model for a Protocol Processor

As the development of integrated circuit technology continues to follow Moore’s law the complexity of circuits increases exponentially. Traditional hardware description languages such as VHDL and Verilog are no longer powerful enough to cope with this level of complexity and do not provide facilities for hardware/software codesign. Languages such as SystemC are intended to solve these problems by combining the powerful expression of high level programming languages and hardware oriented facilities of hardware description languages. To fully replace older languages in the desing flow of digital systems SystemC should also be synthesizable. The devices required by modern high speed networks often share the same tight constraints for e.g. size, power consumption and price with embedded systems but have also very demanding real time and quality of service requirements that are difficult to satisfy with general purpose processors. Dedicated hardware blocks of an application specific instruction set processor are one way to combine fast processing speed, energy efficiency, flexibility and relatively low time-to-market. Common features can be identified in the network processing domain making it possible to develop specialized but configurable processor architectures. One such architecture is the TACO which is based on transport triggered architecture. The architecture offers a high degree of parallelism and modularity and greatly simplified instruction decoding. For this M.Sc.(Tech) thesis, a simulation environment for the TACO architecture was developed with SystemC 2.2 using an old version written with SystemC 1.0 as a starting point. The environment enables rapid design space exploration by providing facilities for hw/sw codesign and simulation and an extendable library of automatically configured reusable hardware blocks. Other topics that are covered are the differences between SystemC 1.0 and 2.2 from the viewpoint of hardware modeling, and compilation of a SystemC model into synthesizable VHDL with Celoxica Agility SystemC Compiler. A simulation model for a processor for TCP/IP packet validation was designed and tested as a test case for the environment.

Assessment of Coastal Bird Populations and Habitats on the Finnish Coast of the Baltic Sea: Implications for Monitoring and Management

Coastal birds are an integral part of coastal ecosystems, which nowadays are subject to severe environmental pressures. Effective measures for the management and conservation of seabirds and their habitats call for insight into their population processes and the factors affecting their distribution and abundance. Central to national and international management and conservation measures is the availability of accurate data and information on bird populations, as well as on environmental trends and on measures taken to solve environmental problems. In this thesis I address different aspects of the occurrence, abundance, population trends and breeding success of waterbirds breeding on the Finnish coast of the Baltic Sea, and discuss the implications of the results for seabird monitoring, management and conservation. In addition, I assess the position and prospects of coastal bird monitoring data, in the processing and dissemination of biodiversity data and information in accordance with the Convention on Biological Diversity (CBD) and other national and international commitments. I show that important factors for seabird habitat selection are island area and elevation, water depth, shore openness, and the composition of island cover habitats. Habitat preferences are species-specific, with certain similarities within species groups. The occurrence of the colonial Arctic Tern (Sterna paradisaea) is partly affected by different habitat characteristics than its abundance. Using long-term bird monitoring data, I show that eutrophication and winter severity have reduced the populations of several Finnish seabird species. A major demographic factor through which environmental changes influence bird populations is breeding success. Breeding success can function as a more rapid indicator of sublethal environmental impacts than population trends, particularly for long-lived and slowbreeding species, and should therefore be included in coastal bird monitoring schemes. Among my target species, local breeding success can be shown to affect the populations of the Mallard (Anas platyrhynchos), the Eider (Somateria mollissima) and the Goosander (Mergus merganser) after a time lag corresponding to their species-specific recruitment age. For some of the target species, the number of individuals in late summer can be used as an easier and more cost-effective indicator of breeding success than brood counts. My results highlight that the interpretation and application of habitat and population studies require solid background knowledge of the ecology of the target species. In addition, the special characteristics of coastal birds, their habitats, and coastal bird monitoring data have to be considered in the assessment of their distribution and population trends. According to the results, the relationships between the occurrence, abundance and population trends of coastal birds and environmental factors can be quantitatively assessed using multivariate modelling and model selection. Spatial data sets widely available in Finland can be utilised in the calculation of several variables that are relevant to the habitat selection of Finnish coastal species. Concerning some habitat characteristics field work is still required, due to a lack of remotely sensed data or the low resolution of readily available data in relation to the fine scale of the habitat patches in the archipelago. While long-term data sets exist for water quality and weather, the lack of data concerning for instance the food resources of birds hampers more detailed studies of environmental effects on bird populations. Intensive studies of coastal bird species in different archipelago areas should be encouraged. The provision and free delivery of high-quality coastal data concerning bird populations and their habitats would greatly increase the capability of ecological modelling, as well as the management and conservation of coastal environments and communities. International initiatives that promote open spatial data infrastructures and sharing are therefore highly regarded. To function effectively, international information networks, such as the biodiversity Clearing House Mechanism (CHM) under the CBD, need to be rooted at regional and local levels. Attention should also be paid to the processing of data for higher levels of the information hierarchy, so that data are synthesized and developed into high-quality knowledge applicable to management and conservation.

Financial and Environmental Impacts of a Dry Port to Facilitate Competitiveness of Two Major Transit Seaports of Finland

Environmental problems and issues have received more and more attention during the last decades. Reasons for this are different increased external costs such as congestion, CO2 emission, noise and accident costs. Transportation sector is the only sector with increasing external costs. The EU will increase its attention in decreasing the external costs of transport. Aim of this research was to find out if a dry port solution could decrease costs of transport, especially external costs. Dry port concept is an intermodal transport system, where inland transport between port and dry port is performed by rail transport instead of traditional road transport. In addition, dry ports offer similar services as ports. Research is conducted by performing a literature review about dry port concept and costs of transport, especially external costs of transport. Financial and environmental impacts of the dry port concept are studied by comparing costs of road and rail transport by cost accounting and with a simulation model. Location of dry port is researched with gravitational models. Results of the literature review are that rail transport is environmentally friendlier mode of transport than road transport. Cost model and simulation model show that if only costs of freight movement are considered, rail transport is more inexpensive transport mode than road transport in terms of internal and external costs. Because of that dry port concept could decrease costs of transport, especially external costs. Results of gravitational models are that city of Kouvola is in a good position to be a dry port. Russian transit traffic through Finland improves location of Kouvola to be a dry port.

Traffic flow development at North European company level with respect of China and Russia

The aim of the thesis is to analyze traffic flows and its development from North European companies` point of view to China and Russia using data from logistics questionnaire. Selected North European companies are large Finnish and Swedish companies. The questionnaire was sent via email to the target group. The study is based on the answers got from respondent companies from years 2006, 2009 and 2010. In the thesis Finnish Talouselämä newspaper and Swedish Affärsdata are used as a database to find the target companies for the survey. Respondents were most often logistics managers in companies. In the beginning of the thesis concepts of transportation logistics is presented, including container types, trade terms, axel loads in roads and in railways. Also there is information about warehousing types and terminals. After that, general information of Chinese and Russian transportation logistics is presented. Chinese and Russian issues are discussed in two sections. In both of them it is analyzed economic development, freight transport and trade balance. Some practical examples of factory inaugurations in China and Russia are presented that Finnish and Swedish companies have completed. In freight transport section different transportation modes, logistics outsourcing and problems of transportation logistics is discussed. The results of the thesis show that transportation flows between Europe and China is changing. Freight traffic from China to European countries will strengthen even more from the current base. When it comes to Russia and Europe, traffic flows seem to be changing from eastbound traffic to westbound traffic. It means that in the future it is expected more freight traffic from Russia to Europe. Some probable reasons for that are recent factory establishments in Russia and company interviews support also this observation. Effects of the economic recession are mainly seen in the lower transportation amounts in 2009.