506 research outputs found
Susceptibility to malaria with a focus on the postpartum period
No full textMalaria in het kraambed is een afspiegeling van het succes waarmee malaria tijdens de zwangerschap is behandeld. Aan de Thais-Birmese grens is een zwangerschapscontrole opgezet met wekelijkse screening voor malaria. Dit heeft de afgelopen 25 jaar geleid tot een enorme afname in moedersterfte. Machteld Boel deed nader onderzoek naar deze screening. Binnen dit system wordt falciparum malaria effectief behandeld, maar de vivax malaria parasieten die in de lever blijven zitten zijn in de zwangerschap niet te behandelen, wat leidt tot een verhoogd risico op vivax in het kraambed. Ook tijdens de bevalling brengt malaria risico’s met zich mee voor moeder en kind. Boel stelt dat de preventie en behandeling van malaria tijdens de zwangerschap wereldwijd moet worden aangescherpt
Sailing rock art boats: a reassessment of seafaring abilities in bronze age Scandinavia and the introduction of the sail in the north
No full textThis thesis examines the basis for the current belief that the introduction of the sail in the North occurred between the 7th or 8th and the 10th centuries AD, almost a thousand years later than on the British Islands and almost 3000 years later than in the Mediterranean. The foundations for this reassessment of the potential timing and development in the use of the sail derives mainly from an examination of the Bronze Age rock art (1800–500 BC) in southern Scandinavia containing imagery of boats with attributes that can be interpreted as masts and sails. In combination with experimental sail trials in Bronze Age type boats and by comparing this material to available research on the transition from paddling to sailing in ancient Egypt and Oceania, the author argues that the use of sail as a complement to paddling would have formed an integral part of the formation of centres of power in Scandinavia in the early Bronze Age. This in turn would have permitted more frequent communication, helping to expand, maintain and control power. The transitions from downwind sailing abilities to abilities to sail within a wider range might have occurred relatively swiftly between c. 1550–1300 BC, and might be directly linked to the expansion of Scandinavian centres of power during the same period, allowing for increased flexibility, both in terms of manpower, range and choice of routes with the use of a wider range of weather conditions. The emergence of the sail would primarily have been driven by increased needs for the movement of people and goods across short medium- and long distances – a process where competition by rivalling chiefs might also have played part. Furthermore, it is here suggested that the developments in sail and boat technology in the North were the result of incremental and gradual changes that eventually resulted in the boats and sails as used in the Viking Era
A compositional stochastic model for real-time freeway traffic simulation.
No full textTraffic flow on freeways is a non-linear, many-particle phenomenon, with complex interactions between vehicles. This paper presents a stochastic model of freeway traffic at a time scale and of a level of detail suitable for on-line estimation, routing and ramp metering control. The freeway is considered as a network of interconnected components, corresponding to one-way road links consisting of consecutively connected short sections (cells). The compositional model proposed here extends the Daganzo cell transmission model by defining sending and receiving functions explicitly as random variables, and by also specifying the dynamics of the average speed in each cell. Simple stochastic equations describing the macroscopic traffic behavior of each cell, as well as its interaction with neighboring cells are obtained. This will allow the simulation of quite large road networks by composing many links. The model is validated over synthetic data with abrupt changes in the number of lanes and over real traffic data sets collected from a Belgian freeway
An unscented Kalman filter for freeway traffic estimation
No full textThis paper addresses the problem of freeway tra±c flow estimation. The freeway is considered as a network of components representing different freeway stretches called segments. The evolution of the traffic in a segment is modelled as a dynamic stochastic system, influenced by states of neighbour segments. Measurements are received only at boundaries between some segments and averaged within regular time intervals. An Unscented Kalman filter is developed and its performance is compared with a particle filter both for synthetic data and for real traffc data. The intended application is to supply traffc control systems with the estimated traffc state
An interval compositional vehicular traffic model for real-time applications
No full textThis paper proposes an interval approach to vehicular traffic flow modeling. The developed interval compositional model (ICM) provides a natural way of predicting traffic flows without the assumption of uniform distribution of vehicles along the road. The model can be used for real-time prediction of traffic flows and can be part of road traffic surveillance and control systems. The approach is flexible and robust and can be used in real-time applications. Its performance is investigated and validated over real traffic data
A particle filter for freeway traffic estimation
No full textThis paper considers the traffic flow estimation problem for the purposes of on-line traffic prediction, mode detection and ramp-metering control. The solution to the estimation problem is given within the Bayesian recursive framework. A particle filter (PF) is developed based on a freeway traffic model with aggregated states and an observation model with aggregated variables. The freeway is considered as a network of components, each component representing a different section of the traffic network. The freeway traffic is modelled as a stochastic hybrid system, i.e. each traffic section possesses continuous and discrete states, interacting with states of neighbor sections. The state update step in the recursive Bayesian estimator is performed through sending and receiving functions describing propagation of perturbations from upstream to downstream, and from downstream to upstream sections. Measurements are received only on boundaries between some sections and averaged within regular or irregular time intervals. A particle filter is developed with measurement updates each time when a new measurement becomes available, and with possibly many state updates in between consecutive measurement updates. It provides an approximate but scalable solution to the difficult state estimation and prediction problem with limited, noisy observations. The filter performance is validated and evaluated by Monte Carlo simulation
Parallelized particle filtering for freeway traffic state tracking
No full textWe consider parallelized particle filters for state tracking (estimation) of freeway traffic networks. Particle filters can accurately solve the state estimation problem for general nonlinear systems with non-Gaussian noises. However, this high accuracy may come at the cost of high computational demand. We present two parallelized particle filtering algorithms where the calculations are divided over several processing units (PUs) which reduces the computational demand per processing unit. Existing parallelization approaches typically assign sets of particles to PUs such that each full particle resides at one PU. In contrast, we partition each particle according to a partitioning of the network into subnetworks based on the topology of the network. The centralized case and the two proposed approaches are evaluated with a benchmark problem by comparing the estimation accuracy, computational complexity and communication needs. This approach is in general applicable to systems where it is possible to partition the overall state into subsets of states, such that most of the interaction takes place within the subsets. Keywords: Parallel particle filters, freeway traffic state tracking
Distributed collision avoidance for autonomous vehicles: world automata representation
No full textThe automatic control of interacting autonomous vehicles (AVs) is one of the problems that engineers are currently trying to solve. The present paper deals with the design of local control laws governing the movement and collision avoidance of such groups of AVs, enforcing the safeness of the operations as well as task completions. This problem is inspired by the automation of a container terminal where each AV executes tasks assigned by a supervisor. A task involves moving an AV from an assigned origin to an assigned destination by a given deadline. The constraints imposed by the bounded workspace (a long and narrow quay in the container terminal example), the deadlines assigned to each task, and the uncertainty in the detection and communication make the problem difficult to solve in a centralized way. Therefore a distributed control approach is preferred with a local control agent in each AV adjusting its trajectory, so that its task is completed without collisions. By applying a fixed set of priority rules the computational complexity for each agent is reduced compared to the centralized case. Whenever an AV detects a possible conflict, i.e. the estimated position of another AV within the detection range, it must adjust its own speed and trajectory in order to avoid a future collision, reducing the number of cases where a supervisor has to intervene in order to resolve conflicts that degenerate in dead-locks. The modelling and validation of the system is performed by using the world automata theory
Discrete Event Systems, Analysis and Control (editors: R. Boel and G. Stremersch) Kluwer Academic Press, Boston, 2000, 484 p.
No full textParallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems
No full textLarge scale traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, 4) cope with multimodal conditional probability density functions for the states. Often centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques able to cope with these problems of large traffic network systems. These are Parallelized Particle Filters (PPFs) and a Parallelized Gaussian Sum Particle Filter (PGSPF) that are suitable for on-line traffic management. We show how complex probability density functions of the high dimensional trafc state can be decomposed into functions with simpler forms and the whole estimation problem solved in an efcient way. The proposed approach is general, with limited interactions which reduces the computational time and provides high estimation accuracy. The efciency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity and communication demands and compared with the case where all processing is centralized
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