To Örebro University

This thesis addresses the problem of gas distribution modelling for gas monitoring and gas detection. The presented research is particularly focused on the methods that are suitable for uncontrolled environments. In such environments, gas source locations and the physical properties of the environment, such as humidity and temperature may be unknown or only sparse noisy local measurements are available. Example applications include air pollution monitoring, leakage detection, and search and rescue operations.

This thesis addresses how to efficiently obtain and compute predictive models that accurately represent spatio-temporal gas distribution.

Most statistical gas distribution modelling methods assume that gas dispersion can be modelled as a time-constant random process. While this assumption may hold in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling for a wider range of realistic scenarios.

This thesis proposes two time-dependent gas distribution modelling methods. In the first method, a temporal (sub-)sampling strategy is introduced. In the second method, a time-dependent gas distribution modelling approach is presented, which introduces a recency weight that relates measurement to prediction time. These contributions are presented and evaluated as an extension of a previously proposed method called Kernel DM+V using several simulation and real-world experiments. The results of comparing the proposed time-dependent gas distribution modelling approaches to the time-independent version Kernel DM+V indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios under the condition that there is a sufficient spatial coverage. Dynamic scenarios are often defined as environments where strong fluctuations and gas plume development are present.

For mobile robot olfaction, we are interested in sampling strategies that provide accurate gas distribution models given a small number of samples in a limited time span. Correspondingly, this thesis addresses the problem of selecting the most informative locations to acquire the next samples.

As a further contribution, this thesis proposes a novel adaptive sensor planning method. This method is based on a modified artificial potential field, which selects the next sampling location based on the currently predicted gas distribution and the spatial distribution of previously collected samples. In particular, three objectives are used that direct the sensor towards areas of (1) high predictive mean and (2) high predictive variance, while (3) maximising the coverage area. The relative weight of these objectives corresponds to a trade-off between exploration and exploitation in the sampling strategy. This thesis discusses the weights or importance factors and evaluates the performance of the proposed sampling strategy. The results of the simulation experiments indicate an improved quality of the gas distribution models when using the proposed sensor planning method compared to commonly used methods, such as random sampling and sampling along a predefined sweeping trajectory. In this thesis, we show that applying a locality constraint on the proposed sampling method decreases the travelling distance, which makes the proposed sensor planning approach suitable for real-world applications where limited resources and time are available. As a real-world use-case, we applied the proposed sensor planning approach on a micro-drone in outdoor experiments.

Finally, this thesis discusses the potential of using gas distribution modelling and sensor planning in large-scale outdoor real-world applications. We integrated the proposed methods in a framework for decision-making in hazardous inncidents where gas leakage is involved and applied the gas distribution modelling in two real-world use-cases. Our investigation indicates that the proposed sensor planning and gas distribution modelling approaches can be used to inform experts both about the gas plume and the distribution of gas in order to improve the assessment of an incident.

This paper presents a framework of ICT solutions developed in the EU research project DIADEM that supports environmental management with an enhanced capacity to assess population exposure and health risks, to alert relevant groups and to organize efficient response. The emphasis is on advanced solutions which are economically feasible and maximally exploit the existing communication, computing and sensing resources. This approach enables efficient situation assessment in complex environmental management problems by exploiting relevant information obtained from citizens via the standard communication infrastructure as well as heterogeneous data acquired through dedicated sensing systems. This is achieved through a combination of (i) advanced approaches to gas detection and gas distribution modelling, (ii) a novel service-oriented approach supporting seamless integration of human-based and automated reasoning processes in large-scale collaborative sense making processes and (iii) solutions combining Multi-Criteria Decision Analysis, Scenario-Based Reasoning and advanced human-machine interfaces. This paper presents the basic principles of the DIADEM solutions, explains how different techniques are combined to a coherent decision support system and briefly discusses evaluation principles and activities in the DIADEM project.

Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set.

Mobile robot olfaction solutions for gas distribution modelling offer a number of advantages, among them autonomous monitoring in different environments, mobility to select sampling locations, and ability to cooperate with other systems. However, most data-driven, statistical gas distribution modelling approaches assume that the gas distribution is generated by a time-invariant random process. Such time-invariant approaches cannot model well developing plumes or fundamental changes in the gas distribution. In this paper, we discuss approaches that explicitly consider the measurement time, either by sub-sampling according to a given time-scale or by introducing a recency weight that relates measurement and prediction time. We evaluate the performance of these time-dependent approaches in simulation and in real-world experiments using mobile robots. The results demonstrate that in dynamic scenarios improved gas distribution models can be obtained with time-dependent approaches.

To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions.

Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methodsthat statistically model gas distribution. Gas measurements are treated as randomvariables, and the gas distribution is predicted at unseen locations either using akernel density estimation or a kernel regression approach. The resulting statistical 

apmodelsdo not make strong assumptions about the functional form of the gas distribution,such as the number or locations of gas sources, for example. The majorfocus of this chapter is on two-dimensional models that provide estimates for themeans and predictive variances of the distribution. Furthermore, three extensionsto the presented kernel density estimation algorithm are described, which allow toinclude wind information, to extend the model to three dimensions, and to reflecttime-dependent changes of the random process that generates the gas distributionmeasurements. All methods are discussed based on experimental validation usingreal sensor data.

In this paper we are proposing an approach for flexible positioning of players in Soccer Simulation in a Multi-Agent environment. We introduce Dynamic Positioning based on Voronoi Cells (DPVC) as a new method for players' positioning which uses Voronoi Diagram for distributing agents in the field. This method also uses Attraction Vectors that indicate agents' tendency to specific objects in the field with regard to the game situation and players' roles. Finally DPVC is compared with SBSP as the conventional method of positioning.

Background

Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.

Results

We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network.

Conclusion

The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.

Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

Carbon capture & storage (CCS) is one of the most promis ing technologies for greenhouse gas (GHG) management.However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leakdetection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and toincrease the granularity of gas concentration measurements, a quickly deployab le, mobile measurement device isneeded. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHGemissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, theuse of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone.Alternatively, we present an adaptive strategy, which suggests sampling points based on an artific ial potential field(APF). The results of real-world experiments demonstrate the feas ibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas.

This paper presents a sampling strategy for mobile gas sensors. Sampling points are selected using a modified artificial potential field (APF) approach, which balances multiple criteria to direct sensor measurements towards locations of high mean concentration, high concentration variance and areas for which the uncertainty about the gas distribution model is still large. By selecting in each step the most often suggested close-by measurement location, the proposed approach introduces a locality constraint that allows planning suitable paths for mobile gas sensors. Initial results in simulation and in real-world experiments witha gas-sensitive micro-drone demonstrate the suitability of the proposed sampling strategy for gas distribution mapping and its use for gas source localization.

This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization.

In this paper, we present a gas dispersal simulation package which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model. Gas dispersal simulation can be useful for many applications. In this paper, we focus on the evaluation of statistical gas distribution models. Simulated data offer several advantages for this purpose, including the availability of ground truth information, repetition of experiments with the exact same constraints and that intricate issue which come with using real gas sensors can be avoided.Apart from simulation results obtained in a simulated wind tunnel (designed to be equivalent to its real-world counterpart), we present initial results with time-independent and time-dependent statistical modelling approaches applied to simulated and real-world data.