Accelerating geoscience and engineering system simulations on graphics hardware

Many complex natural systems studied in the geosciences are characterized by simple local-scale interactions that result in complex emergent behavior. Simulations of these systems, often implemented in parallel using standard central processing unit (CPU) clusters, may be better suited to parallel processing environments with large numbers of simple processors. Such an environment is found in graphics processing units (GPUs) on graphics cards.This paper discusses GPU implementations of three example applications from computational fluid dynamics, seismic wave propagation, and rock magnetism. These candidate applications involve important numerical modeling techniques, widely employed in physical system simulations, that are themselves examples of distinct computing classes identified as fundamental to scientific and engineering computing. The presented numerical methods (and respective computing classes they belong to) are: (1) a lattice-Boltzmann code for geofluid dynamics (structured grid class); (2) a spectral-finite-element code for seismic wave propagation simulations (sparse linear algebra class); and (3) a least-squares minimization code for interpreting magnetic force microscopy data (dense linear algebra class). Significant performance increases (between 10× and 30× in most cases) are seen in all three applications, demonstrating the power of GPU implementations for these types of simulations and, more generally, their associated computing classes.     

Intrusion detection alarms reduction using root cause analysis and clustering

As soon as the Intrusion Detection System (IDS) detects any suspicious activity, it will generate several alarms referring to as security breaches. Unfortunately, the triggered alarms usually are accompanied with huge number of false positives. In this paper, we use root cause analysis to discover the root causes making the IDS triggers these false alarms; most of these root causes are not attacks. Removing the root causes enhances alarms quality in the future. The root cause instigates the IDS to trigger alarms that almost always have similar features. These similar alarms can be clustered together; consequently, we have designed a new clustering technique to group IDS alarms and to produce clusters. Then, each cluster is modeled by a generalized alarm. The generalized alarms related to root causes are converted (by the security analyst) to filters in order to reduce future alarms’ load. The suggested system is a semi-automated system helping the security analyst in specifying the root causes behind these false alarms and in writing accurate filtering rules. The proposed clustering method was verified with three different datasets, and the averaged reduction ratio was about 74% of the total alarms. Application of the new technique to alarms log greatly helps the security analyst in identifying the root causes; and then reduces the alarm load in the future.     

Parallel Lattice Boltzmann Method with Blocked Partitioning

This paper presents and discusses a blocked parallel implementation of bi- and three-dimensional versions of the Lattice Boltzmann Method. This method is used to represent and simulate fluid flows following a mesoscopic approach. Most traditional parallel implementations use simple data distribution strategies to parallelize the operations on the regular fluid data set. However, it is well known that block partitioning is usually better. Such a parallel implementation is discussed and its communication cost is established. Fluid flows simulations crossing a cavity have also been used as a real-world case study to evaluate our implementation. The presented results with our blocked implementation achieve a performance up to 31% better than non-blocked versions, for some data distributions. Thus, this work shows that blocked, parallel implementations can be efficiently used to reduce the parallel execution time of the method.     

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.     

Robust tuning of Two-Degree-of-Freedom (2-DoF) PI/PID based cascade control systems

A design approach for Two-Degree-of-Freedom (2-DoF) PID controllers within a cascade control configuration that guarantees robust and smooth control is presented in this paper. The use of a cascade control configuration comes into place when the use of an additional (intermediate) sensor provides the possibility for a compensation of a load-disturbance before it affects the output variable. The rationale of operation associated to both the inner and outer controllers determines the need of good performance for disturbance attenuation (regulation) as well as set-point following (tracking). Therefore, the use of 2-DoF controllers is introduced. However, the use of 2-DoF controllers, introduces additional parameters that need to be tuned appropriately. Specially for the case of PI/PID controllers there are not known clear auto-tuning guidelines for such situation. The approach undertaken in this paper provides the complete set of tuning parameters for the inner (2-DoF PI) controller and the outer (2-DoF PID) controller. The trade-off among control system performance (measured in terms of closed-loop response speed) and robustness allows to derive a recommendation for the design-parameter lower limit. The design equations are formulated in such a way that a non-oscillatory response is specified for both the inner and outer loop. A side advantage of providing the complete set of parameters is that it avoids the need for the usual identification experiment for the tuning of the outer controller.     

An Objective Deghosting Quality Metric for HDR Images

Reconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result.     

Compact HLIF LiDAR for marine applications

The accidental discharge or accumulated pollution in seawater constitutes high risk of damage to the marine environment. Early detection of deviations in water quality allows taking preventive measures in order to minimize harmful influence of pollution or to implement adequate response actions before the pollution becomes a major spill. This requires regular monitoring of risk areas with effective, reliable, and economically sustainable solutions. The Hyperspectral Laser Induced Fluorescence (HLIF) LiDAR (light detection and ranging) combines highly sensitive and selective oil-in-water detection with characterization capabilities based on feature extraction and pattern recognition in HLIF spectra. Therefore, this technique is equally effective for oil detection in open and coastal waters. Operated in unattended mode as a payload of marine vehicles, it delivers the real-time analytical capabilities directly on site. This article describes the development of HLIF LiDAR and its application on board of operational vessels in the Norwegian Sea, Barents Sea, and the Baltic Sea during the period 2014–2015.     

Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient‐specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter‐patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.