Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios

In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what-if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that attempts to strike a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors such as at the individual entity level. The mesoscopic approach offers the possibility of incorporating complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also provides an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple data (SIMD). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what-if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what-if scenario tree evaluation has been implemented in software on modern GPUs. Synthetic simulation scenarios are explored and presented here to demonstrate the basic feasibility and computational characteristics of our approach. Results from the experiments on large population data illustrate the overall modeling methodology and computational run time performance on large numbers of synthetically generated what-if scenarios.

213 A Novel, Cost-Effective and Convenient Model for Simulating Facial Wound Repair

Introduction We describe the use of a novel synthetic simulation pad for learning complex facial wound management including local flaps. The simulation pad is a cost effective and convenient model of facial wounds. We have compared the use of animal tissue to the simulation pad in the context of a workshop for surgical trainees and collected feedback from delegates. Methods Feedback was collected from 14 of 16 attending delegates. Results It was clear from feedback that animal tissue is not an ideal model of facial wounds with 71% of delegates stating that they did not consider it to be high-fidelity. The synthetic pad was rated more favourably with 100% of delegates reporting that it was a valuable exercise and well designed for local flaps. Conclusions It is imperative that training opportunities are high quality and useful to clinical practice. Techniques learnt in the context of a course or workshop are more valuable where targeted practice may occur following learning. The use of a synthetic pad is more amenable to continued practice where it may be taken home following a workshop. Feedback from this event suggests a well-designed synthetic pad is more useful than animal tissue in learning local flaps.

Linear Discriminant Analysis-Based Motion Classification Using Distributed Micro-Doppler Radars with Limited Backhaul

In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

Psychophysical Study of Human Visual Perception of Flicker Artifacts in Automotive Digital Mirror Replacement Systems

Aliasing effects due to time-discrete capturing of amplitude-modulated light with a digital image sensor are perceived as flicker by humans. Especially when observing these artifacts in digital mirror replacement systems, they are annoying and can pose a risk. Therefore, ISO 16505 requires flicker-free reproduction for 90 % of people in these systems. Various psychophysical studies investigate the influence of large-area flickering of displays, environmental light, or flickering in television applications on perception and concentration. However, no detailed knowledge of subjective annoyance/irritation due to flicker from camera-monitor systems as a mirror replacement in vehicles exist so far, but the number of these systems is constantly increasing. This psychophysical study used a novel data set from real-world driving scenes and synthetic simulation with synthetic flicker. More than 25 test persons were asked to quantify the subjective annoyance level of different flicker frequencies, amplitudes, mean values, sizes, and positions. The results show that for digital mirror replacement systems, human subjective annoyance due to flicker is greatest in the 15 Hz range with increasing amplitude and magnitude. Additionally, the sensitivity to flicker artifacts increases with the duration of observation.

New Approach to Estimate Extreme Flooding Using Continuous Synthetic Simulation Supported by Regional Precipitation and Non-Systematic Flood Data

Stochastic weather generators combined with hydrological models have been proposed for continuous synthetic simulation to estimate return periods of extreme floods. Yet, this approach relies upon the length and spatial distribution of the precipitation input data series, which often are scarce, especially in arid and semiarid regions. In this work, we present a new approach for the estimation of extreme floods based on the continuous synthetic simulation method supported with inputs of (a) a regional study of extreme precipitation to improve the calibration of the weather generator (GWEX), and (b) non-systematic flood information (i.e., historical information and/or palaeoflood records) for the validation of the generated discharges with a fully distributed hydrological model (TETIS). The results showed that this complementary information of extremes allowed for a more accurate implementation of both the weather generator and the hydrological model. This, in turn, improved the flood quantile estimates, especially for those associated with return periods higher than 50 years but also for higher quantiles (up to approximately 500 years). Therefore, it has been proved that continuous synthetic simulation studies focused on the estimation of extreme floods should incorporate a generalized representation of regional extreme rainfall and/or non-systematic flood data, particularly in regions with scarce hydrometeorological records.

Simulation Framework for Cyber-Physical Production System: Applying Concept of LVC Interoperation

In Industry 4.0, many manufacturers have built smart factories by ICTs (Information and Communications Technology), and simulation is one of the core technologies for smart manufacturing. Various kinds of simulations, depending on system levels, such as assembly line, logistics, worker, and process, are utilized for smart manufacturing. Manufacturers own heterogeneous simulations; however, they have difficulty integrating and interoperating them. This paper proposes a novel simulation framework for smart manufacturing based on the concept of live, virtual, and constructive (LVC) simulation. The LVC interoperation provides a synthetic simulation environment with the above three types of simulations. With the LVC interoperation, we propose a systematic and efficient architecture for smart manufacturing. To be specific, the interface technologies between the heterogeneous simulations and their interoperable methods are developed. Finally, we provide a practical LVC simulation applied in the manufacturing company and show what synergy can be created using the LVC simulation.

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

<p>Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep.<br></p>

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

<p>Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep.<br></p>

Learning Optimal Time Series Combination and Pre-Processing by Smart Joins

In industrial applications of data science and machine learning, most of the steps of a typical pipeline focus on optimizing measures of model fitness to the available data. Data preprocessing, instead, is often ad-hoc, and not based on the optimization of quantitative measures. This paper proposes the use of optimization in the preprocessing step, specifically studying a time series joining methodology, and introduces an error function to measure the adequateness of the joining. Experiments show how the method allows monitoring preprocessing errors for different time slices, indicating when a retraining of the preprocessing may be needed. Thus, this contribution helps quantifying the implications of data preprocessing on the result of data analysis and machine learning methods. The methodology is applied to two case studies: synthetic simulation data with controlled distortions, and a real scenario of an industrial process.