Enterprises in the field of industrial automation experience an increasing demand for providing virtualized software solutions. Inspired by the recent trends in serverless and cloud computing, software virtualization is considered even for safety-critical applications with hard real-time requirements, as a means of avoiding hardware vendor lock-in and reducing volume and maintenance cost of devices. In this work, we evaluate the applicability of OS-level virtualization to an industrial automation use case. Our application runs in Docker containers on top of Linux patched with PREEMPT_RT. We investigate the ability of Docker coupled with diverse networking technologies to fulfill the latency requirements of the application under normal or heavy system load. We empirically compare four networking technologies with respect to communication latency and frequency of missing packets. The results indicate that Docker with certain technologies, such as the Single Root I/O Virtualization interface, performs robustly even under heavy load, enabling sufficient performance isolation and low overhead that does not jeopardise the real-time performance of our application.

Testing of large and complex industrial control systems is challenging as the space of possible input and environmental parameters is large. Searching the entire space for potential failures is practically infeasible. This paper introduces an industrial control system robustness testing problem and evaluates artificial intelligence (AI) based strategies to efficiently explore the space and to identify parameter sets that can cause the system to fail. The proposed solution approach uses regression techniques to speed up the search and clustering methods to identify parameter sets that represent distinct system failures.

In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term correlation in the series on top of the features inferred from the VAE module. As a result, our detection algorithm is capable of identifying anomalies that span over multiple time scales. We demonstrate the effectiveness of our detection algorithm on five real world problems and find our method outperforms three other commonly used detection methods.

We propose a photo-realistic method for artificially ageing facial photographs by combining learned shape deformations with skin detail transfer between a donor and a receiver face. Facial ageing is a complicated process that most existing face models, such as 3d Morphable Models fail to express, due to lacking correspondence between the wrinkles of different individuals. We propose an exemplar-based approach to face ageing, where we transfer high-frequency details from an older face texture to a younger receiver. By warping the resulting image according to a learned shape ageing deformation, we obtain photo-realistic aged photographs. We evaluate the simulator with human perception experiments showing that we can indeed create results that are perceived to be real.

In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration model-building demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model.

We propose an efficient self-shadowing illumination model for Morphable Models. Simulating self-shadowing with ray casting is computationally expensive which makes them impractical in Analysis-by-Synthesis methods for object reconstruction from single images. Therefore, we propose to learn self-shadowing for Morphable Model parameters directly with a linear model. Radiance transfer functions are a powerful way to represent self-shadowing used within the precomputed radiance transfer framework (PRT). We build on PRT to render deforming objects with self-shadowing at interactive frame rates. It can be illuminated efficiently by environment maps represented with spherical harmonics. The result is an efficient global illumination method for Morphable Models, exploiting an approximated radiance transfer. We apply the method to fitting Morphable Model parameters to a single image of a face and demonstrate that considering self-shadowing improves shape reconstruction.