In order to perform fault classification using Machine Learning algorithms, sufficient and balanced data is required. Nevertheless, in a lot of use cases data is not available in a sufficient manner to allow a stable and valid training of Machine Learning based algorithms for fault classification. There are various methods for augmenting real measured data, whereby a Generative Adversarial Network (GAN) is one of the most suitable approaches for synthetic data generation. Conditional and non-conditional data augmentation approaches are available for implementation of GAN algorithms for synthetic data generation. In the scope of the proposed paper, the performance of a conditional GAN (cGAN) and traditional GAN is evaluated and compared using vibration data of a rolling bearing measured on a bearing test rig. Both GAN approaches are used with optimized losses, considering the Wasserstein Distance and a gradient penalty in the loss function. The loss function of a cGAN contains label information, enabling the model to be trained as a holistic system for all labels, rather than being trained per label as in a traditional GAN. The training of the both GAN networks show a high performance. The synthetically generated data compared to the real data also shows sufficient similarity. Nevertheless, the distribution of the generated data is different, as the data generated with a non-conditional GAN has a better quality of results. Different methods are used to evaluate the data, for example statistical or cluster analysis, highlighting the differences in the generated data.

Prognostics and Health Management (PHM) systems are evaluated based on different performance criteria, such as result performance and cost efficiency, which may vary depending on the application. The selected features for the PHM application can have a significant influence on the overall PHM system performance. Therefore, this work discusses an approach for multi-objective feature selection for PHM, aiming to optimize overall PHM system performance regarding different criteria. A regularization based embedded approach is tested, which incorporates external evaluation factors for the features in the loss function calculation. The monetary cost of each feature is used to optimize a PHM system for two criteria: Accuracy and cost efficiency. A real test dataset from a monitoring application of a process thermostat is used for evaluation of the approach. It is shown that the presented approach can aid to select suitable subsets of features for multi-objective problems. It can assist in identifying relationships in the dataset and can reduce the dependency on domain knowledge.

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clinical workflows faces challenges, primarily linked to long scan duration resulting in patient motion during scanning and leading to image quality degradation in the reconstructed volumes. This paper introduces a novel approach to CBCT motion estimation using a gradient-based optimization algorithm, which leverages generalized derivatives of the backprojection operator for cone-beam CT geometries. Building on that, a fully differentiable target function is formulated which grades the quality of the current motion estimate in reconstruction space. We drastically accelerate motion estimation yielding a 19-fold speed-up compared to existing methods. Additionally, we investigate the architecture of networks used for quality metric regression and propose predicting voxel-wise quality maps, favoring autoencoder-like architectures over contracting ones. This modification improves gradient flow, leading to more accurate motion estimation. The presented method is evaluated through realistic experiments on head anatomy. It achieves a reduction in reprojection error from an initial average of 3mm to 0.61mm after motion compensation and consistently demonstrates superior performance compared to existing approaches. The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available. In summary, this paper contributes to the advancement of CBCT integration into clinical workflows by proposing a robust motion estimation approach that enhances efficiency and accuracy, addressing critical challenges in time-sensitive scenarios.

With the focus in the realm of automotive testing, process thermostats play a vital role in providing the required operating environment. These process thermostats, with the operating medium of an ethylene glycol-water ratio, play a crucial role in terms of controlling their thermal properties. With an emphasis on identifying the best preprocessing method for classifying these ratios, this study utilises a detailed comparison of statistical methods and the wrapper method-based Genetic Algorithm as a search method for the most relevant feature selections. Given the huge number of existing sensor parameters in the system, thereby emphasising the importance of feature selection criteria for effective analysis and model training. Furthermore, a random forest-based classifier is used with these parameters to predict the accurate ethylene glycol to water ratio.

Condition monitoring plays a crucial role in real-time evaluation of system states, but requires a large amount of measurement data to develop an accurate model. In reality a sufficient amount of data is often not available. Therefore, the proposed approach focuses on data augmentation using a Wasserstein Generative Adversarial Network (WGAN) to augment time series condition data. To enable the generation of synthetic data in the time domain, a Long Short-Term Memory (LSTM) network architecture is used in conjunction with a WGAN. The practical implementation of WGAN with a LSTM architecture is verified with two different datasets, a vibration and an acoustic dataset. Therefore, vibration data of rolling bearings in various system states recorded on a rolling bearing test rig and acoustic data of a welding process are used for synthetic data generation in separately trained networks. This work also focuses on a detailed evaluation of synthetic data in relation to the real data using various methods, such as distribution function analysis, a parameter analysis, and a visual comparison in the time as well as frequency domain. A classification model is also used to classify the real and a combined (real and synthetic) dataset to verify the benefits. The classification of the acoustic data shows an improvement in test accuracy with the combined dataset to 100% compared to the existing real measurement data of 65%. The following paper highlights the potential of the mentioned algorithm for data augmentation as an optimal solution for the described use cases. Synthetic data generation in the time domain is also critically discussed and the difficulties involved are emphasized.

In automotive testing, accurate measurement of torque and speed is critical. However, it is often expensive, requires precise mounting and positioning of sensors, as well as a controlled maintenance. The proposed approach focuses on overcoming these limitations by developing a novel concept of virtual sensors using vibration measurements as a replacement for physical and expensive sensors to measure input shaft speed and output torque with a case study of a two-stage reduction gearbox. The gearbox, mounted on a powertrain test rig, is equipped with multiple sensors, emphasizing sensor positioning and sensor combination as relevant points. A Random Forest-based regression feature importance (RFR-FI) method is used to identify the best performing sensors. A comprehensive time domain analysis and frequency domain comparison is also performed to select the optimal data for model training. Verification of the selected parameters and data is performed by a time-frequency domain analysis using spectrograms. Training two Artificial Neural Network (ANNR) models to predict input shaft speed and output shaft torque results in sufficiently accurate values for R 2 and the Root Mean Squared Error (RMSE). The trained model is evaluated on unseen data with a randomized test cycle, which also yields good results in predicting torque and speed. This method highlights a cost-effective yet reliable estimation of critical parameters for automotive testing, emphasizing the effectiveness and precision of virtual sensor techniques.

We present a training tool flow for deep neural networks (DNN) optimized for a hardware-efficient FPGA-implementation based on reconfigurable constant-coefficient multipliers (RCCMs). RCCMs replace the costly generic multipliers by shift-and-add operations. In previous work, it was shown that RCCMs offer a better alternative for saving FPGA area than utilizing low-precision arithmetic. This work proposes an improved tool flow that enables layer-wise weight quantization, a larger search space by additional RCCM coefficient sets and an optimized retraining. This leads to an improved accuracy compared to the previous method. In addition, hardware requirements are lower as only 1 to 3 adders per multiplication are used. This reduces the overall complexity and the required memory bandwidth simultaneously. We evaluate our tool flow using multiple networks (ResNets) on the ImageNet data set.

In order to perform fault classification using machine learning algorithms, sufficient and balanced data is required. The paper presents a novel approach for data augmentation using a Generative Adversarial Network (GAN) with transient, time-dependent vibration data. The proposed approach aims to generate synthetic data in form of spectrograms that closely resemble the characteristics of the real data sets. Synthetic data generation can be used to improve the training performance of neural networks for vibration analysis if not enough or unbalanced real data is available. The authors demonstrate the effectiveness of this approach by training a vibration-based fault detection model using synthetic data and comparing its performance to a model trained on real data only as well as with a t-distributed stochastic neighbour embedding (t-SNE). Real data is acquired on a bearing test rig and measurements are carried out on bearings with four different system states. The results show that the model trained with the synthetic data set outperforms the model trained with real data only, indicating that the synthetic data generated by the proposed approach can improve the training performance and accuracy of the machine learning model. Overall, the paper highlights the potential of GAN-based data augmentation approach via spectrograms for vibration analysis and offers insights into its practical application to bearings.

Condition monitoring of machine elements for end-of-line quality control is an essential part of product assurance in many production areas. Therefore, the monitored machine elements are often separated in different condition classes and are classified via conventional signal processing and machine learning methods. Especially for machine learning algorithms, collection of a sufficient amount of data from each class is particularly relevant so that balanced training datasets, regarding the condition classes, are available. In reality, however, in most cases significantly more data is captured from good system states. This results in unbalanced data sets, which can be counteracted with synthetically generated data. Generative Adversarial Networks (GAN) are a suitable approach to generate synthetic measurement data. In the scope of this paper, a use case is considered, in which bearings are monitored in an end-of-line control via acoustic signals. The generation of such data requires a high computational effort. To reduce this effort, a suitable signal pre-processing method is presented, which allows the reduction of the generated feature dimension. The synthetic data based on low-dimensional features is subsequently evaluated regarding its suitability for the condition classification. It is shown that with synthetically generated data with low-dimensional features a similar classification accuracy can be achieved as with real data, making the synthetic data suitable for data augmentation in the use case.

Counting the number of perfect matchings in arbitrary graphs is a #P-complete problem. However, for some restricted classes of graphs the problem can be solved efficiently. In the case of planar graphs, and even for K3,3-free graphs, Vazirani showed that it is in NC2. The technique there is to compute a Pfaffian orientation of a graph. In the case of K5-free graphs, this technique will not work because some K5-free graphs do not have a Pfaffian orientation. We circumvent this problem and show that the number of perfect matchings in K5-free graphs can be computed in polynomial time and we describe a circuit construction in TC2.

Instead of incorporating all complex related information system models which are relevant for different related aspects into one super-model, a multi-model framework that addresses those aspects in more detail has been studied. In this paper, first the framework is specified in term of a multidimensional data model and designed by using UML (Unified Modeling Language). The designed diagram also presents different related models as well as the interactive linkings among them. In this context, an instance of just-defined multi-model framework of well-established sectoral models, namely EC4MACS, has been developed to produce a comprehensive assessment of the likely development of future air quality and greenhouse gas emissions in Europe. The extracted-transformed-linked and published EC4MACS key data are represented in this paper in term of case studies.

The Graph Isomorphism problem restricted to graphs of bounded treewidth or bounded tree distance width are known to be solvable in polynomial time. We give restricted space algorithms for these problems proving the following results: • Isomorphism for bounded tree distance width graphs is in L and thus complete for the class. We also show that for this kind of graphs a canon can be computed within logspace. • For bounded treewidth graphs, when both input graphs are given together with a tree decomposition, the problem of whether there is an isomorphism which respects the decompositions (i.e. when only isomorphisms are considered, mapping bags in one decomposition blockwise onto bags in the other decomposition) is in L. • For bounded treewidth graphs, when one of the input graphs is given with a tree decomposition the isomorphism problem is in LogCFL. • As a corollary the isomorphism problem for bounded treewidth graphs is in LogCFL. This improves the known TC 1 upper bound for the problem given by Grohe and Verbitsky.

The Mitigation Efforts Calculator (MEC) has been developed by the International Institute for Applied Systems Analysis (IIASA) as an online tool to compare greenhouse gas (GHG) mitigation proposals by various countries for the year 2020. In this paper, first we introduce the MEC conceptual model, i.e. the methodology and system architecture. Hereafter, the optimization process and its output results, namely cost curves are presented. We then discuss the abstract formulation of four different international greenhouse gas trading regimes that are conceivable. Finally, we illustrate the MEC as a tool for interactively evaluating complex cost curve information in the context of GHG mitigation targets as currently discussed in international climate policy circles.

The isomorphism problem for planar graphs is known to be efficiently solvable. For planar 3-connected graphs, the isomorphism problem can be solved by efficient parallel algorithms, it is in the class AC 1. In this paper we improve the upper bound for planar 3-connected graphs to unambiguous logspace, in fact to UL∩coUL. As a consequence of our method we get that the isomorphism problem for oriented graphs is in NL. We also show that the problems are hard for L.

Graph isomorphism is the prime example of a computational problem with a wide difference between the best known lower and upper bounds on its complexity. There is a significant gap between extant lower and upper bounds for planar graphs as well. We bridge the gap for this natural and important special case by presenting an upper bound that matches the known log-space hardness. In fact, we show the formally stronger result that planar graph canonization is in log-space. This improves the previously known upper bound of AC. Our algorithm first constructs the biconnected component tree of a connected planar graph and then refines each biconnected component into a triconnected component tree. The next step is to log-space reduce the biconnected planar graph isomorphism and canonization problems to those for 3-connected planar graphs, which are known to be in log-space by. This is achieved by using the above decomposition, and by making significant modifications to Lindellpsilas algorithm for tree canonization, along with changes in the space complexity analysis. The reduction from the connected case to the biconnected case requires further new ideas, including a non-trivial case analysis and a group theoretic lemma to bound the number of automorphisms of a colored 3-connected planar graph. This lemma is crucial for the reduction to work in log-space.

Graph isomorphism is an important and widely studied computational problem with a yet unsettled complexity. However, the exact complexity is known for isomorphism of various classes of graphs. Recently, \cite{DLNTW09} proved that planar isomorphism is complete for log-space. We extend this result %of \cite{DLNTW09} further to the classes of graphs which exclude $K_{3,3}$ or $K_5$ as a minor, and give a log-space algorithm. Our algorithm decomposes $K_{3,3}$ minor-free graphs into biconnected and those further into triconnected components, which are known to be either planar or $K_5$ components \cite{Vaz89}. This gives a triconnected component tree similar to that for planar graphs. An extension of the log-space algorithm of \cite{DLNTW09} can then be used to decide the isomorphism problem. For $K_5$ minor-free graphs, we consider $3$-connected components. These are either planar or isomorphic to the four-rung mobius ladder on $8$ vertices or, with a further decomposition, one obtains planar $4$-connected components \cite{Khu88}. We give an algorithm to get a unique decomposition of $K_5$ minor-free graphs into bi-, tri- and $4$-connected components, and construct trees, accordingly. Since the algorithm of \cite{DLNTW09} does not deal with four-connected component trees, it needs to be modified in a quite non-trivial way.

The Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)-Model is studied and developed to provide a consistent framework for the analysis of co-benefits reduction strategies from air pollution and greenhouse gas sources. In this paper we introduced a BI approach, namely GAINS-BI, applied as a further development of the GAINS model. In this context, the GAINS-BI conceptual model, including GAINS-BI architecture and concepts, is specified based on a sound mathematical models used for calculate emission and costs. Hereafter, a multidimensional data model, e.g. activity, emission and cost data cubes, has been studied and introduced to represent specific multidimensional analysis requirements of greenhouse gas and air pollution application domains. To proof of concepts, some implementation results have been presented.