Refine
Year of publication
Document Type
- Article (unreviewed) (123) (remove)
Language
- English (123) (remove)
Has Fulltext
- no (123) (remove)
Is part of the Bibliography
- yes (123)
Keywords
- Dünnschichtchromatographie (4)
- Export (4)
- Machine Learning (4)
- Biogas (3)
- Deep Learning (3)
- Innovation (3)
- Kommunikation (3)
- Trade (3)
- Ultraschall (3)
- Advanced Footwear Technology (2)
Institute
- Fakultät Maschinenbau und Verfahrenstechnik (M+V) (35)
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (31)
- Fakultät Wirtschaft (W) (25)
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (21)
- IMLA - Institute for Machine Learning and Analytics (15)
- Fakultät Medien und Informationswesen (M+I) (bis 21.04.2021) (9)
- IfTI - Institute for Trade and Innovation (8)
- INES - Institut für nachhaltige Energiesysteme (6)
- IUAS - Institute for Unmanned Aerial Systems (4)
- ACI - Affective and Cognitive Institute (2)
- Fakultät Medien (M) (ab 22.04.2021) (2)
- IBMS - Institute for Advanced Biomechanics and Motion Studies (ab 16.11.2022) (2)
- ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik (2)
Open Access
- Open Access (57)
- Closed Access (19)
- Diamond (16)
- Bronze (8)
- Gold (1)
With economic weight shifting toward net zero, now is the time for ECAs, Exim-Banks, and PRIs to lead. Despite previous success, aligning global economic governance to climate goals requires additional activities across export finance and investment insurance institutions. The new research project initiated by Oxford University, ClimateWorks Foundation, and Mission 2020 including other practitioners and academics from institutions such as Atradius DSB, Columbia University, EDC, FMO and Offenburg University focuses on reshaping future trade and investment governance in light of climate action. The idea of a ‘Berne Union Net Zero Club’ is an important item in a potential package of reforms. This can include realigning mandates and corporate strategies, principles of intervention, as well as ECA, Exim-Bank and PRI operating models in order to accelerate net zero transformation. Full transparency regarding Berne Union members’ activities would be an excellent starting point. We invite all interested parties in the sector to come together to chart our own path to net zero
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global level explain model behavior and on a local level explain sample predictions. We propose a visual analytics workflow to support seamless transitions between global and local explanations, focusing on attributions and counterfactuals on time series classification. In particular, we adapt local XAI techniques (attributions) that are developed for traditional datasets (images, text) to analyze time series classification, a data type that is typically less intelligible to humans. To generate a global overview, we apply local attribution methods to the data, creating explanations for the whole dataset. These explanations are projected onto two dimensions, depicting model behavior trends, strategies, and decision boundaries. To further inspect the model decision-making as well as potential data errors, a what-if analysis facilitates hypothesis generation and verification on both the global and local levels. We constantly collected and incorporated expert user feedback, as well as insights based on their domain knowledge, resulting in a tailored analysis workflow and system that tightly integrates time series transformations into explanations. Lastly, we present three use cases, verifying that our technique enables users to (1)~explore data transformations and feature relevance, (2)~identify model behavior and decision boundaries, as well as, (3)~the reason for misclassifications.
Using patent information for identification of new product features with high market potential
(2014)
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an autoencoder to generate suitable latent representation. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features could be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
Deep generative models have recently achieved impressive results for many real-world applications, successfully generating high-resolution and diverse samples from complex datasets. Due to this improvement, fake digital contents have proliferated growing concern and spreading distrust in image content, leading to an urgent need for automated ways to detect these AI-generated fake images.
Despite the fact that many face editing algorithms seem to produce realistic human faces, upon closer examination, they do exhibit artifacts in certain domains which are often hidden to the naked eye. In this work, we present a simple way to detect such fake face images - so-called DeepFakes. Our method is based on a classical frequency domain analysis followed by basic classifier. Compared to previous systems, which need to be fed with large amounts of labeled data, our approach showed very good results using only a few annotated training samples and even achieved good accuracies in fully unsupervised scenarios. For the evaluation on high resolution face images, we combined several public datasets of real and fake faces into a new benchmark: Faces-HQ. Given such high-resolution images, our approach reaches a perfect classification accuracy of 100% when it is trained on as little as 20 annotated samples. In a second experiment, in the evaluation of the medium-resolution images of the CelebA dataset, our method achieves 100% accuracy supervised and 96% in an unsupervised setting. Finally, evaluating a low-resolution video sequences of the FaceForensics++ dataset, our method achieves 91% accuracy detecting manipulated videos.
The COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This study analyzed the collection contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non COVID cases. The most accurate models are MobileNet V3 (97.872 percent), DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization in this study. Our analysis shows that these tactics improve model performance and resilience for advancing COVID19 prediction and detection and shows how Deep Learning can improve disease handling. The methods we suggest would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID19 and other contagious diseases.
Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks, for example by model hardening or by adding explicit defence mechanisms. Thereby, a small “detector” is included in the network and trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. In this work, we propose a simple and light-weight detector, which leverages recent findings on the relation between networks’ local intrinsic dimensionality (LID) and adversarial attacks. Based on a re-interpretation of the LID measure and several simple adaptations, we surpass the state-of-the-art on adversarial detection by a significant margin and reach almost perfect results in terms of F1-score for several networks and datasets. Sources available at: https://github.com/adverML/multiLID
Running shoes were categorized either as motion control, cushioned, or minimal footwear in the past. Today, these categories blur and are not as clearly defined. Moreover, with the advances in manufacturing processes, it is possible to create individualized running shoes that incorporate features that meet individual biomechanical and experiential needs. However, specific ways to individualize footwear to reduce individual injury risk are poorly understood. Therefore, the purpose of this scoping review was to provide an overview of (1) footwear design features that have the potential for individualization; (2) human biomechanical variability as a theoretical foundation for individualization; (3) the literature on the differential responses to footwear design features between selected groups of individuals. These purposes focus exclusively on reducing running-related risk factors for overuse injuries. We included studies in the English language on adults that analyzed: (1) potential interaction effects between footwear design features and subgroups of runners or covariates (e.g., age, gender) for running-related biomechanical risk factors or injury incidences; (2) footwear perception for a systematically modified footwear design feature. Most of the included articles (n = 107) analyzed male runners. Several footwear design features (e.g., midsole characteristics, upper, outsole profile) show potential for individualization. However, the overall body of literature addressing individualized footwear solutions and the potential to reduce biomechanical risk factors is limited. Future studies should leverage more extensive data collections considering relevant covariates and subgroups while systematically modifying isolated footwear design features to inform footwear individualization.
Phenolic compounds, such as flavonoids and phenolic acids, are very important substances that occur in various medicinal plants. They show different pharmacological activities which might be useful in the therapy of many diseases. Phenolic compounds have achieved an increasing interest over the last years because these compounds are easily oxidized and, thus, act as strong antioxidants. We present the chemiluminescence of different phenolic compounds measured directly on high-performance thin-layer chromatography LiChrospher® plates using the oxalic acid derivative bis(2,4,6-trichlorophenyl) oxalate (TCPO) in conjunction with H2O2. Our results indicate that chemiluminescence intensity increases with an ascending number of phenolic groups in the molecule. The method can be used to detect phenolic compounds in beverages like coffee, tea, and wine.
During pyrolysis, biomass is carbonised in the absence of oxygen to produce biochar with heat and/or electricity as co-products making pyrolysis one of the promising negative emission technologies to reach climate goals worldwide. This paper presents a simplified representation of pyrolysis and analyses the impact of this technology on the energy system. Results show that the use of pyrolysis can allow getting zero emissions with lower costs by making changes in the unit commitment of the power plants, e.g. conventional power plants are used differently, as the emissions will be compensated by biochar. Additionally, the process of pyrolysis can enhance the flexibility of energy systems, as it shows a correlation between the electricity generated by pyrolysis and the hydrogen installation capacity, being hydrogen used less when pyrolysis appears. The results indicate that pyrolysis, which is available on the market, integrates well into the energy system with a promising potential to sequester carbon.
Following the traditional paradigm of convolutional neural networks (CNNs), modern CNNs manage to keep pace with more recent, for example transformer-based, models by not only increasing model depth and width but also the kernel size. This results in large amounts of learnable model parameters that need to be handled during training. While following the convolutional paradigm with the according spatial inductive bias, we question the significance of \emph{learned} convolution filters. In fact, our findings demonstrate that many contemporary CNN architectures can achieve high test accuracies without ever updating randomly initialized (spatial) convolution filters. Instead, simple linear combinations (implemented through efficient 1×1 convolutions) suffice to effectively recombine even random filters into expressive network operators. Furthermore, these combinations of random filters can implicitly regularize the resulting operations, mitigating overfitting and enhancing overall performance and robustness. Conversely, retaining the ability to learn filter updates can impair network performance. Lastly, although we only observe relatively small gains from learning 3×3 convolutions, the learning gains increase proportionally with kernel size, owing to the non-idealities of the independent and identically distributed (\textit{i.i.d.}) nature of default initialization techniques.
In order to make material design processes more efficient in the future, the underlying multidimensional process parameter spaces must be systematically explored using digitalisation techniques such as machine learning (ML) and digital simulation. In this paper we shortly review essential concepts for the digitalisation of electrodeposition processes with a special focus on chromium plating from trivalent electrolytes.
Creating growth through trade is an important part of the policy approach of many economies. For decades, many member countries of the Organisation for Economic Co-operation and Development (OECD) have cooperated in a fair competition for the benefit of their national exporters. The countries’ official export credit agencies (ECAs) have established and jointly improved rules and regulations for export credit and political risk insurance. However, new players such as China, Russia or other fast developing countries have now joined the list of top exporting nations. As these countries have established their own ECAs, there is a need to introduce rules and regulations on global standards for financial terms as well as truly international norms ensuring ‘ethical’ trading behaviour.
But how will government support for foreign trade look like in the future? Will global standards for export credit and political risk insurance become reality by 2020? And how will strict rules and regulations for officially supported export credits and FDI regarding ethics, human rights and the environment impact growth through trade in general, or exporters in particular? These are questions addressed by the thirty eight contributions to Global Policy’s third eBook entitled ‘The Future of Foreign Trade Support – Setting Global Standards for Export Credit and Political Risk Insurance’, guest edited by Andreas Klasen and Fiona Bannert.
In contrast to their traditional, non-interactive counterparts, interactive dynamic visualisations allow users to adapt their form and content to their individual cognitive skills and needs. Provided that the interactive features allow for intuitive use without increasing cognitive load, interactive videos should therefore lead to more efficient forms of learning. This notion was tested in an experimental study, where participants learned to tie four nautical knots of different complexity by watching either non-interactive or interactive videos. The results show that in the interactive condition, participants used the interactive features like stopping, replaying, reversing or changing speed to adapt the pace of the video demonstration. This led to an uneven distribution of their attention and cognitive resources across the videos, which was more pronounced for the difficult knots. Consequently users of non-interactive video presentations, needed substantially more time than users of the interactive videos to acquire the necessary skills for tying the knots.
The aim of this data collection is to enforce evidence of SCS effectiveness in treating neuropathic chronic pain and the very low percentage of undesired side effects of complications reported in our case series suggests that all implants should be performed by similarly well-trained and experienced professionals.
The paper conceptualizes the systemic approach for enhancing innovative and competitive capacity of industrial companies (named as Advanced Innovation Design Approach – AIDA) including analysis, optimizations and further development of the innovation process and promoting the innovation climate in industrial companies. The innovation process is understood as a holistic stage-gate system comprising following typical phases with feedback loops and simultaneous auxiliary or follow-up processes: uncovering of solution-neutral customer needs, technology and market trends, identification of the needs and problems with high market potential and formulation of the innovation tasks and strategy, idea generation and problem solving, evaluation and enhancement of solution ideas, creation of innovation concepts based on solution ideas, evaluation of the innovation concepts as well as implementation, validation and market launch of chosen innovation concepts. The article presents the current state of innovation research and discusses the actual status of innovation process in the industrial environment. It defines the future research tasks for amplification of the innovation process with self-configuration, self-optimization, self-diagnostics and intelligent information processing and communication.
The European TRIZ Association ETRIA acts as a connecting link between scientific institutions, universities and other educational organizations, industrial companies and individuals concerned with conceptual and practical questions relating to organization of innovation process, invention methods, and innovation knowledge. In the meantime, more than TFC 1000 papers or presentation of scientists, educators, and practitioners from all over the world are available at the official ETRIA website. Numerous research projects were supported or funded by the European Commission.
Excellent organisations require targeted strategies to implement their vision and mission, deploying a stakeholder-focused approach. As part of evidence-based policy making, it is a common approach to measure government financing vehicles’ results. A state-of-the-art method in quantitative benchmarking to overcome the challenge of considering multiple inputs and outputs is Data Envelopment Analysis (DEA). Descriptive statistics and explorative-qualitative approaches are also applied in a modern ECA benchmarking model to substantiate DEA results and put them into perspective. This enabler-result model provides a holistic view and allows to identify top performing ECAs and Exim-Banks, providing the opportunity for inefficient institutions to learn from their most productive peers. This best practice approach for strategic benchmarking enables the senior management to develop and implement a cutting-edge strategy, and increase value for key stakeholders.
In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
Silicon edges as one-dimensional waveguides for dispersion-free and supersonic leaky wedge waves
(2012)
Acoustic waves guided by the cleaved edge of a Si(111) crystal were studied using a laser-based angle-tunable transducer for selectively launching isolated wedge or surface modes. A supersonic leaky wedge wave and the fundamental wedge wave were observed experimentally and confirmed theoretically. Coupling of the supersonic wave to shear waves is discussed, and its leakage into the surface acoustic wave was observed directly. The velocity and penetration depth of the wedge waves were determined by contact-free optical probing. Thus, a detailed experimental and theoretical study of linear one-dimensional guided modes in silicon is presented.
Mice and rats make up 95% of all animals used in medical research and drug discovery and development. Monitoring of physiological functions such as ECG, blood pressure, and body temperature over the entire period of an experiment is often required. Restraining of the animals in order to obtain this data can cause great inconvenience. The use of telemetric systems solves this problem and provides more reliable results. However, these devices are mostly equipped with batteries, which limit the time of operation or they use passive power supplies, which affects the operating range. The semi-passive telemetric implant being presented is based on RFID technology and overcomes these obstacles. The device is inductively powered using the magnetic field of a common RFID reader device underneath the cage, but is also able to operate for several hours autonomously. Being independent from the battery capacity, it is possible to use the implant over a long period of time or to re-use the device several times in different animals, thus avoiding the disadvantages of existing systems and reducing the costs of purchase and refurbishment.
Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications. However, most of existing approaches are based on deep learning and require an abundant amount of labeled data to produce good results, therefore limiting their applicability. In the same vein, recent advances in meta-learning have led to successful implementations with limited available data, allowing so-called few-shot learning.
In this paper, we address this limitation of supervised methods, by proposing a novel approach based on GANs. These are trained in a meta-training manner, which allows them to perform image-to-image translations using just a few labeled samples from a new target class. This work empirically demonstrates the potential of training a GAN for few shot image-to-image translation on hair color attribute synthesis tasks, opening the door to further research on generative transfer learning.
Objective: To identify and evaluate the evidence of the most relevant running-related risk factors (RRRFs) for running-related overuse injuries (ROIs) and to suggest future research directions.
Design: Systematic review considering prospective and retrospective studies. (PROSPERO_ID: 236832)
Data sources: Pubmed. Connected Papers. The search was performed in February 2021.
Eligibility criteria: English language. Studies on participants whose primary sport is running addressing the risk for the seven most common ROIs and at least one kinematic, kinetic (including pressure measurements), or electromyographic RRRF. An RRRF needed to be identified in at least one prospective or two retrospective studies.
Results: Sixty-two articles fulfilled our eligibility criteria. Levels of evidence for specific ROIs ranged from conflicting to moderate evidence. Running populations and methods applied varied considerably between studies. While some RRRFs appeared for several ROIs, most RRRFs were specific for a particular ROI. The biomechanical measurements performed in many studies would have allowed for consideration of many more RRRFs than have been reported, highlighting a potential for more effective data usage in the future.
Conclusion: This study offers a comprehensive overview of RRRFs for the most common ROIs, which might serve as a starting point to develop ROI-specific risk profiles of individual runners. Future work should use macroscopic (big data) approaches involving long-term data collections in the real world and microscopic approaches involving precise stress calculations using recent developments in biomechanical modelling. However, consensus on data collection standards (including the quantification of workload and stress tolerance variables and the reporting of injuries) is warranted.
This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
Risk aversion, financing and real servicThe Global CEO Survey was launched in 2015 by researchers from Offenburg University, the University of Westminster and the London School of Economics and Political Science (LSE) to better understand and discover what factors influence exporters’ demand for credit insurance. Although some scholars discussed aspects of corporate insurance demand with regard to exporters, there is limited research concerning the demand for export credit insurance associated with firm-specific factors. Only few empirical studies support existing theories on corporate insurance demand and export credits. This project investigates and fills the relevant gap of official export credit insurance demand.es
Modern CNNs are learning the weights of vast numbers of convolutional operators. In this paper, we raise the fundamental question if this is actually necessary. We show that even in the extreme case of only randomly initializing and never updating spatial filters, certain CNN architectures can be trained to surpass the accuracy of standard training. By reinterpreting the notion of pointwise ($1\times 1$) convolutions as an operator to learn linear combinations (LC) of frozen (random) spatial filters, we are able to analyze these effects and propose a generic LC convolution block that allows tuning of the linear combination rate. Empirically, we show that this approach not only allows us to reach high test accuracies on CIFAR and ImageNet but also has favorable properties regarding model robustness, generalization, sparsity, and the total number of necessary weights. Additionally, we propose a novel weight sharing mechanism, which allows sharing of a single weight tensor between all spatial convolution layers to massively reduce the number of weights.
Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data. In this paper, we introduce the first publicly available large-scale dataset for "visual entity matching", based on a production level use case in the retail domain. Using scanned advertisement leaflets, collected over several years from different European retailers, we provide a total of ~786k manually annotated, high resolution product images containing ~18k different individual retail products which are grouped into ~3k entities. The annotation of these product entities is based on a price comparison task, where each entity forms an equivalence class of comparable products. Following on a first baseline evaluation, we show that the proposed "visual entity matching" constitutes a novel learning problem which can not sufficiently be solved using standard image based classification and retrieval algorithms. Instead, novel approaches which allow to transfer example based visual equivalent classes to new data are needed to address the proposed problem. The aim of this paper is to provide a benchmark for such algorithms.
Information about the dataset, evaluation code and download instructions are provided under https://www.retail-786k.org/.
In the area of cloud computing, judging the fulfillment of service-level agreements on a technical level is gaining more and more importance. To support this we introduce privacy preserving set relations as inclusiveness and disjointness based ao Bloom filters. We propose to compose them in a slightly different way by applying a keyed hash function. Besides discussing the correctness of set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. Indeed, our solution proposes to bring another layer of privacy on the sizes. We are in particular interested how the overlapping bits of a Bloom filter impact the privacy level of our approach. We concretely apply our solution to a use case of cloud security audit on access control and present our results with real-world parameters.
Pressure dynamics in metal-oxygen (metal-air) batteries: a case study on sodium superoxide cells
(2014)
Electrochemical reactions in metal–oxygen batteries come along with the consumption or release of gaseous oxygen. We present a novel methodology for investigating electrode reactions and transport phenomena in metal–oxygen batteries by measuring the pressure dynamics in an enclosed gas reservoir above the oxygen electrode. The methodology is exemplified by a room-temperature sodium–oxygen battery forming sodium superoxide (NaO2) in an electrolyte of diethylene glycol dimethyl ether (diglyme) and sodium trifluoromethanesulfonate (NaOSO2CF3, NaOTf). The experiments are supported by microkinetic simulations with a one-dimensional multiphysics continuum model. During galvanostatic cycling over 30 cycles, a constant oxygen consumption/release rate is observed upon discharge/charge. The number of transferred electrons per oxygen molecule is calculated to 1.01 ± 0.02 and 1.03 ± 0.02 for discharge and charge, respectively, confirming the nature of the oxygen reaction product as superoxide O2–. The same ratio is observed in cyclic voltammetry experiments with low scan rate (<1 mV/s). However, at higher scan rates, the ratio increases as a result of oxygen transport limitations in the electrolyte. We introduce electrochemical pressure impedance spectroscopy (EPIS) for simultaneously analyzing current, voltage, and pressure of electrochemical cells. Pressure recording significantly increases the sensitivity of impedance toward oxygen transport properties of the porous electrode systems. In addition, we report experimental data on the diffusion coefficient and solubility of oxygen in electrolyte solutions as important parameters for the microkinetic models.
We introduce an open source python framework named PHS-Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efficient method to propose the next query set of parameters.
Rotation of an elastic medium gives rise to a shift of frequency of its acoustic modes, i.e., the time-period vibrations that exist in it. This frequency shift is investigated by applying perturbation theory in the regime of small ratios of the rotation velocity and the frequency of the acoustic mode. In an expansion of the relative frequency shift in powers of this ratio, upper bounds are derived for the first-order and the second-order terms. The derivation of the theoretical upper bounds of the first-order term is presented for linear vibration modes as well as for stable nonlinear vibrations with periodic time dependence that can be represented by a Fourier series.
The mathematical representations of data in the Spherical Harmonic (SH) domain has recently regained increasing interest in the machine learning community. This technical report gives an in-depth introduction to the theoretical foundation and practical implementation of SH representations, summarizing works on rotation invariant and equivariant features, as well as convolutions and exact correlations of signals on spheres. In extension, these methods are then generalized from scalar SH representations to Vectorial Harmonics (VH), providing the same capabilities for 3d vector fields on spheres.
Micro-cracks give rise to non-analytic behavior of the stress-strain relation. For the case of a homogeneous spatial distribution of aligned flat micro-cracks, the influence of this property of the stress-strain relation on harmonic generation is analyzed for Rayleigh waves and for acoustic wedge waves with the help of a simple micromechanical model adopted from the literature. For the efficiencies of harmonic generation of these guided waves, explicit expressions are derived in terms of the corresponding linear wave fields. The initial growth rates of the second harmonic, i.e., the acoustic nonlinearity parameter, has been evaluated numerically for steel as matrix material. The growth rate of the second harmonic of Rayleigh waves has also been determined for microcrack distributions with random orientation, using a model expression for the strain energy in terms of strain invariants known in a geophysical context.