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Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network
No full textCircular Innovator Education
No full textThe following article is a first step towards raising the awareness of these new trainers (who are conscious and innovators themselves) and giving them methods, tools, and inspiration to develop sustainable innovators to think holistically. Innovators are the proportion of entrepreneurs who do not copy or optimize what already exists but commercialize new technologies or new ideas. Circular innovators are the doers who create systemic innovations solving our current social and ecological crisis
Insights, Trends and Frontiers: A Literature Review on Financial and Risk Modelling in the Information Age (2008-2019)
No full textThis study provides an overview of the model evolution and research trends in the field of financial and risk modelling by applying a bibliometric approach from 2008–2019 and an overall citation network analysis. We present a content analysis of contributing authors, countries, journals, main topics, agreements, disagreements and frontiers within the research community and highlight quantitative features such as implemented models, aggregated model-family combinations and algorithms. Moreover, we describe the data sets employed by researchers. Finally, we discuss insights, such as the main statement, namely the non-existence of a “single-best”-approach as well as the future prospects of our findings
Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic?
No full textIn this study, we present a combinatory chaos analysis of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) compared with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. We show that the dynamics of the S&P 500 return series consist of an almost equally divided combination of stochastic and deterministic chaos. The strange attractor of the S&P 500 return system is graphically displayed via Takens’ embedding and by spectral embedding in combination with Laplacian Eigenmaps. For the field of nonlinear and financial chaos research, we present a bibliometric analysis paired with citation network analysis. We critically discuss implications and future prospects
Micromilling-assisted fabrication of monolithic polymer ridge-type waveguides with integrated photonic sensing structures
No full textThis study demonstrates and discusses a novel approach for the fabrication and rapid prototyping of monolithic photonic platforms comprising a ridge-type waveguide with integrated sensing structures. First, the bulk injection-molded cyclic olefin copolymer substrates are micromilled in order to define the physical extension of the ridge structure. Cross-sections down to 30 × 30 µm2, exhibiting a mean surface roughness of 300 nm, are achieved with this process. Subsequently, UV radiation is used to modify the ridge structure’s refractive index, which leads to the formation of an optical waveguide. By employing a phase mask, it is possible to equip the photonic platform with a Bragg grating suitable for temperature measurements with a sensitivity of −5.1 pm K-1. Furthermore, an integrated Fabry-Pérot cavity, generated during the micromilling step as well, enables refractive index measurements with sensitivities up to 1154 nm RIU-1
Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution
No full textIn this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available
