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Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.
Periodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, “Biology in Space and Analogue Environments”, focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: “How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?” The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed.
The Eurosystem and the Deutsche Bundesbank will incur substantial losses in 2023 that are likely to persist for several years. Due to the massive purchases of securities in the last 10 years, especially of government bonds, the banks' excess reserves have risen sharply. The resulting high interest payments to the banks since the turnaround in monetary poli-cy, with little income for the large-scale securities holdings, led to massive criticism. The banks were said to be making "unfair" profits as a result, while the fiscal authorities had to forego the previously customary transfers of central bank profits. Populist demands to limit bank profits by, for example, drastically increasing the minimum reserve ratios in the Eurosystem to reduce excess reserves are creating new severe problems and are neither justified nor helpful. Ultimately, the EU member states have benefited for a very long time from historically low interest rates because of the Eurosystem's extraordinary loose monetary policy and must now bear the flip side consequences of the massive expansion of central bank balance sheets during the necessary period of monetary policy normalisa-tion.
Due to the massive purchases of securities in the last 15 years central banks incur substantial losses likely to persist for several years. On the other hand, the banking sector gains large profits from interest payments on their excess reserves holdings. Central banks and fiscal authorities must now bear the flip side consequences of their bond purchase programs. Populist demands to limit bank profits by drastically increasing minimum reserve ratios in the Eurosystem are creating new severe problems. Instead, a consistent and faster
normalisation of central bank balance sheets would be desirable. Central banks should also no longer be central players in government bond markets to restore the lost boundaries between fiscal and monetary policy.
Alte Weisheiten wie „Schuster bleib bei deinem Leisten!“ oder auch „Was das Hänschen nicht lernt, das lernt der Hans nimmermehr“ haben in der heutigen, sich wandelnden Arbeitswelt ihren Sinn verloren. Das bedeutet, dass sich die komplette Belegschaft lebenslang weiterbilden muss, idealerweise mit Unterstützung des Arbeitgebers.
In der Veröffentlichung "Chief Qualification Officers (CQOs) und Weiterbildungsmentoren - Thesen und Argumente" wird unter anderem das paradoxe Verhältnis von Arbeitslosigkeit und Fachkräftemangel erläutert, das Marktversagen im Weiterbildungsmarkt beschrieben und die Forderung nach einem Weiterbildungsbeauftragten (CQO) begründet.
Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, “algorithm aversion”, can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.
Modellierung des Antriebsstrangs einer Textilmaschine zum Zweck der modellbasierten Steuerung
(2024)
Zur Produktion von gewirkten Textilien werden mehrere Nadeln auf eine Legebarre gesetzt, die durch Servoantriebe positioniert werden. Aufgrund der erzwungenen Bewegung kann der Antriebsstrang bei hohen Drehzahlen zur Schwingung angeregt werden. Die richtige Wahl der Steuerkurven ist daher eine sehr wichtige und anspruchsvolle Aufgabe, die durch ein Antriebsstrangmodell unterstützt werden soll. Dafür wird am Beispiel eines Teststands ein Modell des Antriebsstranges in MATLAB/Simulink® entwickelt. Für das Antriebsstrangmodell müssen eine geeignete Modellordnung und Modellparameter gewählt werden. Die unbekannten Modellparameter werden durch eine Parameteridentifikation ermittelt. Mit einer Validierung wird ein geeignetes Anregungsspektrum für die Bestimmung der Modellordnung und die Parameteridentifikation ermittelt.
Krisen gab es schon immer. Aktuell hat sich jedoch ein ganzes Spektrum an Krisen entfaltet – es gibt politische und militärische Krisen, es brechen Energiemärke und Lieferketten weg, wir sehen ganze Branchen gefährdet, auch die Gesundheit macht uns Sorgen, vom Klimawandel ganz zu schweigen. In den Veranstaltungen der Ringvorlesung „Krisen und Auswege“, die im Wintersemester 2022/2023 stattfand, wurden Phänomene, Konzepte und Lösungen aus vielfältiger Perspektive erschlossen und damit die Komplexität des Gegenstands aufgezeigt. Herausforderungen für Unternehmen und Privatpersonen wurden identifiziert sowie der Einfluss auf technologische und organisatorische Entwicklungswege dargelegt.
In dieser Publikation wurden die einzelnen Vorträge der Ringvorlesung von den Autorinnen zusammengefasst.
Im Sommer des Jahres 2022 hatte der Bayerische Landtag das neue Hochschulinnovationsgesetz (BayHIG) verabschiedet. Darin ist erstmals der Wissenstransfer als die dritte, neue Säule der Hochschulaufgaben enthalten.
Tatsächlich hat sich das IMI bereits seit 15 Jahren dem Wissenstransfer Hochschule - Wirtschaft verschrieben und zahlreiche Projekte mit unterschiedlichem Fokus erfolgreich durchgeführt. In der vorliegenden Publikation werden die bearbeiteten Themen im Wissenstransfer, die zahlreichen verschiedenen Veranstaltungsformate sowie die Drittmittelprojekte beleuchtet.
Der technische Fortschritt von IT-Systemen, wie zum Beispiel der sogenannten „Künstlichen Intelligenz“ (KI) oder den „Common Data Spaces“ (CDSs), bringt die Frage nach der Akzeptanz dieser Systeme mit sich. Sowohl die aktive (ökonomische) Akzeptanz in Form des Erwerbs und der Nutzung, als auch die passive (soziale) Akzeptanz in Form der Duldung der aktiven Nutzung durch Dritte, sind für den Erfolg von IT-Systemen ausschlaggebend. Eine geringe Akzeptanz der Systeme und ein mangelndes Vertrauen in dieselben wird in einen Zusammenhang gebracht: Man fragt nach der Gestaltung „zuverlässiger und vertrauenswürdiger Systeme“, da diese besonders akzeptiert werden.
In diesem Beitrag werden „Vertrauen“ und dessen Zusammenhang mit „Zuverlässigkeit“ und „Verstehen“ im Kontext von IT-Systemen erörtert.
We report on laser drilling borehole arrays using ultrashort pulsed lasers with a particular focus on reducing the inadvertent heat accumulation across the workpiece by optimizing the drilling sequence. For the optimization, evolutionary algorithms are used and their results are verified by thermal simulation using Comsol and experimentally evaluated using a thermal imaging camera. To enhance process efficiency in terms of boreholes drilled per second, multi-spot approaches are employed using a spatial light modulator. However, as higher temperatures occur across the workpiece when using simultaneous multi-spot drilling as compared to a single-spot process, a subtle spatial distribution and sequence of the multi-spot approach has to be selected in order to limit the
resulting local heat input over the processing time. Different optimization approaches based on evolutionary algorithms aid to select those drilling sequences which allow for the combination of a high efficiency of multi-spot profiles, a low-generated process temperature and a high-component quality. In particular, using a 4 x 4 laser spot array allows for the drilling of 40,000 boreholes in less than 76 s (526 boreholes/s) with a reduced temperature increase by about 35%, as compared to a single spot process when employing an optimized drilling sequence.
Multi-Bessel Beams Generated by an Axicon and a Spatial Light Modulator for Drilling Applications
(2023)
We report on an optical setup to generate multi-Bessel beam profiles combining a refractive axicon and a spatial light modulator. Based on their particular beam profile, Bessel beams offer advantageous properties for micro drilling processes and internal volume processing, especially for transparent materials. In addition, the laser power of industrial, ultrashort pulsed lasers has increased significantly over the last few years, offering the possibility for highly efficient processes using multi-spot profiles. Our optical concept combines the dynamic possibilities of beam splitting using a spatial light modulator with the benefits of Bessel beams, which facilitates multi-Bessel beam processing. Beside the simulation and experimental evaluation of the generated multi-Bessel beams, we exemplify the applicability of the developed module for the perforation of thin metal foils by micro drilling.
We report on a comprehensive study of the mechanical properties of maraging steel
body-centred cubic lattice structures fabricated by a hybrid additive manufacturing technology that combines laser powder bed fusion with in situ high-speed milling. As the mechanical properties of additive manufactured components are inferior to, e.g., cast components, surface modifications can improve the mechanical behaviour. Different hybrid additive manufacturing technologies have been designed using additive and subtractive processes, improving process quality. Following this, mechanical testing is performed with respect to static tensile properties and dynamic stress, hardness, and porosity, comparing specimens manufactured by laser powder bed fusion only to those manufactured by the hybrid approach. In addition, the influence of different heat-treatment techniques on the mechanical behaviour of the lattice structures is investigated, namely solution and aging treatment as well as hot isostatic pressing. Thus, the influence of the superior surface quality due to the hybrid approach is evaluated, leading to, e.g., an offset of about 14–16% for the static testing of HIP lattice structures. Furthermore, the dynamic load behaviour can be improved with a finished surface, heading to a shift of the different zones of fatigue behaviour in the testing of hybrid-built specimens.
Axicon generated Bessel beams are a popular tool for high aspect ratio precision laser drilling. Spot diameter and working distance are given by the geometric parameters of the axicon and the wavelength used. Thus, it is difficult to manipulate the beam shape of a Bessel beam for a given setup. Spatial light modulators (SLMs) overcome limitations in flexibility. However, due to the limited phase shift of SLMs, only Bessel beams with flat cone angles and large focal length can be generated. In this contribution, an approach for generating Bessel beams with a shorter, but tunable focal length is presented. A physical axicon was combined with an SLM. A holographic image of a negative axicon is put on the SLM to generate a ring beam, which is focused by a subsequent physical axicon to get a small focal diameter. Thus, different sized high aspect ratio micro holes can be drilled without using any moving components.
In recent years, the demand for sustainable and degradable materials and electronic devices has increased significantly.
Among a range of biodegradable polymers, poly(lactic acid) (PLA) is a good alternative to conventional petrol-based polymers because of its attractive mechanical properties and its easy processability. Recently, PLA has also been described as a promising dielectric material with piezoelectric and electret properties. We expect that PLA—after further optimization— will play an important role as a material for environmentally friendly sensors in the future, where first applications such as air filters and pressure sensors have already been proposed. However, degradation under normal ambient conditions is very slow, and an accelerated and controllable degradation process is highly desirable for any type of PLA-based sensors.
Enzymatic hydrolysis with embedded enzymes has been proposed as an approach to accelerate and control degradation. In this work, we investigate the properties of PLA in terms of dielectric and mechanical properties with a focus on its ability to store charges after the enzyme proteinase K (Trit. album) has been incorporated. Results reveal that proteinase K has a positive effect on the charge stability of solvent-cast PLA electrets after complete evaporation of the solvent. Furthermore, we observed a concentration-dependent acceleration of mass loss in a Tris-HCl buffer. A fast degradation within only one day occurred at a concentration of 6 wt% proteinase K.
Two different control concepts for the automatic track guidance of forklifts are
proposed. Both approaches are based on Reinforcement Learning (RL), a method of Artificial
Intelligence (AI), and are able to take into account time-variant parameters, such as the vehicle velocity, and to reduce the influence of the path curvature, the most important disturbance variable of lateral vehicle control. In the first approach, both, the path curvature and the vehicle velocity signal, are provided to the controller in addition to the state variables of the controlled system. By varying the corresponding parameters in the training process, both signals can be considered and the control parameters can be optimized accordingly. In the second approach, several controllers (multi-model concept) considering the path curvature are used and the varying vehicle velocity is taken into account using a gain-scheduling concept. Considering time-variant vehicle parameters and the influence of the disturbance variable during operation, a stable track guidance is guaranteed within the whole speed range of the industrial trucks.
Rechtswidrigkeit einer Vorkaufssatzung zur Sicherung städtebaulicher Entwicklung, Urteilsbesprechung
(2000)
Schuldrecht BT
(2022)
BGB AT
(2023)
Schuldrecht AT
(2023)
The creation of aligning layers for the uniform orientation of liquid crystals is significant for both research and the application of liquid crystals. For all applications, the creation of aligning layers possessing controllable characteristics such as azimuthal and polar anchoring energies, easy-axis of director alignment and pretilt angle, in the same way as it is achieved by using photoaligning layers processed by light, is very important. Here, aligning properties of hybrid aligning layers created on the basis of sapphire surfaces additionally coated by photoaligning layer of PVCN-F are studied. These hybrid layers possess the properties of the nano-structured sapphire layer and the photosensitive PVCN-F layer, and complement each other. The irradiation time dependence of the azimuthal anchoring energy of the hybrid layers is studied. By using certain experimental conditions during irradiation of hybrid layers, e.g., polarization of light and irradiation time, a minimum value of the azimuthal anchoring energy, close to zero, was obtained. Atomic force microscope studies of the irradiated hybrid layers were also carried out. It was found that the behavior of the contact angle of nematic droplets placed on treated sapphire surfaces are in good agreement with properties of hybrid aligning layers and parameters of structuring surface obtained from AFM images.
内燃机测量技术和试验台架
(2023)
We report on a comprehensive study of laser percussion microvia drilling of Ajinomoto build-up film (ABF) material using an ultrashort pulsed laser in MHz burst mode. After laser processing, microvia drilling quality is being evaluated by the fabricated diameter and taper using laser scanning microscopy and metallography. The influences of the incubation effect, heat accumulation and shielding effects as a result of pulse to pulse interactions are being discussed on the ablation threshold, penetration depth and laser microvia drilling quality. We find that an increasing heat accumulation in MHz burst mode processing is responsible for the void formation and delamination of the insulating ABF layer. Therefore, the parameter clearance is introduced to evaluate these effects on the microvia sidewalls. For a comparable clearance, applying 2 intra-burst pulses achieves an average reduced taper of down to 19.5% compared to single pulse mode. At the same time, a reduced laser drilling time of 16.7% per microvia highlights the enormous potential of the MHz burst mode for laser drilling of ABF material in printed circuit board fabrication.
This article discusses the process of the laser turning of rotational symmetric, cylindrical components using ultrashort laser pulses with respect to the geometrical conditions and the resulting energy distribution during the laser turning process. As a result, process predictions and potential process optimizations are feasible. Particular attention is drawn to the laser spot formation on the cylindrical surface of the work piece in conjunction with the positioning of the laser beam relative to the rotation axis of the specimen. Based on fundamental calculations and experimental results, an optimum processing strategy is discussed, whereat the use of a trepanning optic in the laser turning process and the forming of a particular surface structure is additionally being issued.
We report on ultraviolet ultrashort pulsed laser processing of silicon carbide. Laser ablated cavities are evaluated with respect to their ablation rates, surface roughness and overall quality by varying the laser pulse fluence. Using this type of laser, high edge quality and surface roughness down to 107 nm can be achieved with an ablation efficiency of up to 0.228 mm 3 W −1 min −1. Functional surfaces are produced by generating laser induced periodic surface structures. The comparison with infrared wavelength underlines the advantages of the ultraviolet wavelength for laser micro machining this material. Significant differences with respect to the measured ablation depths and roughness as well as generated micro-and nanostructures appear. While infrared ablation is dominated by a chipping mechanism above a critical fluence of 9.93 Jcm −2 , higher ablation rates are observed with strong quality losses at the same time. In comparison to the infrared emission wavelength, in general , a significantly higher processing quality is achieved with the ultraviolet emission wavelength. In addition, the influence of spot size and repetition rate in UV processing is investigated. By increasing these parameters, a process acceleration without quality losses is enabled. The generation of a sophisticated microstructure exemplifies the advantages of processing silicon carbide with the UV laser reported here.
We report on the acceleration of femtosecond reductive laser sintering of copper (II) oxide on the surface of transparent cyclic olefin copolymers (COC) by varying the focus size to generate metallic copper (Cu) layers. By using different focusing conditions, different process speeds are achieved, and the structural accuracies are controlled. The formation of Cu lines is observed for different focus sizes at various scan speeds, which is attributed to laser exposure duration and chemical reaction time. The formed lines are categorized and measured by optical microscopy and provide the basis for two-dimensional Cu surfaces. By hatching, high quality conductive films are produced with an increased focus diameter of up to 400 µm at a scan speed of 400 mm/s and a hatch distance of 250 µm resulting in a multiple increased process speed by a factor of thirteen compared to previous research. Using 4-tip measurement , a low sheet resistance of 0.165 Ω/sq was validated on these Cu films, which have an atomic Cu content of 89.5%.
In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier.
A wavelet-based sparse row-action method for image reconstruction in magnetic particle imaging
(2021)
Purpose
Magnetic particle imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images.
Methods
In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem.
Results
Its performance is numerically evaluated against a classical FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) approach on simulated and real MPI data. The experimental results show that the proposed method increases image quality with significantly reduced computation times.
Conclusions
In comparison to state-of-the-art MPI reconstruction methods, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm.
This contribution demonstrates and discusses the preparation of finely dispersed copper(II) oxide nanosuspensions as precursors for reductive laser sintering (RLS). Since the presence of agglomerates interferes with the various RLS sub-processes, fine dispersion is required, and oversized particles must be identified by a measurement methodology. Aside from the established method of scanning electron microscopy for imaging individual dried particles, this work applies the holistic and statistically more significant laser diffraction in combination with dynamic image analysis in wet dispersion. In addition to direct ultrasonic homogenization, high-energy ball milling is introduced for RLS, to produce stable nanosuspensions with a high fine fraction, and, above all, the absence of oversize particles. Whereas ultrasonic dispersion stagnates at particle sizes between 500 nm and 20 μm, even after 8 h, milled suspension contains a high proportion of finest particles with diameters below 100 nm, no agglomerates larger than 1 μm and a trimodal particle size distribution with the median at 50 nm already, after 100 min of milling. The precursor layers produced by doctor blade coating are examined for their quality by laser scanning microscopy. The surface roughness of such a dry film can be reduced from 1.26 μm to 88 nm by milling. Finally, the novel precursor is used for femtosecond RLS, to produce homogeneous, high-quality copper layers with a sheet resistance of 0.28 Ω/sq and a copper mass concentration of 94.2%.
We demonstrate and discuss the integration of Bragg gratings in aerosol-jetted polymer optical waveguides, produced in the optical assembly and connection technology for component-integrated bus systems (OPTAVER) process. By using a femtosecond laser and adaptive beam shaping, an elliptical focal voxel generates different types of single pulse modification by nonlinear absorption in the waveguide material, which are arranged periodically to form Bragg gratings. Integration of a single grating structure or, alternatively, an array of Bragg grating structures in the multimode waveguide yields a pronounced reflection signal with typical multimodal properties, i.e., a number of reflection peaks with non-Gaussian shapes. However, the main wavelength of reflection, located around 1555 nm, is evaluable by means of an appropriate smoothing algorithm. When loaded by mechanical bending, a pronounced Bragg wavelength shift of this reflected peak up to 160 pm is detected. This demonstrates that the additively manufactured waveguides can be used not only for signal transmission but also as a sensor.
We report on an experimental study of laser microvia percussion drilling of Ajinomoto build-up film (ABF) material using an ultrashort pulsed laser in the ultraviolet region. In order to optimize the drilling quality in terms of the fabricated taper and microvia diameter, the
MHz burst mode is used in combination with both a Gaussian and a top-hat laser intensity distribution. To evaluate the influence of the top-hat beam shape on defocusing, microvias were drilled at different z-positions in the range of 200 μm evenly distributed around the focal plane. The drilling quality was evaluated by laser scanning microscopy and metallography. When using a top-hat beam shape, a reduced taper of down to 26.8 % on average is achieved compared to a Gaussian beam. For the application of 3 intra-burst pulses, we find an improvement of the taper by 11.6% compared to the single pulse mode due to the increasing heat accumulation. Using a top-hat beam shape in combination with the MHz burst mode, an 18.2 % reduction in laser drilling time for a microvia with a similar taper is remarkable, demonstrating the high potential for scaling throughput in electronics manufacturing.
We report on the laser ablation of cyclic olefin copolymer using an amplified ultrashort pulsed laser in the ultraviolet spectral range. In addition to a high ablation depth per laser-structured layer up to 74 μm at a fluence of 22 J cm−2, an excellent mean roughness 𝑅𝑎
of laser-patterned surfaces down to 0.5 μm is demonstrated. Furthermore, with increasing fluence, increasing ablation efficiencies up to 2.5 mm3 W−1 min−1 are determined. Regarding the quality of the ablation, we observed steep ablation flanks and low debris formation, though for fluences above 10.5 J cm−2 the formation of troughs was observed, being attributed to multiple reflections on the ablation flanks. For comparison, laser ablation was performed under identical conditions with an infrared laser wavelength. The results highlight that UV ablation exhibits significant advantages in terms of ablation efficiency, surface roughness and quality. Moreover, our results show that a larger UV focus spot accelerates the ablation process with comparable quality, paving the way for high-power UV ultrashort pulsed lasers towards an efficient and qualitative tool for the laser machining of cyclic olefin copolymer. The production of complex microfluidics further underlines the suitability of this type of laser.
Evaluation of an ultrashort pulsed laser robot system for flexible and large-area micromachining
(2023)
We report for the first time on the realization, characterization and application of an ultrashort pulsed laser robot system for flexible and large-area 2D and 3D laser micromachining with 6 articulated axes. To characterize the dynamic positioning of the laser beam during and after axes movement, CMOS image sensors were integrated into the beam path. A method introduced for the alignment of the optical axes allows a reduction of the deviations in laser beam positioning to less than 141.8 ± 92.9 μm within a 110° rotation range of axis 4. In addition, a high laser beam positioning repeatability of less than 102.2 μm is demonstrated over a total period of 14 h for a movement of axis 5 within a range of 0° to 90°. Initial laser cutting, laser structuring and laser marking applications on automotive dashboards and glass substrates are presented for flexible and large area 2D and 3D manufacturing. By applying a special laser cutting strategy for processing AF 32 eco thin glass, high cutting quality is achieved with a taper of up to 96.3% without the generation of cracks, demonstrating the innovative potential of the high-precision laser robot system. Nonetheless, different identified inherent influences of each axis 1–5 during robot axis movement demand for an innovative beam stabilization concept to achieve high precision in laser beam positioning.
Reflective coatings for astronomical X-ray optics were developed at the “Aschaffenburg Competence Center for Astronomical and Space Instrumentation” (ACCASI) since several years. As part of a Bavarian-Czech cooperation between the Technical University of Aschaffenburg and the Czech Technical University of Prague, now two mechanically identical telescopes were built. One telescope optic was equipped with conventional gold-coated mirrors, manufactured by the Czech project partners. The 34 X-ray mirrors of the second telescope use an innovative coating system made of chromium and iridium, which was applied at the Aschaffenburg coating laboratory. Both telescopes are designed according to the bionic principle of a reflecting lobster eye. The optics works with two consecutive reflections on mutually perpendicular mirror surfaces. This enables a large field of view with many square degrees in diameter, which, however, comes at the price of a reduced angular resolution. An extensive X-ray characterization of these telescopes was carried out at the PANTER test facility of MPE, which simulates parallel starlight incident on the telescopes. The telescopes have an angular resolution of about 4 arc minutes in X-rays and a focal length of about 2 meters. Furthermore, the used X-ray mirrors reflect and focus visible light as well and this functionality in the optical regime was checked in laboratory tests. Now another test campaign was done to examine the telescope resolution for real objects of the visible sky and the imaging properties for star constellations. Such functional tests by observing astronomical objects of the visible sky may simplify and accelerate the development of X-ray telescopes for satellite applications.
Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events
(2024)
This paper investigates the ability of autonomous driving systems to predict outcomes by
considering human factors like gender, age, and driving experience, particularly in the context of
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to
make plausible deductions, handle conflicting data, and adjust their responses in real-time during
safety-critical situations. A foundational dataset, which encompasses various driving scenarios
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep
learning model incorporates personalized cognitive agents for each driver, considering their distinct
preferences, characteristics, and requirements. This personalized approach aims to enhance the
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall
performance within autonomous driving systems. To describe the causal relationship between external
factors like weather conditions and human factors, and safety-critical driver behaviors, various
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,
correlation analysis is employed to reveal relationships between different factors, helping to
identify the strength and direction of their impact on safety-critical driver behavior.
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical
events; cognitive vehicles
1. Introduction
Despite the increasing prevalence of vehicle automation, the persistently high number
of car crashes remains a concern. Safety-critical events in human-driven scenarios have
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating
human driving behavior is imperative to establish traffic baselines for mixed
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)
behavior [1,2], influence human driving behavior [3].
Car-following behavior, illustrating how a following vehicle responds to the lead
vehicle in the same lane, is a crucial aspect. Existing car-following models often make
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving
experience, gender, character, emotions, and sociological, psychological, and physiological
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of
car-following behavior, limiting model accuracy and applicability. In the development of
more realistic car-following models for mixed traffic, acknowledging the diversity among
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction
times, decision-making processes, and driving styles, the modeling of real-world
driving complexities can be improved. Simplifying drivers into a few categories overlooks
the richness and variety of their characteristics, prompting the need for a more comprehensive
approach to capture nuances within different driver profiles. To address these
Smart Cities
This contribution quantifies the birefringence within injection-molded cyclic olefin copolymer plates and discusses its impact on the mechanical properties of the plates. It also focuses on the impact of birefringence on integrated waveguides and Bragg gratings and provides fabrication guidelines for such structures. The anisotropy in all three dimensions of the workpiece is examined by means of polarimetry and a prism coupler. It is found that the birefringence is inhomogenously distributed within the workpieces, whereas the maximum birefringence not only varies locally, but also depends on the observation direction. Overall, a maximum birefringence of 10 × 10−4 is found at the plate’s surface near the injection gate. The anisotropy then reduces exponentially towards the center of the workpiece and saturates at 1.8 × 10−4, in a depth of 0.4 mm. Thus, the birefringence strongly affects near-surface photonic structures. It is found that, depending on their orientation and the local birefringence of the substrate, waveguides and Bragg gratings fabricated with comparable parameters behave completely differently in terms of polarization-dependent optical attenuation, cross-sectional intensity distribution and Bragg reflection signal. For example, the support of the TM mode can vary between total loss and an optical attenuation of 0.9 dB × cm−1. In consequence, this study underlines the importance of quantifying the birefringent state of an injection-molded cyclic olefin copolymer workpiece if it is supposed to serve as a substrate for integrated photonic structures. The study furthermore demonstrates that birefringence effects can be omitted by burying the photonic structures deeper into the volume of the thermoplastic.
Planning adaptive learning paths for students’ progress throughout a course can be a challenging task, although it can be helpful for their learning progress. Within the HASKI-System, students should be able to get their own, personalized learning paths. In this paper, we present an approach towards the learning path sequencing problem. This idea is based on a novel proposal for arranging learning objects in a multi-dimensional space, bringing the relationship and similarities of these objects into a new relationship. We show, that we can use both, the Ant Colony Optimization Algorithm and the Genetic Algorithm with the idea of the Traveling-Salesman-Problem and get results, that are comparable with a proposed literature-based adaption mechanism. Nevertheless, the learning paths are all personalized based on the Felder & Silverman Learning Style Model and the hyperspace model will allow us later on to include more dimensions for other influencing factors.
Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap “Biology in Space and Analogue Environments” focusing on “How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?” The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed.
Machine learning algorithms make predictions by fitting highly parameterized nonlinear
functions to massive amounts of data. Yet those models are not necessarily consistent with physical
laws and offer limited interpretability. Extending machine learning models by introducing scientific
knowledge in the optimization problem is known as physics-based and data-driven modelling. A
promising development are physics informed neural networks (PINN) which ensure consistency to
both physical laws and measured data. The aim of this research is to model the time-dependent
temperature profile in bulk materials following the passage of a moving laser focus by a PINN. The
results from the PINN agree essentially with finite element simulations, proving the suitability of the
approach. New perspectives for applications in laser material processing arise when PINNs are
integrated in monitoring systems or used for model predictive control.
Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. The discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks.
The substitution of expensive non-destructive material testing by data-based process monitoring is intensively explored in quality assurance for additive manufactured components. Machine learning show promising results for defect detection but require conceptual adaption to layer wise manufacturing and line scanning patterns in laser powder bed fusion. A multi-layer approach to co-register µ-computer tomography measurements with process monitoring data is developed and a workflow for automatic data set generation is implemented. The objective of this research is to benchmark the volumetric multi-layer approach and specifically selected deep learning methods for defect detection. The volumetric approach shows superior results compared to single slice monitoring. All investigated structured neural network topologies deliver similar performance.
Limited process control can cause metallurgical defect formation and inhomogeneous relative density in laser powder bed fusion manufactured parts. This study shows that process monitoring, based on optical melt-pool signal analysis is capable of tracing relative density variations: Unsupervised machine learning, applied to cluster multiple-slice monitoring data, reveals characteristic patterns in this noisy time-series signal, which can be co-registered with geometrical positions in the build part. For cylindrical 15–5 PH stainless steel specimens, manufactured under constant process parameters and post-analyzed by µ-computer tomography, correlations between such patterns and an increased local relative density at the edge have been observed. Finite element method (FEM) modeling of thermal histories at exemplary positions close to the edge suggest pre-heating effects caused by neighboring laser scan trajectories as possible reasons for the increased melt pool intensity at the edge.
Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.
Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.
Lithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-the-art, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.
There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.
Das Eurosystem und auch die Deutsche Bundesbank werden 2023 Verluste erzielen, die einige Jahre anhalten dürften. Aufgrund der massiven Käufe von Wertpapieren in den letzten 10 Jahren, vor allem Staatsanleihen, sind die Überschussreserven der Banken stark angestiegen. Die dadurch bedingten hohen Zinszahlungen an die Banken seit der geldpolitischen Kehrtwende, bei geringen Einnahmen für die hohen Wertpapierbestände, führten zu massiver Kritik. Die Banken würden dadurch „ungerechte“ Gewinne erzielen, während die Fiskalbehörden auf die früher üblichen Überweisungen der Zentralbankgewinne verzichten müssen. Populistische Forderungen nach einer Begrenzung der Bankengewinne durch z.B. eine pauschale, drastische Erhöhung der Mindestreservesätze im Eurosystem zur Reduzierung der Überschussreserven bergen neue Probleme und sind nicht hilfreich. Letztlich haben die Staaten sehr lange von historisch niedrigen Zinsen infolge der expansiven Geldpolitik des Eurosystems profitiert und müssen nun die Folgen der massiven Aufblähung der Zentralbankbilanzen während des Zeitraums der Normalisierung der Geldpolitik tragen.
Die fortschreitende Digitalisierung im Bildungssektor ist gerade für die Berufliche Weiterbildung von zentraler Bedeutung. Bereits seit längerer Zeit sind in diesem Kontext zeit- und ortsunabhängige Lehr-Lern-Formate sehr wichtig. Allerdings bietet digitales Lernen auch großes Potential hinsichtlich dem Thema Motivationsförderung. Serious Games stellen ein vielversprechendes Beispiel von Game-based Learning (GBL) dar, um die Lernmotivation zu adressieren. Hierbei geht es darum, Mechaniken (digitaler) Spiele zu verwenden, um einen Wissenserwerb zu ermöglichen, der zeitgleich motivierend gestaltet ist.
Das AdLer-Projekt hat zum Ziel ein frei verfügbares Instrument zu entwickeln, mit welchem Lehrende virtuelle 3D-Lernumgebungen erstellen und in ihrer Lehre in unterschiedlichen Formen einsetzen können. Mit Hilfe eines Autorentools werden Lehrende bei der Konzeption und Erstellung von 3D-Lernumgebungen unterstützt. Dadurch können Lehrende auf schnellem und einfachem Weg eigene digitale 3D-Lernumgebungen erstellen sowie nach ihren individuellen Vorstellungen hinsichtlich der eigenen Lernszenarien konfigurieren.
In der 3D-Lernumgebung können Lernende nach den Prinzipien des GBL erkundungsorientiert und interaktiv lernen. Dazu können Lernende mit einem virtuellen Avatar die AdLer-3D-Lernumgebung betreten und verschiedene Handlungsmöglichkeiten wahrnehmen. Sie können sich frei in dieser Lernumgebung bewegen und unterschiedliche Lernangebote (z.B. die Bearbeitung von Lernelementen, wie H5P, Text und Video) durchführen.
Im geplanten Poster-Pitch wird eine aktuelle Version der Software AdLer präsentiert und steht für die Konferenzteilnehmer zum Ausprobieren bereit.
Development of an Authoring Tool for the Creation of Individual 3D Game-Based Learning Environments
(2023)
Game-based learning in general and serious games in particular have a promising potential in higher education. In this article we going to show the capability of serious games with regard to current challenges in higher education. The focus of this article is the presentation of the AdLer authoring tool, which offers lecturers the possibility to design and generate virtual 3D learning environments
in which students can interact with learning content according to the principles of game-based learning.
The present white paper concerns the indications and recommendations of the SciSpacE Science Community to make progress in filling the gaps of knowledge that prevent us from answering the question: “How Do Gravity Alterations Affect Animal and Human Systems at a Cellular/Tissue Level?” This is one of the five major scientific issues of the ESA roadmap “Biology in Space and Analogue Environments”. Despite the many studies conducted so far on spaceflight adaptation mechanisms and related pathophysiological alterations observed in astronauts, we are not yet able to elaborate a synthetic integrated model of the many changes occurring at different system and functional levels. Consequently, it is difficult to develop credible models for predicting long-term consequences of human adaptation to the space environment, as well as to implement medical support plans for long-term missions and a strategy for preventing the possible health risks due to prolonged exposure to spaceflight beyond the low Earth orbit (LEO). The research activities suggested by the scientific community have the aim to overcome these problems by striving to connect biological and physiological aspects in a more holistic view of space adaptation effects.
We consider in details the dual models for the Goldstone mesons (pions) scattering in the presence of the explicit chiral symmetry breaking caused by non-zero current quark mass. New method of incorporation of the quark masses into the dual model is suggested. In contrast to the previously considered in the literature methods, the dual amplitude obtained by this method is consistent with all low-energy theorems following from the Effective Chiral Lagrangian (EChL) to the O(p^4) order and simultaneously it does not contain states with negative width. The resonance spectrum of the model and its implications for the fourth and sixth order EChL in large N_c limit are discussed. We argue that the possible relations between large N_c QCD and some underlying string theory can be revealed by studying interactions of hadrons at low-energies.
We discuss the use of a hybrid system utilizing Object Oriented Bayesian networks and influence diagrams for probabilistic reasoning under uncertainties in industrial process operations. The Bayesian networks are used for condition monitoring and root cause analysis of process operation. The recommended decision sequence of corrective actions and observations is obtained following the "myopic" approach. The BN inference on most probable root cause is used in an influence diagram for taking decisions on urgency of corrective actions vs. delivery deadline. The build-in chain of causality from root cause to process faults can provide the user with explanation facility and a simulation tool of the effect of intended actions.
We discuss a Root Cause Analysis (RCA) system implementing a probabilistic approach based on Bayesian inference for adaptive reasoning under uncertainties in industrial process operation. The proposed approach is model based and accumulates the process knowledge within the problem domain, which data is gathered and stored in XML-based information server. The Bayesian networks have been created automatically from the XML-structured data. The interconnection between XML-failure trees is handled as object oriented instances of Bayesian sub-networks within master-network covering the entire process and monitoring its overall condition, output quality and equipment effectiveness. We implement sequential on-line adaptivity of models' parameters to reflect changes in process operation. The system learning can be supervised by user feedback on the actual root cause. The general RCA methodology is applied to plate cutting in a hot rolling mill.
We propose a methodology for Root Cause Analysis (RCA), allowing fast and flexible decision support for operators, maintenance staff and process engineers in pulp and paper industry. RCA can identify non-obvious process problems and is therefore a powerful complement to normal automatic control. The general methodology is applied to a continuous digester.
In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Visy Pulp and Paper mill in Tumut, Australia.
We consider a critical composite superconformal string model to desribe hadronic interactions. We present a new approach of introducing hadronic quantum numbers in the scattering amplitudes. The physical states carry the quantum numbers and form a common system of eigenfunctions of the operators in this string model. We give explicit constructions of the quantum number operators.
Learning Analytics Dashboard for Educators: Proposed Project to Design with Pedagogical Background
(2023)
In this article, the authors describe a prototype of a Learning Analytics Dashboard (LAD) for educators. It is based on the analysis of pedagogical actions and taking into the process and learning style of students in an online environment based on learning analytics (LA). A description of the Dashboard structure, divided into levels and categories based on available learning analytics, will allow the educator to dive deeper into the online course themselves and explore more. It will also allow them to determine the level of student performance, identify gaps in learning materials, and research student data.
The authors have identified further directions for the development of a LAD for a professor, including modeling algorithms for researching student behavior and learning style using Artificial Intelligence and presenting LA in a visualized form.
This paper shows the stages of creating a professor's LAD prototype as a functional part of the adaptive learning system in the HASKI-System to analyze visual information obtained from LA and the possibilities to monitor the learning process, learning progress, student activity, and make decisions on careful intervention in the students’ learning process.
Die vorliegende Studie untersucht Entwicklungen und Trends im Nachhaltigkeitscontrolling, insbesondere hinsichtlich der strategischen Bedeutung der fünf Stufen der Nachhaltigkeit, des Einflusses von Stakeholdergruppen, Zielsetzungen und Instrumenten anhand von drei Studien des Fachkreis Green Controlling for Responsible Business und gibt Implikationen für die Controlling- und Unternehmenspraxis.
We present an application, where extensions of existing methods for decision-theoretic troubleshooting are used for industrial process operation and asset management. The extension includes expected average cost of asset management actions, prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand.
Applications of object-oriented Bayesian networks for causal analysis of process disturbances
(2003)
We discuss a hybrid approach for causal analysis of disturbances in industrial process operation. It represents a combination of OOBN with first level diagnostic packages and physical models serving as agents in the system design and providing evidence for automated reasoning on abnormality in process operation. The aim is causal analysis of non-measurable disturbances as a decision advice complement to the distributed control system (DCS). The approach includes prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand. The methodology has been applied on a screening process with a pressure-flow network in a Pulp Mil.
ADAPTIVE RISK ASSESSMENT IN COMPLEX LARGE SCALE PROCESSES WITH REDUCED COMPUTATIONAL COMPLEXITY
(2004)
We have developed a methodology that targets risk assessment for process operation. It includes both abnormality prediction and evaluation of its development, provided no corrective actions are taken, as well as a possibility to examine the impact of intended actions. It handles the uncertainties in the domain and the big number of influences on the effect variables by utilizing causal probabilistic modeling. The process is modeled by Hidden Markov Models (HMM), and object oriented dynamic Bayesian networks (OOBNs). Various modeling techniques and assumptions have been used to reduce the computational complexity and ensure fast inference. The methodology is applied in a case study.
The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been applied. The methodology integrates decision-theoretic troubleshooting with risk assessment for industrial process control. It is applied to a pulp digesting and screening process. The process is modeled using generic object-oriented Bayesian networks (OOBNs). The system performs reasoning under uncertainty and presents to users corrective actions, with explanations of the root causes. The system records users’ actions with associated cases and the BN models are prepared to perform sequential learning to increase its performance in diagnostics and advice.
Die entwickelte Methodik für die Fehlerursachenanalyse (RCA) stellt ein Instrument zur Entscheidungsunterstützung dar, das den Prozesszustand auf der Grundlage qualitativer und quantitativer Informationen bewertet. Das vorgestellte RCA-System nutzt die verfügbaren Daten, um die wahrscheinlichsten Grundursachen zu ermitteln und schlägt eine Handlungssequenz vor. Die Lernfähigkeit des Systems ermöglicht seine sequentielle Online-Anpassung, um Änderungen im Prozessbetrieb zu berücksichtigen.
The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process. We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space.
This work demonstrates that histological grading of brain tumors and astrocytomas can be accurately predicted and causally explained with the help of causal probabilistic models, also known as Bayesian networks (BN). Although created statistically, this allows individual identification of the grade of malignancy as an internal cause that has enabled the development of the histological features to their observed state. The BN models are built from data representing 794 cases of astrocytomas with their malignant grading and corresponding histological features. The computerized learning process is improved when pre-specified knowledge (from the pathologist) about simple dependency relations to the histological features is taken into account. We use the BN models for both grading and causal analysis. In addition, the BN models provide a causal explanation of dependency between the histological features and the grading. This can offer the biggest potential for choice of an efficient treatment, since it concentrates on the malignancy grade as the cause of pathological observations. The causal analysis shows that all ten histological features are important for the grading. The histological features are causally ordered, implying that features of first order are of higher priority, e.g. for the choice of treatment in order not to allow the malignancy to progress to a higher degree. Due to the explanations of feature relations, the causal analysis can be considered as a powerful complement to any malignancy classification tool and allows reproducible comparison of malignancy grading.
To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presence of 50 histologic features was rated in 4 categories from 0 (not present) to 3 (abundant) by visual inspection of the sections under a microscope. All tumors were also classified with the corresponding WHO grade between I and IV. We tested the prediction performance of several statistical classification tools (learning vector quantization [LVQ], supervised relevance neural gas [SRNG], support vector machines [SVM], and generalized regression neural network [GRNN]) for this data set. The WHO grade was predicted correctly from histologic features in close to 80% of the cases by 2 modern classifiers (SRNG and SVM), and GRADO-IGL was predicted correctly in > 84% of the cases by a GRNN. A standardized report, based the 50 histologic features, can be used in conjunction with modern classification tools as an objective and reproducible method for histologic grading of astrocytomas.
In diesem Artikel wird ein Ansatz zur Erkennung von Spurwechselmanövern mit Hilfe von objekt-orientierten Bayes Netzen beschrieben. Dieser Ansatz ist eine Erweiterung von Grundlagenarbeiten zur Einscherererkennung. Zunächst werden die zur Erkennung von Spurwechselvorgängen erforderlichen Fahrsituationsmerkmale vorgestellt. Darauf aufbauend wird das entwickelte objekt-orientierte Bayes Netz zur Modellierung der Spurwechsel erläutert. Dabei wird ein Spurwechsel als eine Beziehung zwischen zwei Fahrzeugen betrachtet. Dabei kann ein Fahrzeug in der Spur bleiben, die Spur nach links oder nach rechts wechseln. Durch die Kombination der Möglichkeiten der beiden Fahrzeuge entstehen 9 Klassen. Aus den relativen Positionen der Fahrzeuge ergeben sich aus den 9 Situationsklassen 27 mögliche Spurwechselmanöver. Dabei ist das Einschervorgang ein Sonderfall der modellierten Fahrmanöver.
In this paper we introduce a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and identify some of the key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy grids for all vehicles. This combination allows for an efficient classification of the existing vehiclelane and vehicle-vehicle relations in a traffic scene and thus substantially improves the understanding of complex traffic scenes. We systematically propagate probabilities and variances within our network which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, we are able to classify a total of 27 driving maneuvers including merging and object following.
We propose a system design for preventive traffic safety in general intersection situations involving all present traffic participants (vehicles and vulnerable road users) in the context of their environment and traffic rules. It exploits the developed overall probabilistic framework for modeling and analysis of intersection situations under uncertainties in the scene, in measured data or in communicated information. It proposes OOBN modeling for the cognitive assessment of potential and real danger in intersection situations and presents schematically an algorithm for multistage cognitive situation assessment. A concept for the interaction between situation assessment and the proposed Proactive coaching Safety Assistance System (PaSAS) is outlined. The assessment of danger in a situation development serves as a filter for the output and intensity of HMI-signals for directing driver's attention to essentials.
This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy schedule grids for all modeled vehicles. This combination allows an efficient classification of the existing vehicle-lane and vehicle-vehicle relations in traffic scenes and thus substantially improves the understanding of complex traffic scenes. Probabilities and variances within the network are propagated systematically which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, the network is able to classify a total of 27 driving maneuvers including merging and object following.
This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fused from communicated data, vehicles local perception and self-localization. Thus, a cooperatively validated set of data is obtained to characterize all objects involved in a situation (resolving occlusions). The system is tested with data, acquired by vehicles with heterogenic equipment (without/with perception). In a first step the probabilistic mapping of a vehicle onto a fixed set of traffic lanes and forward motion predictions is introduced. Second, criticality measures are evaluated for these motion predictions to infer the collision probability. In our test vehicle this probability is then used to warn the driver of a possible hazardous situation. It serves as a likelihood alarm parameter for deciding the intensity of HMI acoustic signals to direct the driver's attention. First results in various simulated and live real-time scenarios show, that a collision can be predicted up to two seconds before a possible impact by applying the developed Bayesian network. The extension of this network to further situation features is the content of ongoing research.
The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The development process was accompanied by a series of driving simulator studies to determine both the potential to reduce traffic conflicts and important design characteristics of early advisory warning signals. The most important details of the prototype system's components inter-vehicle information-fusion and situation analysis are described and the achieved warning timings are compared to the results of the driving simulator studies.
An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended.
Cooperative perception makes it possible – in addition to emergency warnings – to provide drivers with early advisory warnings about potentially dangerous driving situations. Based on research results pertaining to imminent crash warnings, it was expected that the effectiveness of such advisory warnings depends on situation-specific anticipations by the driver. During a simulator study, N = 20 drivers went through a wide range of longitudinal traffic and intersection scenarios. The scenarios varied in the possibility to anticipate traffic conflicts (Anticipation: high vs. low) and were completed under different visibility conditions (Visibility: obstructed vs. visible), with and without driver assistance based on cooperative perception (i.e., visual-auditory advisory warnings two seconds prior to the last-possible warning moment; assistance: no assistance vs. with assistance). The warning concept was based on empirical pre-studies and previously validated on a public test intersection. During non-assisted driving, critical situations were mainly experienced when the possibility to anticipate traffic conflicts was low. Visual obstructions lead to a further increase in the frequency of critical situations. Furthermore, the results indicate a clear mitigation of critical encounters when providing early advisory warnings which is independent from sight obstructions. This applies particularly to surprising and unexpected scenarios and thus illustrates the potential of cooperative perception to enhance active traffic safety.
Kurzfassung Diese Arbeit stellt ein robustes wissensbasiertes Verfahren zur Lückenbewertung für Spur-wechselmanöver vor. Zur Modellierung wurden dynamische Bayes-Netzwerke eingesetzt und mit Hilfe von Lernalgorithmen die Erkennungsleistung verbessert. Die Testergebnisse zeigen eine sehr hohe Trefferquote.
This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility.
This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility.
This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane. Dynamic Bayesian networks (DBN) capture the motion of the own and surrounding vehicles as a dynamic process, following the trend development of lateral motion features. The static and dynamic models for maneuver recognition are statistically evaluated with real highway driving data sequences. The DBN demonstrates earlier recognition (∼ 1.1 seconds) and higher accuracy (1.12% error rate) with more stable performance than the static models. The system is deployed on an experimental vehicle, where a Divide-and-conquer approach to inference in object-oriented Bayesian networks is introduced and its implementation is shown to require computation time of 0.15 milliseconds and ROM memory ∼ 10 2 kilobytes. The promising performance evaluation results are confirmed by test drives in real highway traffic.
We outline the challenges of situation awareness with early and accurate recognition of traffic maneuvers and how to assess them. This includes also an overview of the available data and derived situation features,handling of data uncertainties, modelling and the approach for maneuver recognition. An efficient and effective solution, meeting the automotive requirements, is successfully deployed and tested on a prototype car. Test driving results show that earlier recognition of intended maneuver is feasible on average 1 second (and up to 6.72 s) before the actual lane-marking crossing. The even earlier maneuver recognition is dependent on the earlier recognition of surrounding vehicles.
Conditional automated driving (CAD) systems (SAE level 3) will soon be introduced to the public market. This automation level is designed to take care of all aspects of the dynamic driving task in specific application areas and does not require the driver to continuously monitor the system performance. However, in contrast to higher levels of automation the "fallback-ready" user always has to be able to regain control if requested by the system. As CAD allows the driver to engage in non-driving-related tasks (NDRTs) past human factors research has looked at their effects on takeover time and quality especially in short-term takeover situations. In order to understand how takeover performance is impacted by different NDRTs, this paper summarizes and compares available results according to the NDRT's impact on the sensoric, motoric and cognitive transition. In addition, aspects of arousal and motivation are considered. Due to the heterogeneity of the empirical work and the available data practically relevant effects can only be attested for NDRTs that cause severe discrepancies between the current driver state and the requirements of the takeover task, such as sensoric and motoric unavailability. The paper concludes by discussing methodological issues and recommending the development of standardized methods for the future.
The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing.
Livable cities measure quality-of-life factors such as transportation, convenience of daily life, education, and a safe and stable built and natural environment. Livability of a city includes also some social and psychological factors, like emotion and perception. How do we realize the advantages of new technology under mixed traffic conditions, while observing all daily requirements on safety, convenience and high education needs?
Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style
(2023)
Erste Schritte zur Implementierung eines Nachhaltigkeitsmanagements im Unternehmen
Dieser kompakte Band geht auf zentrale Prinzipien und Konzepte ein, die Unternehmen helfen, ein Nachhaltigkeitsmanagement aufzubauen und weiterzuentwickeln. Darüber hinaus bietet er einen Überblick über die verschiedenen Ansätze und Instrumente des Nachhaltigkeitsmanagements, damit Unternehmen ihre Aktivitäten analysieren, bewerten und verbessern können, um ökologische und soziale Auswirkungen zu reduzieren und langfristige Wertschöpfung zu ermöglichen.
Diese grundlegende Einführung richtet sich an die Führungskräfte in den Unternehmen, die sich mit Fragen des Umwelt- und Nachhaltigkeitsmanagements beschäftigen. Zunächst werden die relevanten normativen und regulativen Anforderungen an das unternehmerische Nachhaltigkeitsmanagement vorgestellt, bevor konkret erste Schritte zur Implementierung eines Nachhaltigkeitsmanagements entwickelt werden. Die beiden letzten Kapitel stellen ein geeignetes Steuerungssystem sowie die Grundlagen der Nachhaltigkeitskommunikation eines Unternehmens vor.
Das Buch vermittelt die Grundlagen, um die Besonderheiten der Elektronik und Software im Kfz nicht nur zu kennen, sondern auch zu verstehen. Zusätzlich wird an Beispielen die Komplexität realer Systeme im Fahrzeug vorgeführt und gezeigt, welche Anwendungen durch die Elektronik erst möglich werden. Das Spannungsfeld zwischen Sicherheit, Zuverlässigkeit und Komplexität prägt in Verbindung mit branchenüblichen Abläufen das Vorgehen bei der Entwicklung, das ein in diesem Bereich tätiger Ingenieur verstehen muss.
In this paper, a new control concept for automatic track guidance of forklifts within a heterogeneous logistics fleet is presented. The proposed control scheme is universally applicable and based on Artificial Intelligence (AI). The lateral control is realized using a Model Predictive Controller (MPC). In order to take into account the diversity of the industrial truck variants, a multi-model approach is used. Therefore, a plant model for each truck variant is integrated into the MPC. In a practical application, the most suitable model has to be selected. This decision is based on AI in the form of an Artificial Neural Network (ANN). It is able to distinguish the different truck variants based on that part of the plant's state vector, which represents the vehicle dynamic characteristics. Thus, the appropriate model can be used, which significantly improves the control quality and guarantees an accurate track guidance of different forklifts. Due to the AI-based selection of the model, the computational effort can be kept low and real-time capability can be ensured. In order to classify the performance of the proposed control concept, its simulation results are compared to the closed-loop behavior, using a classical MPC.
An integrative definition of digital, innovation, and sustainable: A bridge between disciplines
(2023)
It needs to be clarified how digital, innovation, and sustainable concepts are interdependent and what dimensions they encompass from an organizational perspective. This article aims to fill these gaps. 12,249 articles are analyzed, of which 776 are minimum C-ranked according to VHB-Jourqual, and 457 pass the quality filters. Only 48 publications deal with all three concepts, and 24 offer definitions. This results from an eight-step systematic literaturereview. Three dimensions for 'digital' (data, technologies, and networks), three dimensions for ‘sustainable’(environmental, social, and economic), and four dimensions for ‘innovation’(product, service, business model, and process) are elaborated in a framework. Based on this, an integrated definition is derivedusing a content analysis.The DIS-definition can be used for strategic decision-making processes and bridges the disciplines of technology, innovation, and sustainability management. Finally, a canvas that clarifies the concepts and their connections is proposed for interdisciplinary management discussions.