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Image novelty detection is a repeating task in computer vision and describes the detection of anomalous images based on a training dataset consisting solely of normal reference data. It has been found that, in particular, neural networks are well-suited for the task. Our approach first transforms the training and test images into ensembles of patches, which enables the assessment of mean-shifts between normal data and outliers. As mean-shifts are only detectable when the outlier ensemble and inlier distribution are spatially separate from each other, a rich feature space, such as a pre-trained neural network, needs to be chosen to represent the extracted patches. For mean-shift estimation, the Hotelling T2 test is used. The size of the patches turned out to be a crucial hyperparameter that needs additional domain knowledge about the spatial size of the expected anomalies (local vs. global). This also affects model selection and the chosen feature space, as commonly used Convolutional Neural Networks or Vision Image Transformers have very different receptive field sizes. To showcase the state-of-the-art capabilities of our approach, we compare results with classical and deep learning methods on the popular dataset CIFAR-10, and demonstrate its real-world applicability in a large-scale industrial inspection scenario using the MVTec dataset. Because of the inexpensive design, our method can be implemented by a single additional 2D-convolution and pooling layer and allows particularly fast prediction times while being very data-efficient.
Eine konsequente Kunden- und Marktorientierung hat einen wesentlichen Einfluss auf den Erfolg eines Unternehmens. Das Marketing erhält damit einen herausgehobenen Stellenwert in der Unternehmensführung und übt einen nachhaltigen Einfluss auf alle Funktionen im Unternehmen aus. In der Managementausbildung wird zunehmend das theoretisch vermittelte Wissen anhand von Fallstudien aus der Unternehmenspraxis vertieft. Die Studierenden erhalten damit die Möglichkeit, das Erlernte in Kleingruppen anzuwenden. Die hierzu erforderlichen Fallstudien stehen im deutschsprachigen Raum jedoch nur begrenzt zur Verfügung. Die vorliegende Sammlung von Fallstudien zur marktorientierten Unternehmensführung basiert auf Beiträgen, die angehende Wirtschaftsingenieure und Maschinenbauingenieure kurz vor ihrem Bachelor- bzw. Masterabschluss an der Hochschule Konstanz Technik, Wirtschaft und Gestaltung unter Anleitung des Herausgebers erstellt haben. Die Autoren zeigen auf, wie aktuelle Aspekte aus dem Bereich der Marketingstrategien und -instrumente in Unternehmen aus verschiedenen Branchen Anwendung finden. Die vorbereiteten Fragen ermöglichen es, die Fallstudien während oder nach der Vorlesung in Kleingruppenform zu bearbeiten. Die angegebenen Literaturhinweise bieten die Möglichkeit, sich vertiefend mit der jeweiligen Thematik zu befassen. Diese Fallstudiensammlung ist darüber hinaus auch für Praktiker von Interesse, die sich mit aktuellen Fragestellungen aus dem Bereich der marktorientierten Unternehmensführung näher auseinandersetzen möchten.
Heute vergeht keine Woche, in der nicht in der Presse über neue Unternehmenskrisen berichtet wird. Dabei überrascht, dass scheinbar auch größere Unternehmen weitgehend auf die Anwendung präventiver Maßnahmen des Krisenmanagements verzichten. Krisen entstehen meist nicht kurzfristig, sondern kündigen sich bereits im Vorfeld an. Diese schwachen Signale gilt es frühzeitig zu erfassen, um rechtzeitig und konsequent gegensteuern zu können. Hierzu steht ein umfangreiches Set von Methoden zur Verfügung. Viele Beispiele aus der aktuellen Wirtschaftspraxis zeigen, dass auch im Bereich des reaktiven Krisenmanagements Schwächen bestehen, Manager also vielfach zögern, die erforderlichen Gegenmaßnahmen umzusetzen. In der Managementausbildung wird zunehmend das theoretisch vermittelte Wissen anhand von Fallstudien aus der Unternehmenspraxis vertieft. Die Studierenden erhalten somit die Möglichkeit, in Kleingruppen das Erlernte anzuwenden und zu diskutieren. Die hierzu erforderlichen Fallstudien stehen im deutschsprachigen Raum allerdings in vielen Bereichen erst begrenzt zur Verfügung. Die vorliegende Sammlung von Fallstudien zum Krisenmanagement basiert auf Beiträgen, die angehende Wirtschaftsingenieurinnen und Wirtschaftsingenieure kurz vor ihrem Bachelorabschluss an der Hochschule Konstanz Technik, Wirtschaft und Gestaltung unter Anleitung des Herausgebers erstellt haben. Hierbei werden Unternehmen aus verschiedenen Branchen betrachtet, die alle mit einer Unternehmenskrise konfrontiert waren. Die Autoren zeigen auf, wie die jeweilige Krise entstanden ist und welche Maßnahmen vom Management ergriffen wurden, um diese zu bewältigen. Die vorbereiteten Fragen ermöglichen es, die Fallstudien während oder nach der Vorlesung in Kleingruppenform zu bearbeiten. Diese Fallstudiensammlung ist darüber hinaus auch für Praktiker von Interesse, die sich mit dem Thema Krisenmanagement näher auseinandersetzen möchten.
Im modernen Automobilbau spielen Fussgängerschutzsysteme eine immer stärker werdende Rolle, um Verletzungen und Todesfälle bei Verkehrsunfällen mit Fussgängerbeteiligung zu reduzieren. Eines dieser Sicherheitssysteme ist die aktive Motorhaube. Durch die Verwendung von Formgedächtnislegierungen (FGL) als Aktoren können die bestehenden Systeme vereinfacht werden, wobei die gleiche Funktion durch neue Mechanismen bei reduzierter Grösse und Gewicht sowie verringerten Kosten ausgeführt werden kann. In diesem Beitrag werden nach einer Einleitung zu existierenden Systemen zur Motorhauben-Anhebung FG-Aktoren und deren potenzielle Einsatzmöglichkeiten in Automobilbau kurz vorgestellt.
Nowadays, organizations must invest strategically in information technology (IT) and choose the right digital initiatives to maximize their benefit. Nevertheless, Chief Information Officers still struggle to communicate IT costs and demonstrate the business value of IT. The goal of this paper is to support their effective communication. In focus groups, we analyzed how different stakeholders perceive IT costs and the business value of IT as the basis of communication. We identified 16 success factors to establish effective communication. Hence, this paper enables a better understanding of the perception and the operationalization of effective communication.
This thesis investigates methods for the recognition of facial expressions using support vector machines. Rather than trying to recognize facial actions in the face such as raised eyebrow, mouth open and frowns. These facial actions are described in the Facial Action Coding System (FACS) and are essential facial components, which can be combined to form facial expressions. We perform independent recognition of 6 upper and 10 lower action units in the face, which may occur either individually or in combination. Based on a feature extraction from grey-level values, the system is expected to recognize under real-time conditions. Results are presented with different image resolutions, SVM kernels and variations of low-level features.
In this paper, approximating the shape of a sailing boat using elliptic cones is investigated. Measurements are assumed to be gathered from the target's surface recorded by 3D scanning devices such as multilayer LiDAR sensors. Therefore, different models for estimating the sailing boat's extent are presented and evaluated in simulated and real-world scenarios. In particular, the measurement source association problem is addressed in the models. Simulated investigations are conducted with a static and a moving elliptic cone. The real-world scenario was recorded with a Velodyne Alpha Prime (VLP-128) mounted on a ferry of Lake Constance. Final results of this paper constitute the extent estimation of a single sailing boat using LiDAR data applying various measurement models.
Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model
(2022)
Random matrices are widely used to estimate the extent of an elliptically contoured object. Usually, it is assumed that the measurements follow a normal distribution, with its standard deviation being proportional to the object’s extent. However, the random matrix approach can filter the center of gravity and the covariance matrix of measurements independently of the measurement model. This work considers the whole chain from data acquisition to the linear Kalman Filter with extension estimation as a reference plant. The input is the (unknown) ground truth (position and extent). The output is the filtered center of gravity and the filtered covariance matrix of the measurement distribution. A virtual measurement model emulates the behavior of the reference plant. The input of the virtual measurement model is adapted using the proposed algorithm until the output parameters of the virtual measurement model match the result of the reference plant. After the adaptation, the input to the virtual measurement model is considered an estimation for position and extent. The main contribution of this paper is the reference model concept and an adaptation algorithm to optimize the input of the virtual measurement model.
Random matrices are used to filter the center of gravity (CoG) and the covariance matrix of measurements. However, these quantities do not always correspond directly to the position and the extent of the object, e.g. when a lidar sensor is used.In this paper, we propose a Gaussian processes regression model (GPRM) to predict the position and extension of the object from the filtered CoG and covariance matrix of the measurements. Training data for the GPRM are generated by a sampling method and a virtual measurement model (VMM). The VMM is a function that generates artificial measurements using ray tracing and allows us to obtain the CoG and covariance matrix that any object would cause. This enables the GPRM to be trained without real data but still be applied to real data due to the precise modeling in the VMM. The results show an accurate extension estimation as long as the reality behaves like the modeling and e.g. lidar measurements only occur on the side facing the sensor.
The random matrix approach is a robust algorithm to filter the mean and covariance matrix of noisy observations of a dynamic object. Afterward, virtual measurement models can be used to find iteratively the extent parameters of an object that would cause the same statistical moments within their measurements. In previous work, this was limited to elliptical targets and only contour measurements.In this paper, we introduce the parallel use of an elliptical, triangular and rectangular-shaped virtual measurement model and a shape classification that selects the model that fits best to the measurements. The measurement likelihood is modeled either via ray tracing, a uniformly or normally spatial distribution over the object’s extent or as a combination of those.The results show that the extent estimation works precisely and that the classification accuracy highly depends on the measurement noise.
Many countries offer state credit guarantees to support credit-constrained exporters. The policy instrument is commonly justified by governments as a means to mitigating adverse outcomes of financial market frictions for exporting firms. Accumulated returns to the German state credit guarantee scheme deriving from risk-compensating premia have outweighed accumulated losses over the past 60 years. Why do private financial agents not step in and provide insurance given that the state-run program yields positive returns? We argue that costs of risk diversification, liquidity management, and coordination among creditors limit the ability of private financial agents to offer comparable insurance products. Moreover, we suggest that the government’s greater effectiveness in recovering claims in foreign countries endows the state with a cost advantage in dealing with the risks involved in large export projects. We test these hypotheses using monthly firm-level data combined with official transaction-level data on covered exports of German firms and find suggestive evidence that positive effects on trade are due to mitigated financial constraints: State credit guarantees benefit firms that are dependent on external finance, if the value at risk which they seek to cover is large, and at times when refinancing conditions on the private financial market are tight.
Corporate Entrepreneurship (CE) units have become an increasingly important part of established companies’ development activities enabling them to also create more discontinuous innovations. As a result, companies have developed and implemented different forms of CE units, such as corporate accelerators, incubators, startup supplier programs, and corporate venture capital. Driven by the need to innovate, companies have even begun to use multiple CE units simultaneously. However, this has not been empirically investigated yet. Thus, with this study, we aim to shed some light on this by investigating the parallel use of multiple CE units in the German business landscape. We conducted an extensive desk research, combining, coding, and analyzing different sources. We found that 55 out of 165 large established companies have multiple CE units, which allowed us to characterize the parallel use and identify differences and similarities, e.g., in terms of industry, company size, and CE forms implemented. We conclude by presenting different implications for both practice and research and by pointing out directions for future research.
Diese Masterarbeit erforscht das Potenzial großer Sprachmodelle in der Bauindustrie mit einem Fokus auf digitale Transformation, Effizienzsteigerung und Nachhaltigkeit. Durch eine umfassende Literaturanalyse und qualitative Experteninterviews werden spezifische Anwendungsfälle, Herausforderungen bei der Implementierung und ethische sowie datenschutzrechtliche Überlegungen untersucht.
Die Arbeit hebt hervor, wie große Sprachmodelle die Planungsprozesse optimieren, das Risikomanagement verbessern und maßgeschneiderte Lösungen entwickeln können, um ökonomische und ökologische Vorteile zu erzielen. Zudem werden praxisorientierte Empfehlungen für eine erfolgreiche Integration dieser Technik in das Bauwesen präsentiert, die sowohl die technologische Machbarkeit als auch soziale Akzeptanz berücksichtigen.
Abschließend werden zukünftige Forschungsrichtungen aufgezeigt, die darauf abzielen, die digitale Transformation im Bauwesen unter Einbeziehung ethischer Standards und Datenschutz zu beschleunigen.
Die Ergebnisse dieser Arbeit demonstrieren das Potenzial von großen Sprachmodellen, traditionelle Bauprozesse zu revolutionieren, und betonen die Notwendigkeit einer sorgfältigen Implementierung, um die Vorteile dieser Technologie vollständig auszuschöpfen.
Prior quantitative research identified in the text of technology-based ventures' business plans distinctive performance patterns of evolving business models. Accordingly, interactions with customers, financiers, and people and the patenting strategy's status evolved and served as indicators of early-stage tech ventures' performance. With longitudinal data from five venture cases, this research sheds light on the evolving business model by validating the performance patterns, and elucidating how and why the ventures' business models evolved. Based on a generic systems theory framework for the indicators, the explanatory case studies re-contextualize the performance patterns taken from the snapshot perspective of business plans to the longitudinal perspective of technology-based ventures' life-cycle. This research confirms the relation of business model patterns of digital and non-digital ventures to the performance groups of failure, survival, or success and suggests a broader systems perspective for further research.
Experimentelle Untersuchung einer Kontaktverbindung für die Anwendung im konstruktiven Holzbau
(2021)
Im Rahmen dieser Arbeit wurde eine Holz-Holz-Kontaktverbindung aus Brettschichtholz ohne mechanische Verbindungsmittel hinsichtlich ihrer Steifigkeit experimentell untersucht. Eine Druckstrebe wurde mit Hilfe eines versteckten Treppenversatzes unter einem Winkel von 30° bzw. 60° an einen Gurt angeschlossen. Insgesamt wurden in den zwei Prüfreihen 20 Prüfkörper untersucht.
Das für die Versuche verwendete Brettschichtholz wurde von vier verschiedenen Firmen und Ingenieurbüros in Deutschland zur Verfügung gestellt. Für die experimentellen Untersuchungen in dieser privatfinanzierten Masterthesis standen so immerhin ca. 50 Meter Brettschichtholz zur Verfügung.
Die Versuche wurden in der Öffentlichen Prüfstelle für Baustoffe und Geotechnik an der HTWG durchgeführt.
Die Auswertung der Versuchsergebnisse soll Aufschluss über die Steifigkeit, die Tragfähigkeit und das mechanische Verhalten eines solchen modifizierten Versatzes geben. Diese Erkenntnisse sollen eine Grundlage für die computergestützte Modellierung von Verbindungen schaffen, um lokale Effekte in Anschlussbereichen besser zu verstehen und möglichst realitätsnah implementieren zu können.
Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
(2022)
A reliable quantification of the worst-case influence of model uncertainty and external disturbances is crucial for the localization of vessels in marine applications. This is especially true if uncertain GPS-based position measurements are used to update predicted vessel locations that are obtained from the evaluation of a ship’s state equation. To reflect real-life working conditions, these state equations need to account for uncertainty in the system model, such as imperfect actuation and external disturbances due to effects such as wind and currents. As an application scenario, the GPS-based localization of autonomous DDboat robots is considered in this paper. Using experimental data, the efficiency of an ellipsoidal approach, which exploits a bounded-error representation of disturbances and uncertainties, is demonstrated.
Nowadays there is a rich diversity of sleep monitoring systems available on the market. They promise to offer information about sleep quality of the user by recording a limited number of vital signals, mainly heart rate and body movement. Typically, fitness trackers, smart watches, smart shirts, smartphone applications or patches do not provide access to the raw sensor data. Moreover, the sleep classification algorithm and the agreement ratio with the gold standard, polysomnography (PSG) are not disclosed. Some commercial systems record and store the data on the wearable device, but the user needs to transfer and import it into specialised software applications or return it to the doctor, for clinical evaluation of the data set. Thus an immediate feedback mechanism or the possibility of remote control and supervision are lacking. Furthermore, many such systems only distinguish between sleep and wake states, or between wake, light sleep and deep sleep. It is not always clear how these stages are mapped to the four known sleep stages: REM, NREM1, NREM2, NREM3-4. [1] The goal of this research is to find a reduced complexity method to process a minimum number of bio vital signals, while providing accurate sleep classification results. The model we propose offers remote control and real time supervision capabilities, by using Internet of Things (IoT) technology. This paper focuses on the data processing method and the sleep classification logic. The body sensor network representing our data acquisition system will be described in a separate publication. Our solution showed promising results and a good potential to overcome the limitations of existing products. Further improvements will be made and subjects with different age and health conditions will be tested.
The principal objective of this study is to investigate the impact of perceived stress on traffic and road safety. Therefore, we designed a study that allows the generation and collection of stress-relevant data. Drivers often experience stress due to their perception of lack of control during the driving process. This can lead to an increased likelihood of traffic accidents, driver errors, and traffic violations. To explore this phenomenon, we used the Stress Perceived Questionnaire (PSQ) to evaluate perceived stress levels during driving simulations and the EPQR questionnaire to determine the personality of the driver. With the presented study, participants can categorised based on their emotional stability and personality traits. Wearable devices were utilised to monitor each participant's instantaneous heart rate (HR) due to their non-intrusive and portable nature. The findings of this study deliver an overview of the link between stress and traffic and road safety. These findings can be utilised for future research and implementing strategies to reduce road accidents and promote traffic safety.
Excubation
(2015)
Ökonomische Aktivitäten sind auf den Input hochwertiger Energieträger angewiesen; diese sind knapp und werden in der fossil-nuklearen Energiewirtschaft aufgrund einer qualitativen Fehlanpassung zwischen Primärenergieeinsatz und Nutzenergiebedarf verschwenderisch genutzt. Daraus resultieren ökologische Probleme, insbesondere der Klimawandel, mit entsprechenden externen Kosten. Ein Umstieg auf erneuerbare Energien und effizientere Nutzungsstrukturen unterliegt diversen Pfadabhängigkeiten und ist aufgrund der multiplen Lernkosten mit hohen Pfadwechselkosten verbunden, die ebenfalls von der Gesellschaft getragen werden müssen. Unterschiedliche politökonomische Interessen der maßgeblichen Staaten verhindern derzeit harmonische weltweite Lösungen. Für eine evolutorische Energieökonomik ergeben sich einige Herausforderungen, insbesondere hinsichtlich der Klärung von sekundären und tertiären Pfadabhängigkeiten, der Erfassung systemischer Wechselwirkungen sowie der Problematik von Interventionsspiralen und der Formulierung von evolutorischen Designregeln für Energie- und Zertifikatemärkte.
To learn from the past, we analyse 1,088 "computer as a target" judgements for evidential reasoning by extracting four case elements: decision, intent, fact, and evidence. Analysing the decision element is essential for studying the scale of sentence severity for cross-jurisdictional comparisons. Examining the intent element can facilitate future risk assessment. Analysing the fact element can enhance an organization's capability of analysing criminal activities for future offender profiling. Examining the evidence used against a defendant from previous judgements can facilitate the preparation of evidence for upcoming legal disclosure. Follow the concepts of argumentation diagrams, we develop an automatic judgement summarizing system to enhance the accessibility of judgements and avoid repeating past mistakes. Inspired by the feasibility of extracting legal knowledge for argument construction and employing grounds of inadmissibility for probability assessment, we conduct evidential reasoning of kernel traces for forensic readiness. We integrate the narrative methods from attack graphs/languages for preventing confirmation bias, the argumentative methods from argumentation diagrams for constructing legal arguments, and the probabilistic methods from Bayesian networks for comparing hypotheses.
Technologiebasierte Startups leisten einen wesentlichen Beitrag zur wirtschaftlichen sowie gesellschaftlichen Entwicklung. Im Zuge ihrer Gründung benötigen sie Unterstützung in Form von Risikokapital, das in der Seed- und Early-Stage primär durch Business Angels (BAs) bereitgestellt wird. Die Abläufe und Bewertungskriterien des BA Investmentprozesses sind bisher jedoch unzureichend erforscht. Der vorliegende Beitrag nutzt Experteninterviews im Rahmen einer Fallstudie des baden-württembergischen entrepreneurialen Ökosystems zur Identifikation des Vorgehens von BAs bei der Bewertung und Auswahl technologiebasierter Startups. Zudem werden die Kriterien, nach denen BAs vielversprechende von scheiternden Startups unterscheiden abgeleitet. Somit trägt der Beitrag zur Öffnung der „Black Box” von Investmentaktivitäten in den frühsten Gründungsphasen bei.
Evaluation of tech ventures’ evolving business models: rules for performance-related classification
(2022)
At the early stage of a successful tech venture's life cycle, it is assumed that the business model will evolve to higher quality over time. However, there are few empirical insights into business model evolution patterns for the performance-related classification of early-stage tech ventures. We created relevant variables evaluating the evolution of the venture-centric network and the technological proposition of both digital and non-digital ventures' business models using the text of submissions to the official business plan award in the German State of Baden-Württemberg between 2006 and 2012. Applying a principal component analysis/rough set theory mixed methodology, we explore performance-related business model classification rules in the heterogeneous sample of business plans. We find that ventures need to demonstrate real interactions with their customers' needs to survive. The distinguishing success rules are related to patent applications, risk capital, and scaling of the organisation. The rules help practitioners to classify business models in a way that allows them to prioritise action for performance.
The importance of sleep for human life is enormous. It affects physical, mental, and psychological health. Therefore, it is vital to recognise sleep disorders in a timely manner in order to be able to initiate therapy. There are two methods for measuring sleep-related parameters - objective and subjective. Whether the substitution of a subjective method for an objective one is possible is investigated in this paper. Such replacement may bring several advantages, including increased comfort for the user. To answer this research question, a study was conducted in which 75 overnight recordings were evaluated. The primary purpose of this study was to compare both ways of measurement for total sleep time and sleep efficiency, which are essential parameters for, e.g., insomnia diagnosis and treatment. The evaluation results demonstrated that, on average, there are 32 minutes of difference between the two measurement methods when total sleep time is analysed. In contrast, on average, both measurement methods differ by 7.5% for sleep efficiency measurement. It should also be noted that people typically overestimate total sleep time and efficiency with the subjective method, where the perceived values are measured.
In the reverse engineering process one has to classify parts of point clouds with the correct type of geometric primitive. Features based on different geometric properties like point relations, normals, and curvature information can be used, to train classifiers like Support Vector Machines (SVM). These geometric features are estimated in the local neighborhood of a point of the point cloud. The multitude of different features makes an in-depth comparison necessary. In this work we evaluate 23 features for the classification of geometric primitives in point clouds. Their performance is evaluated on SVMs when used to classify geometric primitives in simulated and real laser scanned point clouds. We also introduce a normalization of point cloud density to improve classification generalization.
Nowadays, the importance of early active patient mobilization in the recovery and rehabilitation phase has increased significantly. One way to involve patients in the treatment is a gamification-like approach, which is one of the methods of motivation in various life processes. This article shows a system prototype for patients who require physical activity because of active early mobilization after medical interventions or during illness. Bedridden patients and people with a sedentary lifestyle (predominantly lying in bed) are also potential users. The main idea for the concept was non-contact system implementation for the patients making them feel effortless during its usage. The system consists of three related parts: hardware, software, and game application. To test the relevance and coherence of the system, it was used by 35 people. The participants were asked to play a video game requiring them to make body movements while lying down. Then they were asked to take part in a small survey to evaluate the system's usability. As a result, we offer a prototype consisting of hardware and software parts that can increase and diversify physical activity during active early mobilization of patients and prevent the occurrence of possible health problems due to predominantly low activity. The proposed design can be possibly implemented in hospitals, rehabilitation centers, and even at home.
Evaluation of a Contactless Accelerometer Sensor System for Heart Rate Monitoring During Sleep
(2024)
The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain.
Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
Vor dem Hintergrund der großen Bedeutung des Europäischen Wirtschaftsrechts, gerade auch für das deutsche Wirtschaftsleben, gibt die vorliegende Veröffentlichung zunächst einen einführenden Überblick über die Entwicklung, die Ziele und Zuständigkeiten sowie die Organisationsstruktur und die Rechtsquellen der Europäischen Union. Es soll deutlich werden, wo und wie Recht, speziell Wirtschaftsrecht, überhaupt entsteht und welche Inhalte dieses jeweils aufweist. Dieser erste Teil der Studie ist bewusst sehr ausführlich gehalten und dient als Wissens- und Verständnisgrundlage für den zweiten Teil, die eigentliche Darstellung des Europäischen Wirtschaftsrechts unter besonderer Berücksichtigung der Grundfreiheiten als Rahmenbedingung eines marktorientierten Managements deutscher Unternehmen. Die Studie soll als Einstieg in dieses Rechtsgebiet dienen und gleichzeitig Vertiefungen zu bestimmten einzelnen Aspekten jeweils zu erleichtern helfen. Adressaten sind Studierende von Universitäten, Fachhochschulen, Berufsakademien und anderen Bildungseinrichtungen, die sich während ihres Studiums mit dem Recht der Europäischen Union zu befassen haben, ebenso wie betroffene Entscheidungsträger der Wirtschaft.
The purpose of this paper is to examine the effects of perceived stress on traffic and road safety. One of the leading causes of stress among drivers is the feeling of having a lack of control during the driving process. Stress can result in more traffic accidents, an increase in driver errors, and an increase in traffic violations. To study this phenomenon, the Stress Perceived Questionnaire (PSQ) was used to evaluate the perceived stress while driving in a simulation. The study was conducted with participants from Germany, and they were grouped into different categories based on their emotional stability. Each participant was monitored using wearable devices that measured their instantaneous heart rate (HR). The preference for wearable devices was due to their non-intrusive and portable nature. The results of this study provide an overview of how stress can affect traffic and road safety, which can be used for future research or to implement strategies to reduce road accidents and promote traffic safety.
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
The estimation of the holding periods of financial products has to be done in a dynamic process in which the size of the observation time interval influences the result. Small intervals will produce smaller average holding periods than bigger ones. The approach developed in this paper offers the possibility of estimating this average independently of the size of this time interval. This method is demonstrated on the example of two distributions, based on the exponential and the geometric probability functions. The estimation will be found by maximizing the likelihood function.
Error correction coding based on soft-input decoding can significantly improve the reliability of flash memories. Such soft-input decoding algorithms require reliability information about the state of the memory cell. This work proposes a channel model for soft-input decoding that considers the asymmetric error characteristic of multi-level cell (MLC) and triple-level cell (TLC) memories. Based on this model, an estimation method for the channel state information is devised which avoids additional pilot data for channel estimation. Furthermore, the proposed method supports page-wise read operations.
Spatial modulation (SM) is a low-complexity multiple-input/multiple-output transmission technique that combines index modulation and quadrature amplitude modulation for wireless communications. In this work, we consider the problem of link adaption for generalized spatial modulation (GSM) systems that use multiple active transmit antennas simultaneously. Link adaption algorithms require a real-time estimation of the link quality of the time-variant communication channels, e.g., by means of estimating the mutual information. However, determining the mutual information of SM is challenging because no closed-form expressions have been found so far. Recently, multilayer feedforward neural networks were applied to compute the achievable rate of an index modulation link. However, only a small SM system with two transmit and two receive antennas was considered. In this work, we consider a similar approach but investigate larger GSM systems with multiple active antennas. We analyze the portions of mutual information related to antenna selection and the IQ modulation processes, which depend on the GSM variant and the signal constellation.
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow backend with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data.
Angesichts der aktuellen höchstrichterlichen Rechtsprechung ist der betagte oder sonst hinfällige, auf Geldentschädigung klagende Verletzte gezwungen, in Rechtsmittelüberlegungen sein nahendes Ableben als erhebliches Prozessrisiko mit einzubeziehen. Das führt zu einer beträchtlichen Schmälerung der ideellen Bestandteile seines Persönlichkeitsrechts. Es ist daher dringend geboten, im Wege der richterlichen Rechtsfortbildung Kriterien für Ausnahmefallgruppen aufzustellen, um untragbare Härtefälle künftig verlässlich auszuschließen.
Zum automatischen Testen der Endgeräte bei Nokia wird ein computergestütztes Systen namens Austere eingesetzt. Dieses System wurde vollständig innerhalb der Firma konzipiert und wird ständig weiterentwickelt. Der erste Teil der Diplomarbeit bestand darin, ein solches Austere Testsystem aus neuen Hardwarekomponenten aufzubauen und die schon vorhandene Testsoftware namens RAPT darauf anzupassen. Der Aufbau sowie der Zusammenhang der Soft- und Hardwarekomponenten untereinander und die Handhabung des Systems wird in Kapitel 3 beschrieben. Erst wenn man die Komplexität des Systems und den Zusammenhang der Komponenten untereinander verstanden hat, kann man sich den Erweiterungen widmen. Sie gehören zum zweiten Teil der Diplomarbeit. Diese Erweiterungen werden in den Folgekapiteln behandelt. In ihnen wird für das jeweilige Thema zunächst die Theorie beschrieben und anschließend soll eine Lösung der Erweiterung des Austere-Systems gegeben werden.