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Nowadays established companies use Corporate Entrepreneurship (CE) as a means to create discontinuous innovations. Many companies thereby even implement multiple CE units that typically involve several entrepreneurial activities. This explorative study aimed to identify the reasons why established companies implement multiple CE units concurrently. In conducting a comparative case study with eight companies from different industries, valuable insights for science and practice were gained. We provide an overview of different 11 reasons for implementing multiple CE units. This shows that the combination of CE units used by companies differs depending on the reason. It further allowed to derive general approaches of established companies to the implementation of CE units. Last, we identify the concept of co-specialization to be a central driver explaining the creation of the need to set up multiple units. We conclude by indicating implications and subjects for future research.
The cornerstone of cognitive systems is environment awareness which enables agile and adaptive use of channel resources. Whitespace prediction based on learning the statistics of the wireless traffic has proven to be a powerful tool to achieve such awareness. In this paper, we propose a novel Hidden Markov Model (HMM) based spectrum learning and prediction approach which accurately estimates the exact length of the whitespace in WiFi channels within the shared industrial scientific medical ISM) bands. We show that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification leads to a substantial increase in the prediction horizon compared to classical approaches that predict the immediate (short-term) future. We verify the proposed algorithm through simulations which utilize a model for WiFi traffic based on extensive measurement campaigns.
Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition
(2018)
Deep learning is a field of machine learning that has been the focus of active research and successful applications in recent years. Offline handwriting recognition is one of the research fields and applications were deep neural networks have shown high accuracy. Deep learning models and their training pipeline show a large amount of hyper-parameters in their data selection, transformation, network topology and training process that are sometimes interdependent. This increases the overall difficulty and time necessary for building and training a model for a specific data set and task at hand. This work proposes a novel visualization-assisted workflow that guides the model developer through the hyper-parameter search in order to identify relevant parameters and modify them in a meaningful way. This decreases the overall time necessary for building and training a model. The contributions of this work are a workflow for hyper-parameter search in offline handwriting recognition and a heat map based visualization technique for deep neural networks in multi-line offline handwriting recognition. This work applies to offline handwriting recognition, but the general workflow can possibly be adapted to other tasks as well.
Motion estimation is an essential element for autonomous vessels. It is used e.g. for lidar motion compensation as well as mapping and detection tasks in a maritime environment. Because the use of gyroscopes is not reliable and a high performance inertial measurement unit is quite expensive, we present an approach for visual pitch and roll estimation that utilizes a convolutional neural network for water segmentation, a stereo system for reconstruction and simple geometry to estimate pitch and roll. The algorithm is validated on a novel, publicly available dataset recorded at Lake Constance. Our experiments show that the pitch and roll estimator provides accurate results in comparison to an Xsens IMU sensor. We can further improve the pitch and roll estimation by sensor fusion with a gyroscope. The algorithm is available in its implementation as a ROS node.
When mobile devices at the network edge want to communicate with each other, they too often depend on the availability of faraway resources. For direct communication, feasible user-friendly service discovery is essential. DNS Service Discovery over Multicast DNS (DNS-SD/mDNS) is widely used for configurationless service discovery in local networks, due inno small part to the fact that it is based on the well establishedDNS, and efficient in small networks. In our research, we enhance DNS-SD/mDNS providing versatility, user control, efficiency, and privacy, while maintaining the deployment simplicity and backward compatibility. These enhancements are necessary to make it a solid, flexible foundationfor device communication in the edge of the Internet. In this paper, we focus on providing multi-link capabilities and scalable scopes for DNS-SD while being mindful of both user-friendliness and efficiency. We propose DNS-SD over StatelessDNS (DNS-SD/sDNS), a solution that allows configurationless service discovery in arbitrary self-named scopes - largely independentof the physical network layout - by leveraging ourStateless DNS technique and the Raft consensus algorithm.
Ballistocardiography (BCG) can be used to monitor heart rate activity. Besides, the accelerometer should have high sensitivity and minimal internal noise; a low-cost approach was taken into consideration. Several measurements have been executed to determine the optimal positioning of a sensor under the mattress to obtain a signal strong enough for further analysis. A prototype for an unobtrusive accelerometer-based measurement system has been developed and tested in a conventional bed without any specific extras. The influence of the human sleep position for the output accelerometer data was tested. The obtained results indicate the potential to capture BCG signals using accelerometers. The measurement system can detect heart rate in an unobtrusive form in the home environment.
This article describes a research project that aims at investigating individual entrepreneurial founders concerning their shift tendencies of decision-making logics - especially during the respective phases of the venture creating process. Prior studies found that team founders show a hybrid perspective on strategic decision-making. They not only combine causation (planning-based) and effectuation (flexible) logics but also show logic shifts and also re-shifts over time. Due to the fact, that founders' social identity shapes early structuring processes, this article describes the necessity of elimination of in-group influences of multi-founding ventures and focus on individuals in order to make specific assessments on logic shifts and re-shifts. Based on an extensive literature review, a pre-selection-test and a qualitative case study design from the empirical body of the paper. Insofar, this study applies a qualitative design of a process research approach to investigate shifts of decision-making logics of individual founders in new venture creation over time.
Corporate Entrepreneurship (CE) has now evolved into an imperative innovation practice of established companies. Despite organizational design models for CE activities and companies' frequent initiation of new activities, effectively managing them remains a challenging endeavor which results in disappointment about the outcomes of CE and its early termination. We assume specific types of goals for CE as one element of this unresolved management issue. While both practice and literature address goals in different contexts, no uniform picture has emerged so far. Although goals are commonly used to categorize CE activities, they seldomly seem to be the core subject of investigation. Based on this preliminary analysis and consolidation, we put the goals of CE in focus. In a systematic literature review, we reveal aspects of goals to unmask the different types of goals and their underlying dimensions and characteristics. Our review contributes to a better understanding of goals by (1) organizing relevant literature on goals of CE in a specific classification process, (2) describing dimensions and attributes for a systematic classification of CE goals; and (3) providing a framework showing differences of goals for the CE context. We conclude with a discussion and hints for future research paths.
Low-Code Development Platforms (LCDPs) enable non-information technology (IT) personnel to develop applications and workflows independently of the IT department. Consequently, these digital platforms help to overcome the growing need for software development. However, science and practice warn of several barriers that slow down or hinder the usage of LCDPs. This publication scientifically identifies, analyzes, and discusses challenges during implementation and application of LCDPs from both perspectives in a holistic manner. Therefore, we conduct an exploratory study (data from scientific literature, expert interviews, and practical studies) and assign the challenges to the socio-technical system model. The results show that the scientific and practical communities recognize common challenges (especially knowledge transfer) but also perceive differences related to technological (science) and social (practice) aspects. This paper proposes future research directions for academia, such as governance, culture change, and value evaluation of LCDPs. Additionally, practitioners can prepare for possible challenges when using LCPDs.
TU Darmstadt HUMVIB-Bridge
(2016)
The simulation of the human-induced vibrations of lightweight footbridges is in general a complex problem where the dynamics of the pedestrian system meets the structural dynamics of the bridge. However, standard methods for numerical analysis of pedestrian bridges deal with this issue by using simplified approaches. The structure is mostly represented either by discretised multi mass systems or through a formulation in modal coordinates, while the excitation is typically described by a moving load.
Positive effects of the interaction between the two systems (pedestrian and structure) are usually completely neglected. This paper, which is partially
extracted from an actual research report of the Institute of Structural Mechanics and Design (TU Darmstadt), presents an experimental set-up developed for investigations of the human-structure interaction (HSI), as well as results of the preliminary investigations carried out in the same context.
This paper summarizes the trends in metallization and interconnection technology in the eyes of the participants of the 8th Metallization and Interconnection Workshop. Participants were asked in a questionnaire to share their view on the future development of metallization technology, the kind of metal used for front side metallization and the future development of interconnection technology. The continuous improvement of the screen-printing technology is reflected in the high expected percentage share decreasing from 88% in three years to still 70% in ten years. The dominating front side metal in the view of the participants will be silver with an expected percentage share of nearly 70% in 2029. Regarding interconnection technologies, the experts of the workshop expect new technologies to gain significant technology shares faster. Whereas in three years soldering on busbars is expected to dominate with a percentage share of 71% it will drop in ten years to 35% in the eyes of the participants. Multiwire and shingling technologies are seen to have the highest potential with expected percentage shares of 33% (multiwire) and 16% (shingling) in ten years.
The trajectory tracking problem for a real-scaled fully-actuated surface vessel is addressed in this paper. A nonlinear model predictive control (NMPC) scheme was designed to track a reference trajectory, considering state and input constraints, and environmental disturbances, which were assumed to be constant over the prediction horizon. The controller was tested by performing docking maneuvers using the real-scaled research vessel from the University of Applied Sciences Konstanz at the Rhine river in Germany. A comparison between the experimental results and the simulated ones was analyzed to validate the NMPC controller.
Trajectory Tracking of a Fully-actuated Surface Vessel using Nonlinear Model Predictive Control
(2021)
The trajectory tracking problem for a fully-actuated real-scaled surface vessel is addressed in this paper. The unknown hydrodynamic and propulsion parameters of the vessel’s dynamic model were identified using an experimental maneuver-based identification process. Then, a nonlinear model predictive control (NMPC) scheme is designed and the controller’s performance is assessed through the variation of NMPC parameters and constraints tightening for tracking a curved trajectory.
This paper presents the integration of a spline based extension model into a probability hypothesis density (PHD) filter for extended targets. Using this filter the position and extension of each object as well as the number of present objects can jointly be estimated. Therefore, the spline extension model and the PHD filter are addressed and merged in a Gaussian mixture (GM) implementation. Simulation results using artificial laser measurements are used to evaluate the performance of the presented filter. Finally, the results are illustrated and discussed.
Virtual measurement models (VMM) can be used to generate artificial measurements and emulate complex sensor models such as Lidar. The input of the VMM is an estimation and the output is the set of measurements this estimation would cause. A Kalman filter with extension estimation based on random matrices is used to filter mean and covariance of the real measurements. If these match the mean and covariance of the artificial measurements, then the given estimation is appropriate. The optimal input of the VMM is found using an adaptation algorithm. In this paper, the VMM approach is expanded for multi-extended object tracking where objects can be occluded and are only partially visible. The occlusion can be compensated if the extension estimation is performed for all objects together. The VMM now receives as input an estimation for the multi-object state and the output are the measurements that this multi-object state would cause.