Refine
Year of publication
- 2021 (154) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (53)
- IfB - Institut für Bioengineering (35)
- Fachbereich Elektrotechnik und Informationstechnik (25)
- Fachbereich Luft- und Raumfahrttechnik (23)
- Fachbereich Energietechnik (17)
- INB - Institut für Nano- und Biotechnologien (15)
- Fachbereich Chemie und Biotechnologie (11)
- Fachbereich Bauingenieurwesen (10)
- Solar-Institut Jülich (10)
- ECSM European Center for Sustainable Mobility (9)
Language
- English (154) (remove)
Document Type
- Article (86)
- Conference Proceeding (48)
- Part of a Book (12)
- Book (2)
- Doctoral Thesis (2)
- Conference: Meeting Abstract (1)
- Other (1)
- Preprint (1)
- Working Paper (1)
Keywords
- Hydrogen (2)
- NOx emissions (2)
- Out-of-plane load (2)
- PCM (2)
- Principal component analysis (2)
- autonomous driving (2)
- building information modelling (2)
- capacitive field-effect sensor (2)
- constructive alignment (2)
- earthquakes (2)
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is
required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA.
Subject of this case is Deutsche Telekom Services Europe (DTSE), a service center for administrative processes. Due to the high volume of repetitive tasks (e.g., 100k manual uploads of offer documents into SAP per year), automation was identified as an important strategic target with a high management attention and commitment. DTSE has to work with various backend application systems without any possibility to change those systems. Furthermore, the complexity of administrative processes differed. When it comes to the transfer of unstructured data (e.g., offer documents) to structured data (e.g., MS Excel files), further cognitive technologies were needed.
Robotic process automation (RPA) has attracted increasing attention in research and practice. This chapter positions, structures, and frames the topic as an introduction to this book. RPA is understood as a broad concept that comprises a variety of concrete solutions. From a management perspective RPA offers an innovative approach for realizing automation potentials, whereas from a technical perspective the implementation based on software products and the impact of artificial intelligence (AI) and machine learning (ML) are relevant. RPA is industry-independent and can be used, for example, in finance, telecommunications, and the public sector. With respect to RPA this chapter discusses definitions, related approaches, a structuring framework, a research framework, and an inside as well as outside architectural view. Furthermore, it provides an overview of the book combined with short summaries of each chapter.
The benefits of robotic process automation (RPA) are highly related to the usage of commercial off-the-shelf (COTS) software products that can be easily implemented and customized by business units. But, how to find the best fitting RPA product for a specific situation that creates the expected benefits? This question is related to the general area of software evaluation and selection. In the face of more than 75 RPA products currently on the market, guidance considering those specifics is required. Therefore, this chapter proposes a criteria-based selection method specifically for RPA. The method includes a quantitative evaluation of costs and benefits as well as a qualitative utility analysis based on functional criteria. By using the visualization of financial implications (VOFI) method, an application-oriented structure is provided that opposes the total cost of ownership to the time savings times salary (TSTS). For the utility analysis a detailed list of functional criteria for RPA is offered. The whole method is based on a multi-vocal review of scientific and non-scholarly literature including publications by business practitioners, consultants, and vendors. The application of the method is illustrated by a concrete RPA example. The illustrated
structures, templates, and criteria can be directly utilized by practitioners in their real-life RPA implementations. In addition, a normative decision process for selecting RPA alternatives is proposed before the chapter closes with a discussion and outlook.
Intelligent autonomous software robots replacing human activities and performing administrative processes are reality in today’s corporate world. This includes, for example, decisions about invoice payments, identification of customers for a marketing campaign, and answering customer complaints. What happens if such a software robot causes a damage? Due to the complete absence of human activities, the question is not trivial. It could even happen that no one is liable for a damage towards a third party, which could create an uncalculatable legal risk for business partners. Furthermore, the implementation and operation of those software robots involves various stakeholders, which result in the unsolvable endeavor of identifying the originator of a damage. Overall it is advisable to all involved parties to carefully consider the legal situation. This chapter discusses the liability of software robots from an interdisciplinary perspective. Based on different technical scenarios the legal aspects of liability are discussed.
A new functionalization method to modify capacitive electrolyte–insulator–semiconductor (EIS) structures with nanofilms is presented. Layers of polyallylamine hydrochloride (PAH) and graphene oxide (GO) with the compound polyaniline:poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PANI:PAAMPSA) are deposited onto a p-Si/SiO2 chip using the layer-by-layer technique (LbL). Two different enzymes (urease and penicillinase) are separately immobilized on top of a five-bilayer stack of the PAH:GO/PANI:PAAMPSA-modified EIS chip, forming a biosensor for detection of urea and penicillin, respectively. Electrochemical characterization is performed by constant capacitance (ConCap) measurements, and the film morphology is characterized by atomic force microscopy (AFM) and scanning electron microscopy (SEM). An increase in the average sensitivity of the modified biosensors (EIS–nanofilm–enzyme) of around 15% is found in relation to sensors, only carrying the enzyme but without the nanofilm (EIS–enzyme). In this sense, the nanofilm acts as a stable bioreceptor onto the EIS chip improving the output signal in terms of sensitivity and stability.