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An apparatus and method for analyzing a flow of material having an inlet region, a measurement range and an outlet region, and having a first diverter and a second diverter, and a deflection area, wherein in a first state of operation, the two diverters form a continuous first material flow space from the inlet region via the first diverter through the measurement range, via the second diverter to the outlet region, and in a second state of operation, form a continuous second material flow space from the inlet region via the first diverter through the deflection area, via the second diverter to the outlet region.
In the last 20 years there have been major advances in autonomous robotics. In IoT (Industry 4.0), mobile robots require more intuitive interaction possibilities with humans in order to expand its field of applications. This paper describes a user-friendly setup, which enables a person to lead the robot in an unknown environment. The environment has to be perceived by means of sensory input. For realizing a cost and resource efficient Follow Me application we use a single monocular camera as low-cost sensor. For efficient scaling of our Simultaneous Localization and Mapping (SLAM) algorithm, we integrate an inertial measurement unit (IMU) sensor. With the camera input we detect and track a person. We propose combining state of the art deep learning with Convolutional Neural Network (CNN) and SLAM algorithms functionality on the same input camera image. Based on the output robot navigation is possible. This work presents the specification, workflow for an efficient development of the Follow Me application. Our application’s delivered point clouds are also used for surface construction. For demonstration, we use our platform SCITOS G5 equipped with the afore mentioned sensors. Preliminary tests show the system works robustly in the wild.
SLAM systems are mainly applied for robot navigation while research on feasibility for motion planning with SLAM for tasks like bin-picking, is scarce. Accurate 3D reconstruction of objects and environments is important for planning motion and computing optimal gripper pose to grasp objects. In this work, we propose the methods to analyze the accuracy of a 3D environment reconstructed using a LSD-SLAM system with a monocular camera mounted onto the gripper of a collaborative robot. We discuss and propose a solution to the pose space conversion problem. Finally, we present several criteria to analyze the 3D reconstruction accuracy. These could be used as guidelines to improve the accuracy of 3D reconstructions with monocular LSD-SLAM and other SLAM based solutions.
We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the time up and go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used dynamic time warping and hierarchical custering to distinguish the stadiums. A specificity of 92% was achieved.
Research and Development (R&D) is crucial for the growth and future success of research-based pharma companies. To maintain their R&D organisations efficient, pharmaceutical companies started to hedge the potential of open innovation to cut R&D costs and to access external knowledge. These new strategies could be divided into several categories: open source, innovation centres, crowd sourcing and virtual R&D.
Fitting 3D Morphable Face Models (3DMM) to a 2D face image allows the separation of face shape from skin texture, as well as correction for face expression. However, the recovered 3D face representation is not readily amenable to processing by convolutional neural networks (CNN). We propose a conformal mapping from a 3D mesh to a 2D image, which makes these machine learning tools accessible by 3D face data. Experiments with a CNN based face recognition system designed using the proposed representation have been carried out to validate the advocated approach. The results obtained on standard benchmarking data sets show its promise.
New approaches to respiratory assist: bioengineering an ambulatory, miniaturized bioartificial lung
(2019)
Although state-of-the-art treatments of respiratory failure clearly have made some progress in terms of survival in patients suffering from severe respiratory system disorders, such as acute respiratory distress syndrome (ARDS), they failed to significantly improve the quality of life in patients with acute or chronic lung failure, including severe acute exacerbations of chronic obstructive pulmonary disease or ARDS as well. Limitations of standard treatment modalities, which largely rely on conventional mechanical ventilation, emphasize the urgent, unmet clinical need for developing novel(bio)artificial respiratory assist devices that provide extracorporeal gas exchange with a focus on direct extracorporeal CO2 removal from the blood. In this review, we discuss some of the novel concepts and critical prerequisites for such respiratory lung assist devices that can be used with an adequate safety profile, in the intensive care setting, as well as for long-term domiciliary therapy in patients with chronic ventilatory failure. Specifically, we describe some of the pivotal steps, such as device miniaturization, passivation of the blood-contacting surfaces by chemical surface modifications, or endothelial cell seeding, all of which are required for converting current lung assist devices into ambulatory lung assist device for long-term use in critically ill patients. Finally, we also discuss some of the risks and challenges for the long-term use of ambulatory miniaturized bioartificial lungs.
For the widespread establishment of a circular economy, the acceptance of used products among consumers is a prerequisite. This paper investigates the customer experience of product service systems related to used products (PSSuP), such as renting, remanufacturing, and second-hand models, and aims to point out the offering characteristics that effect customer response and customer engagement. This study was conducted by means of a content analysis-based literature review of 69 empirical PSSuP studies. A frequency analysis of the categories that determine customer experience creation was conducted, as well as a contingency analysis to reveal the interrelationship between these categories. On this basis, the different PSSuP types were compared, and four strategic orientations of customer experience creation in PSSuP are pointed out: price, confidence, convenience, and delight orientation. For each of these strategic orientations, supportive PSSuP offering characteristics are specified. Building on the findings of this study, theoretical and managerial implications for product–service systems marketing are pointed out, and the need for research on the role of information and communication technology as an enabler of customer experience creation in PSSuP is highlighted.
In this exploratory research eight suppliers in the automotive industry are interviewed to measure the application of supply chain finance instruments in their supply chain in the Netherlands and the region of South-West Germany. Current adoption levels and reasons for non adoption are discussed. Based on these indicative results, a set of hypotheses is suggested for further research. The theoretical base of this study is a conceptual model of Supply Chain Finance based on literature research and empirical research in the Netherlands.
Early reduction of risks in a startup or an innovation project is highly important. Appropriate means for risk reduction, such as testing business models with different kinds of experiments exist. However, deciding what to test and how to select the right test, is challenging for many startups and innovation projects. This article presents the so-called Business Experiments Navigator (BEN), a toolkit to assist startup and innovation processes. It compliments other tools such as the Business Model Canvas or the Lean Startup process. The main contribution of BEN is to bridge the gap between the riskiest assumptions of a business model and the multitude of available testing techniques by providing assumption templates. The Business Experiments Navigator has been validated in several workshops. Results show that it creates awareness among the workshop participants that a business model is based on assumptions which impose risks and need to be validated. Further, users of BEN were able to identify relevant assumptions and map different kinds of assumptions to appropriate testing techniques. The process applied in the workshops, as well as the assumption templates, helped the participants understand the main concepts and transfer their learnings, to their own business ideas.
