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Generalized concatenated (GC) codes with soft-input decoding were recently proposed for error correction in flash memories. This work proposes a soft-input decoder for GC codes that is based on a low-complexity bit-flipping procedure. This bit-flipping decoder uses a fixed number of test patterns and an algebraic decoder for soft-input decoding. An acceptance criterion for the final candidate codeword is proposed. Combined with error and erasure decoding of the outer Reed-Solomon codes, this bit-flipping decoder can improve the decoding performance and reduce the decoding complexity compared to the previously proposed sequential decoding. The bit-flipping decoder achieves a decoding performance similar to a maximum likelihood decoder for the inner codes.
Advanced approaches for analysis and form finding of membrane structures with finite elements
(2018)
Part I deals with material modelling of woven fabric membranes. Due to their structure of crossed yarns embedded in coating, woven fabric membranes are characterised by a highly nonlinear stress-strain behaviour. In order to determine an accurate structural response of membrane structures, a suitable description of the material behaviour is required. A linear elastic orthotropic model approach, which is current practice, only allows a relative coarse approximation of the material behaviour. The present work focuses on two different material approaches: A first approach becomes evident by focusing on the meso-scale. The inhomogeneous, however periodic structure of woven fabrics motivates for microstructural modelling. An established microstructural model is considered and enhanced with regard to the coating stiffness. Secondly, an anisotropic hyperelastic material model for woven fabric membranes is considered. By performing inverse processes of parameter identification, fits of the two different material models w.r.t. measured data from a common biaxial test are shown. The results of the inversely parametrised material models are compared and discussed.
Part II presents an extended approach for a simultaneous form finding and cutting patterning computation of membrane structures. The approach is formulated as an optimisation problem in which both the geometries of the equilibrium and cutting patterning configuration are initially unknown. The design objectives are minimum deviations from prescribed stresses in warp and fill direction along with minimum shear deformation. The equilibrium equations are introduced into the optimisation problem as constraints. Additional design criteria can be formulated (for the geometry of seam lines etc.). Similar to the motivation for the Updated Reference Strategy [4] the described problem is singular in the tangent plane. In both the equilibrium and the cutting patterning configuration finite element nodes can move without changing stresses. Therefore, several approaches are presented to stabilise the algorithm. The overall result of the computation is a stressed equilibrium and an unstressed cutting patterning geometry. The interaction of both configurations is described in Total Lagrangian formulation.
The microstructural model, which is focused in Part I, is applied. Based on this approach, information about fibre orientation as well as the ending of fibres at cutting edges are available. As a result, more accurate results can be computed compared to simpler approaches commonly used in practice.
Sleep study can be used for detection of sleep quality and in general bed behaviors. These results can helpful for regulating sleep and recognizing different sleeping disorders of human. In comparison to the leading standard measuring system, which is Polysomnography (PSG), the system proposed in this work is a non-invasive sleep monitoring device. For continuous analysis or home use, the PSG or wearable Actigraphy devices tends to be uncomfortable. Besides, these methods not only decrease practicality due to the process of having to put them on, but they are also very expensive. The system proposed in this paper classifies respiration and body movement with only one type of sensor and also in a noninvasive way. The sensor used is a pressure sensor. This sensor is low cost and can be used for commercial proposes. The system was tested by carrying out an experiment that recorded the sleep process of a subject. These recordings showed excellent results in the classification of breathing rate and body movements.
This paper presents a bed system able to analyze a person’s movement, breathing and recognize the positions that the subject is lying on the bed during the night without any additional physical contact. The measurements are performed with sensors placed between the mattress and the bed-frame. An Intel Edison board was used as an endpoint that served as a communication node from the mesh network to external service. Two nodes and Intel Edison are attached to the bottom of the bed frame and they are connected to the sensors. First test results have indicated the potential of the proposed approach for the recognition of sleep positions with 83% of correct recognized positions.
The process of restoring our body and brain from fatigue is directly depend-ing on the quality of sleep. It can be determined from the report of the sleep study results. Classification of sleep stages is the first step of this study and this includes the measurement of biovital data and its further processing.
In this work, the sleep analysis system is based on a hardware sensor net, namely a grid of 24 pressure sensors, supporting sleep phase recognition. In comparison to the leading standard, which is polysomnography, the proposed approach is a non-invasive system. It recognises respiration and body move-ment with only one type of low-cost pressure sensors forming a mesh archi-tecture. The nodes implement as a series of pressure sensors connected to a low-power and performant microcontroller. All nodes are connected via a system wide bus with address arbitration. The embedded processor is the mesh network endpoint that enables network configuration, storing and pre-processing of the data, external data access and visualization.
The system was tested by executing experiments recording the sleep of different healthy young subjects. The results obtained have indicated the po-tential to detect breathing rate and body movement. A major difference of this system in comparison to other approaches is the innovative way to place the sensors under the mattress. This characteristic facilitates the continuous using of the system without any influence on the common sleep process.
Corporate venturing is one way for corporations to
introduce strategic renewal into their business portfolios, which is
imperative for ongoing success in innovation-driven industries.
Prior research finds that corporate ventures should be separated
from the mainstream business in loosely coupled sub-units, but
scholars continue to discuss how loose or tight the ventures should
be to balance exploration and exploitation. Hence, the antecedents
for successful venture management are yet to be fully explored and
our study contributes to this effort. The study shows that
corporate venture success is enhanced when corporate
management grants job and strategic autonomy to the venture
managers. This is further amplified when corporate management
simultaneously imposes an exploitative policy that forces venture
managers to prioritize extensions to and improvements of existing
competences and product-market offerings.
This paper focuses on the multivariable control of a drawing tower process. The nature of the process together with the differences in measurement noise levels that affect the variables to be controlled motivated the development of a new MPC algorithm. An extension of a multivariable predictive control algorithm with separated prediction horizons is proposed. The obtained experimental results show the usefulness of the proposed algorithm..
Ingenieure auf die Bühne
(2018)
Optical surface inspection: A novelty detection approach based on CNN-encoded texture features
(2018)
In inspection systems for textured surfaces, a reference texture is typically known before novel examples are inspected. Mostly, the reference is only available in a digital format. As a consequence, there is no dataset of defective examples available that could be used to train a classifier. We propose a texture model approach to novelty detection. The texture model uses features encoded by a convolutional neural network (CNN) trained on natural image data. The CNN activations represent the specific characteristics of the digital reference texture which are learned by a one-class classifier. We evaluate our novelty detector in a digital print inspection scenario. The inspection unit is based on a camera array and a flashing light illumination which allows for inline capturing of multichannel images at a high rate. In order to compare our results to manual inspection, we integrated our inspection unit into an industrial single-pass printing system.
Simulationsmethoden bei der Entwicklung von Extremleichtbaukomponenten in Faserverbundbauweise
(2018)
The Role of Support-Activities for the successful Implementation of Internal Corporate Accelerators
(2018)
Business units are increasingly able to fuel the transformation that digitalization demands of organizations. Thereby, they implement Shadow IT (SIT) to create flexible and innovative solutions. However, the individual implementation of SIT leads to high complexities and redundancies. Integration suggests itself to meet these challenges but can also eliminate the described benefits. In this emergent research, we develop propositions for a conceptual decision framework, that balances the benefits and drawbacks of an integration of SIT using a literature review as well as a multiple-case study. We thereby integrate the perspective of the overall organization as well as the specific business unit. We then pose six propositions regarding SIT integration that will serve to evaluate our conceptual framework in future research.
Einfluss der Oberfläche
(2018)
When designing drying processes for sensitive biological foodstuffs like fruit or vegetables, energy and time efficiency as well as product quality are gaining more and more importance. These all are greatly influenced by the different drying parameters (e.g. air temperature, air velocity and dew point temperature) in the process. In sterilization of food products the cooking value is widely used as a cross-link between these parameters. In a similar way, the so-called cumulated thermal load (CTL) was introduced for drying processes. This was possible because most quality changes mainly depend on drying air temperature and drying time. In a first approach, the CTL was therefore defined as the time integral of the surface temperature of agricultural products. When conducting experiments with mangoes and pineapples, however, it was found that the CTL as it was used had to be adjusted to a more practical form. So the definition of the CTL was improved and the behaviour of the adjusted CTL (CTLad) was investigated in the drying of pineapples and mangoes. On the basis of these experiments and the work that had been done on the cooking value, it was found, that more optimization on the CTLad had to be done to be able to compare a great variety of different products as well as different quality parameters.
With the increased deployment of biometric authentication systems, some security concerns have also arisen. In particular, presentation attacks directed to the capture device pose a severe threat. In order to prevent them, liveness features such as the blood flow can be utilised to develop presentation attack detection (PAD) mechanisms. In this context, laser speckle contrast imaging (LSCI) is a technology widely used in biomedical applications in order to visualise blood flow. We therefore propose a fingerprint PAD method based on textural information extracted from pre-processed LSCI images. Subsequently, a support vector machine is used for classification. In the experiments conducted on a database comprising 32 different artefacts, the results show that the proposed approach classifies correctly all bona fides. However, the LSCI technology experiences difficulties with thin and transparent overlay attacks.
SDG Voyager - A practical guide to align business excellence with Sustainable Development Goals
(2018)
By now, an inflationary high number of international publications on the topic “Agenda 2030” exist. But unanswered to this day seems to be the question of how the CSR-management of a company can make a concrete contribution to the SDGs. Instead of unilaterally demanding the reporting of companies’ sustainability activities, the SDG Voyager starts earlier in the process with the intention of encouraging companies of all sizes to become familiar with the fields of action for corporate responsibility and to attend to these issues without feeling overwhelmed. Many companies will find that they are already making a big contribution to sustainable development in a number of fields. In other areas, however, there will still be an urgent need for action. The SDG Voyager aims to acquaint companies with these topics and support them to fulfill their responsibilities towards their stakeholders and society.
Der DEX Deutscher Ethik Index® gibt Auskunft darüber, ob der Erfolg eines Unternehmens auf anständige Weise erreicht wurde. Zielgruppe des veröffentlichten DEX sind alle Anspruchsgruppen (Stakeholder) und die Gesellschaft. Der DEX informiert darüber, ob das Unternehmen einen Mehrwert für alle seine Stakeholder schafft (= Stakeholder Value). Der DEX unterstützt Entscheidungen der Öffentlichkeit, ob einer Organisation aufgrund ihres Beitrags für die Gesellschaft die „License to Operate“ erteilt wird (= Shared Value).
Research on Shadow IT is facing a conceptual dilemma in cases where previously "covert" systems developed by business entities (individual users, business workgroups, or business units) are integrated in the organizational IT management. These systems become visible, are therefore not "in the shadows" anymore, and subsequently do not fit to existing definitions of Shadow IT. Practice shows that some information systems share characteristics of Shadow IT, but are created openly in alignment with the IT department. This paper therefore proposes the term "Business-managed IT" to describe "overt" information systems developed or managed by business entities. We distinguish Business-managed IT from Shadow IT by illustrating case vignettes. Accordingly, our contribution is to suggest a concept and its delineation against other concepts. In this way, IS researchers interested in IT originated from or maintained by business entities can construct theories with a wider scope of application that are at the same time more specific to practical problems. In addition, value-laden terminology is complemented by a vocabulary that values potentially innovative developments by business entities more adequately. From a practical point of view, the distinction can be used to discuss the distribution of task responsibilities for information systems.
When integrating task-based learning into LSP courses, assessment procedures have to reflect the communicative goals of such tasks. However, performances on authentic and complex tasks are often difficult to score, because they involve not only specific language ability but also field specific knowledge. Moreover, the emphasis of tasks on meaning suggests that the communicative outcome should be regarded as the main criterion of success. This paper focuses on task design and scoring. It is argued that tasks have to be adapted to assessment purposes. However, to avoid a misalignment between teaching/learning and assessment, they should comprise the main features of LSP tasks in the sense of task-based language learning.
Product development and product manufacturing are entering a new era, namely an era where engineering tasks are executed under collaboration of all involved parties. Engineers and potential customers work together mainly in a virtual world for the design and realization of the product. We address this so called “crowdsourcing” trend in the automotive industry that lowers cost and accelerates production of new car. Current practice and prior studies fail to handle data management and collaboration aspects in sufficient detail. We propose a PLM based crowdsourcing platform that applies best practices to the established approach and adapt it with new methods for handling specific requirements. Our work provides a basis for establishing an improved collaboration platform to support a Gig Economy in the automotive industry.
Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) for image inpainting of large regions in high-resolution textures. Due to limited computational resources processing high-resolution images with neural networks is still an open problem. Existing methods separate inpainting of global structure and the transfer of details, which leads to blurry results and loss of global coherence in the detail transfer step. Based on advances in texture synthesis using CNNs we propose patch-based image inpainting by a single network topology that is able to optimize for global as well as detail texture statistics. Our method is capable of filling large inpainting regions, oftentimes exceeding quality of comparable methods for images of high-resolution (2048x2048px). For reference patch look-up we propose to use the same summary statistics that are used in the inpainting process.
In this paper we present a method using deep learning to compute parametrizations for B-spline curve approximation. Existing methods consider the computation of parametric values and a knot vector as separate problems. We propose to train interdependent deep neural networks to predict parametric values and knots. We show that it is possible to include B-spline curve approximation directly into the neural network architecture. The resulting parametrizations yield tight approximations and are able to outperform state-of-the-art methods.
It is widely recognized that sustainability is a new challenge for many manufacturing companies. In this paper, we tackle this issue by presenting an approach that deals with material and substance compliance within Product Lifecycle Management in a complex value chain. Our analysis explains why, how and when sustainable manufacturing arises, and it identifies, quantifies and evaluates the environmental impact of a new product. We propose (I) a Life Cycle Assessment tool (LCA) and (II) a model to validate this approach and evaluate the risk of noncompliance in supply chain. Our LCA approach provides comprehensive information on environmental impacts of a product.
Product and materials cycles are parallel and intersecting, making it challenging to integrate Material Selection Process across Product Lifecycle Management, Integration of LCA with PLM. We provide only a foundation. Further research in systems engineering is necessary. LCA is sensitive to data quality. Outsourcing production and having problems in supplier cooperation can result in material mismatch (such as property, composition mismatching) in the production process due to that may cause misleading of LCA results.
This paper also describes research challenges using riskbased due diligence.
It is widely recognized that sustainability is a new challenge for many manufacturing companies. In this paper, we tackle this issue by presenting an approach that deals with material and substance compliance within Product Lifecycle Management in a complex value chain. Our analysis explains why, how and when sustainable manufacturing arises, and it identifies, quantifies and evaluates the environmental impact of a new product. We propose (I) a Life Cycle Assessment tool (LCA) and (II) a model to validate this approach and evaluate the risk of non-compliance in supply chain. Our LCA approach provides comprehensive information on environmental impacts of a product.
Product and materials cycles are parallel and intersecting, making it challenging to integrate Material Selection Process across Product Lifecycle Management, Integration of LCA with PLM. We provide only a foundation. Further research in systems engineering is necessary. LCA is sensitive to data quality. Outsourcing production and having problems in supplier cooperation can result in material mismatch (such as property, composition mismatching) in the production process due to that may cause misleading of LCA results. This paper also describes research challenges using risk-based due diligence.
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters k, and for each 1≤k≤kmax, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this “learning to cluster” and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.