Refine
Document Type
- Conference proceeding (31)
- Book (16)
- Book chapter (3)
- Journal article (2)
- Journal (1)
- Report (1)
- Working Paper (1)
Language
- English (55) (remove)
Is part of the Bibliography
- yes (55)
Institute
- Informatik (25)
- Texoversum (19)
- ESB Business School (9)
- Life Sciences (1)
- Technik (1)
Publisher
- Hochschule Reutlingen (55) (remove)
A MATLAB toolbox was developed both for teachers performing quick experimental demonstrations during lectures and for students practicing measurement and frequency analysis procedures. The conceptual purpose was to support fundamental acoustics courses with contents defined by the DEGA recommendation 102. All implemented functions and parameters are visible at once and quickly adjustable by a GUI without submenus. A user manual is provided with explanations of how to get started and how all implemented functions can be applied. The toolbox probably still contains bugs. All users are welcome to inform the author about their experiences and proposals for improvement. In future it is planned to convert "Acoustics" to the MATLAB app designer format as Mathworks announced GUIDE to be replaced. Useful extensions would be additional tabs containing animations of sound propagation phenomena or sound fields caused by different sources.
Type 1 diabetes is a chronic and a life threatening disease: an adjusted treatment and a proper management of the disease are crucial to prevent or delay the complications of diabetes. Although during the last decade the development of the artificial pancreas has presented great advances in diabetes care, the multiple daily injections therapy still represents the most widely used treatment option for type 1 diabetes. This work presents the proposal and first development stages of an application focused on guiding patients using the continuous glucose monitors and smart pens together with insulin and carbohydrates recommendations. Our proposal aims to develop a platform to integrate a series of innovative machine learning models and tools rigorously tested together with the use of the latest IoT devices to manage type 1 diabetes. The resulting system actually closes the loop, like the artificial pancreas, but in an intermittent way.
The following paper is dealing with the issue on which actual consumer lifestyle segmentation methods there are for particular European countries and accordingly for Europe as a whole. This is important for corporations to be able to place their products accurately by a consumer orientated marketing concerning the constant change of values and minds. Researching current literature, internet sources and documents, the state of the science is presented by a detailed description of the most popular lifestyle segmentation methods used in European countries. In addition to that, these instruments are discussed individually and then compared to each other. All instruments, the Sinus-Milieus, Euro-Socio-Styles, Roper-Consumer-Styles, RISC and Mosaic, are serving the same purpose even so they differ pretty much from each other. Each market research company has its own method to generate their model just as different segments and definitions for them. Furthermore every segmentation method is illustrated in a different way. This paper demonstrates all these instruments in detail and shows its advantages and disadvantages. Summing up literature research concerning the main research question, there are several models segmenting consumers in different lifestyle groups for e.g. in Germany, France or Great Britain, but still less models referring to the entire European market.
There are several intra-operative use cases which require the surgeon to interact with medical devices. I used the Leap Motion Controller as input device for three use-cases: 2D-interaction (e.g. advancing EPR data), selection of a value (e.g. room illumination brightness) and an application point and click scenario. I evaluated the Palm Mouse as the most suitable gesture solution to coordinate the mouse and advise to use the implementation using all fingers to perform a click. This small case study introduces the implementations and methods that result those recommendations.
Today many scientific works are using deep learning algorithms and time series, which can detect physiological events of interest. In sleep medicine, this is particularly relevant in detecting sleep apnea, specifically in detecting obstructive sleep apnea events. Deep learning algorithms with different architectures are used to achieve decent results in accuracy, sensitivity, etc. Although there are models that can reliably determine apnea and hypopnea events, another essential aspect to consider is the explainability of these models, i.e., why a model makes a particular decision. Another critical factor is how these deep learning models determine how severe obstructive sleep apnea is in patients based on the apnea-hypopnea index (AHI). Deep learning models trained by two approaches for AHI determination are exposed in this work. Approaches vary depending on the data format the models are fed: full-time series and window-based time series.
The aim of this paper is to show to what extent Artificial Intelligence can be used to optimize forecasting capability in procurement as well as to compare AI with traditional statistic methods. At the same time this article presents the status quo of the research project ANIMATE. The project applies Artificial Intelligence to forecast customer orders in medium-sized companies.
Precise forecasts are essential for companies. For planning, decision making and controlling. Forecasts are applied, e.g. in the areas of supply chain, production or purchasing. Medium-sized companies have major challenges in using suitable methods to improve their forecasting ability.
Companies often use proven methods such as classical statistics as the ARIMA algorithm. However, simple statistics often fail while applied for complex non-linear predictions.
Initial results show that even a simple MLP ANN produces better results than traditional statistic methods. Furthermore, a baseline (Implicit Sales Expectation) of the company was used to compare the performance. This comparison also shows that the proposed AI method is superior.
Until the developed method becomes part of corporate practice, it must be further optimized. The model has difficulties with strong declines, for example due to holidays. The authors are certain that the model can be further improved. For example, through more advanced methods, such as a FilterNet, but also through more data, such as external data on holiday periods.
It has not yet been possible to achieve the desired aim of decoupling economic growth from global material demand. Small and medium sized enterprises (SMEs) represent the backbone of most industrialized economies. Although material efficiency is of vital importance for many SMEs, few of them actually treat it as their top priority. There is a cornucopia of tools and methods available which can be used for material efficiency purposes. These, however, have gained little ground in the SME-field. This work deals with the enabling factors for material efficiency improvements in manufacturing SMEs and projections towards aspects of supply chain and circular economy. A multi-disciplinary decoupling approach for manufacturing SMEs and an implementation roadmap for further practical development are proposed. The approach combines appropriate complexity of technology and socio-economic considerations. It enables a connection of existing methods and the implementation of established information technologies.
Today many vertical retailers are operating different sales channels at the same time and are respon-sible for the range of products in all sales channels. The purpose of this paper is to examine whether for vertical fashion retailers a format-specific assortment policy can be observed on the German mar-ket. To investigate this topic in addition to secondary data of a literature research, quantitative prima-ry data was collected through a structured observation by conducting store checks. The combination provides insights into the research topic, allows to build hypotheses and to get a current and specific answer on the research topic. The study revealed all vertical retailers exploit the advantage of unlim-ited capacity of the online shop by offering in this channel mainly the broadest and deepest assort-ment. Within the retail store the vertical retailers focus on offering full-price goods for the current season in full size sets. Compared to the online shop here are less styles sophisticated presented and adjusted on the sales floor. For the outlet channel all brands showed a higher density of products and at least a price reduction of 30 per cent. The present paper is limited by time, depth and language of secondary data collection. As the study only conducted quantitative data within limited observations additional visual data over a longer period is necessary.
Purpose of the research paper is to illuminate the subject of assortment policy in the German fashion e‐commerce market. A short literature review is conducted in order to set up a system of characteristics to contemplate assortments on a strategic level. In a second step, structured observations are conducted to quantitatively analyze and compare the assortments of the leading online fashion retailers within Germany. Based on literature, the following characteristics for a classification of assortments can be identified: assortment structure, assortment size, assortment width, assortment depth, assortment consistency and rotation, price level, quality mix, fashion degree as well as the mix of private labels and manufacturer brands. Furthermore, the results of the empirical analysis show that there are currently five leaders within the nalyzed market: Amazon, Otto, Zalando, Baur and About You. Among these five market leaders, Amazon positions itself as a retailer that not only offers an enormous assortment size, but also the lowest entry prices as well as the broadest price dispersion. Through the development of the system of characteristics for assortment analysis and the examination of the current market environment, the findings of this paper contribute to the current state of the art in both theoretical and practical aspects.
This case study of Breuninger aims to analyze how Breuninger adapts to the emerging omnichannel environment in fashion business. From a consumer’s perspective Breuninger and the general omnichannel strategy of Breuninger is explained, before the loyalty program of Breuninger is analyzed in detail. Key factors as the mobile app and the mobile Breuninger card, social media, direct mail and in-store capabilities are described. A discussion chapter finalizes the case.