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Suche nach „[TC Grafenau]“ hat 71 Publikationen gefunden
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    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Heribert Popp, Leon Binder, Monica I. Ciolacu

    Enabling IoT in Education 4.0 with Biosensors from Wearables and Artificial Intelligence

    [Status: Paper accepted]

    Proceedings of the 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME) [Oct 23-26, 2019; Cluj-Napoca, Romania], New York, NY, USA

    2020

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, M. Aggarwal

    Modelling Human Decision Behaviour with Preference Learning

    INFORMS Journal on Computing, vol. 31, no. 2

    2019

    DOI: 10.1287/ijoc.2018.0823

    Abstract anzeigen

    Preferences provide a means for specifying the desires of a decision maker (DM) in a declarative way. In this paper, based on a DM’s pairwise preferences, we infer the DM’s unique decision model. We capture (a) the attitudinal character, (b) relative criteria importance, and (c) the criteria interaction, all of which are specific to the DM. We make use of the preference-learning (PL) technique to induce predictive preference models from empirical data. Because PL is emerging as a new subfield of machine learning, we could use standard machine-learning methods to accomplish our learning objective. We consider the DM’s exemplary preference information in the form of pairwise comparisons between alternatives as the training information. The DM’s decision model is captured in terms of (a), (b), and (c), through the parameters of an attitudinal Choquet integral operator. The proposed learning approach is validated through an experimental study on 16 standard data sets. The superiority of the proposed method in terms of predictive accuracy and easier interpretability is shown both theoretically as well as empirically.

    MobilAngewandte WirtschaftswissenschaftenTC Grafenau

    Zeitschriftenartikel

    Mohammed Alnahhal, Diane Ahrens

    A Simulation-Based System for Calculating Optimal Numbers of Forklift Drivers in Industrial Plants

    Bavarian Journal of Applied Sciences, vol. 4, no. 1, pp. 354-369

    2018

    DOI: 10.25929/bjas.v4i1.53

    Abstract anzeigen

    Dieser Artikel beschreibt eine Optimierungsmethode für ein Materialtransportsystem von Gabelstaplern mittels Warteschlangentheorie und Simulation. Ziel ist es, verschiedene Arten von Verschwendung bei den Kapazitätskosten, verspäteten Arbeitsaufträgen und Transportkosten zu reduzieren. Es wird eine gewisse ITInfrastruktur angenommen, wie etwa die Verwendung von Monitoren, um die aktuellen Arbeitsaufträge von verschiedenen Arbeitsplätzen anzuzeigen. Mathematische Gleichungen werden benutzt, um anfängliche obere und untere Grenzen für die benötigten Kapazitätsniveaus zu finden. Danach wird eine Simulation für verschiedene Kapazitätsniveaus innerhalb des Bereichs der theoretischen Ergebnisse durchgeführt, um die genau benötigte Mannzeit für verschiedene Jobsequenzierungsstrategien zu finden. Mit Hilfe der Statistiksoftware R wird ein Tool erstellt, welches Unternehmen für verschiedene Parameter Ergebnisse liefert. Diese Ergebnisse zeigen die Auswirkungen der Verwendung von Batching, unter Berücksichtigung der Begrenzung des Zeilenseitenraums und der Reduzierung der Leerfahrtstrategie für Leistungsmessungen. Die Strategie, das Leerfahren zu reduzieren, indem nach dem nächsten Arbeitsplatz gesucht wird, der einen Auftrag benötigt, ist nicht so effizient, da es die benötigte Kapazität erhöht. Dies liegt daran, dass es die Variabilität der Wartezeit vergrößert und somit den Prozentsatz der verspäteten Bestellungen steigert.

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Leon Binder, Ali Fallah Tehrani, P. M. Svasta, Monica I. Ciolacu

    Education 4.0 - Artificial Intelligence Assisted Higher Education: Early Recognition System with Machine Learning to Support Students' Success

    2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME)

    2018

    ISBN: 978-1-5386-5578-8

    DOI: 10.1109/SIITME.2018.8599203

    Abstract anzeigen

    Education 4.0 is being empowered more and more by artificial intelligence (AI) methods. We observe a continuously growing demand for adaptive and personalized Education. In this paper we present an innovative approach to promoting AI in Education 4.0. Our first contribution is AI assisted Higher Education Process with smart sensors and wearable devices for self-regulated learning. Secondly we describe our first results of Education 4.0 didactic methods implemented with learning analytics and machine learning algorithms. The aim of this case study is to predict the final score of students before participating in final examination. We propose an Early Recognition System equipped with real data captured in a blended learning course with a personalized test at the beginning of the semester, an adaptive learning environment based on Auto Tutor by N. A. Crowder theory with adaptive self-assessment feedback. It is obvious that focusing on students' success and their experiences is a win-win scenario for students and professors as well as for the administration.

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, Diane Ahrens

    Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression

    Artificial Intelligence for Fashion Industry in the Big Data Era, Singapore

    2018

    ISBN: 978-981-13-0079-0

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Zeitschriftenartikel

    Diane Ahrens

    Frauenau und Spiegelau werden digital

    Der Bayerische Bürgermeister, vol. 71, no. 7+8, pp. 290-293

    2018

    DigitalTC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Modeling Label Dependence for Multi-Label Classification Using the Choquistic Regression

    Pattern Recognition Letters, vol. 92, no. June, pp. 75-80

    2017

    DOI: 10.1016/j.patrec.2017.04.018

    Abstract anzeigen

    While an incorrect identification of underlying dependency in data can lead to a flawed conclusion, recognizing legitimate dependency allows for the opportunity to adapt a model in a correct manner. In this regard, modeling the inter-dependencies in multi-label classification (multi target prediction) is one of the challenging tasks from a machine learning point of view. While common approaches seek to exploit so-called correlated information from labels, this can be improved by assuming the interactions between labels. A well-known tool to model the interaction between attributes is the Choquet integral; it enables one to model non-linear dependencies between attributes. Beyond identifying proper prior knowledge in data (if such knowledge exists), establishing suitable models that are in agreement with prior knowledge is not always a trivial task. In this paper, we propose a first step towards modeling label dependencies for multi-target classifications in terms of positive and negative interactions. In the experimental, we demonstrate real gains by applying this approach.

    DigitalNachhaltigTC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Modified Sequential k‐means Clustering by Utilizing Response: A Case Study for Fashion Products

    Expert Systems, vol. 34, no. 6

    2017

    DOI: 10.1111/exsy.12226

    Abstract anzeigen

    Modified sequential k‐means clustering concerns a k‐means clustering problem in which the clustering machine utilizes output similarity in addition. While conventional clustering methods commonly recognize similar instances at features‐level modified sequential clustering takes advantage of response, too. To this end, the approach we pursue is to enhance the quality of clustering by using some proper information. The information enables the clustering machine to detect more patterns and dependencies that may be relevant. This allows one to determine, for instance, which fashion products exhibit similar behaviour in terms of sales. Unfortunately, conventional clustering methods cannot tackle such cases, because they handle attributes solely at the feature level without considering any response. In this study, we introduce a novel approach underlying minimum conditional entropy clustering and show its advantages in terms of data analytics. In particular, we achieve this by modifying the conventional sequential k‐means algorithm. This modified clustering approach has the ability to reflect the response effect in a consistent manner. To verify the feasibility and the performance of this approach, we conducted several experiments based on real data from the apparel industry.

    TC Grafenau

    Zeitschriftenartikel

    C. Dupke, C. Bonenfant, M. Ewald, M. Heurich, Robert Hable, B. Reineking, T. Zeppenfeld

    Habitat selection by a large herbivore at multiple spatial and temporal scales is primarily governed by food resources

    Ecography - Pattern and Process in Ecology, vol. 40, no. 8, pp. 1014-1027

    2017

    Abstract anzeigen

    Habitat selection can be considered as a hierarchical process in which animals satisfy their habitat requirements at different ecological scales. Theory predicts that spatial and temporal scales should co‐vary in most ecological processes and that the most limiting factors should drive habitat selection at coarse ecological scales, but be less influential at finer scales. Using detailed location data on roe deer Capreolus capreolus inhabiting the Bavarian Forest National Park, Germany, we investigated habitat selection at several spatial and temporal scales. We tested 1) whether time‐varying patterns were governed by factors reported as having the largest effects on fitness, 2) whether the trade‐off between forage and predation risks differed among spatial and temporal scales and 3) if spatial and temporal scales are positively associated. We analysed the variation in habitat selection within the landscape and within home ranges at monthly intervals, with respect to land‐cover type and proxys of food and cover over seasonal and diurnal temporal scales. The fine‐scale temporal variation follows a nycthemeral cycle linked to diurnal variation in human disturbance. The large‐scale variation matches seasonal plant phenology, suggesting food resources being a greater limiting factor than lynx predation risk. The trade‐off between selection for food and cover was similar on seasonal and diurnal scale. Habitat selection at the different scales may be the consequence of the temporal variation and predictability of the limiting factors as much as its association with fitness. The landscape of fear might have less importance at the studied scale of habitat selection than generally accepted because of the predator hunting strategy. Finally, seasonal variation in habitat selection was similar at the large and small spatial scales, which may arise because of the marked philopatry of roe deer. The difference is supposed to be greater for wider ranging herbivores.

    DigitalNachhaltigTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    A. Hübl, Gudrun Fischer

    Simulation-based business game for teaching methods in logistics and production

    Proceedings of the Winter Simulation Conference (WSC) 2017 (3-6 December, 2017; Las Vegas, NV, USA)

    2017

    DOI: 10.1109/WSC.2017.8248129

    Abstract anzeigen

    Uncertainty in planning tasks such as processing times, set-up times, customer required lead times, due dates, time to failure, time to repair and the complexity in terms of product variety, outsourcing, short lead times, low inventory levels, low costs and high utilization are major hurdles for planning logistics and production processes. This paper introduces a simulation-based business game for methods in planning logistics and production processes. Basic methods such as material requirement planning (MRP), Constant work in progress (Conwip), Kanban, reorder policies, dispatching rules, basic demand forecasting methods and master production schedule (MPS) are implemented in the game. Due to the generic environment additional methods can be implemented efficiently. The midterm planning concept sales and operations planning (S&OP) is implemented as well, where the gamers have to act as managers responsible for purchasing, production, sales and finance. Their target is to identify sales and production volumes for the next planning periods.

    DigitalInstitut ProtectITTC Grafenau

    Zeitschriftenartikel

    Karl Leidl, Robert Hable, Michael Fernandes, Nari Arunraj, Michael Heigl

    Comparison of Supervised, Semi-supervised and Unsupervised Learning Methods in Network Intrusion Detection Systems (NIDS) Application

    Anwendungen und Konzepte in der Wirtschaftsinformatik (AKWI), no. 6, pp. 10-19

    2017

    Abstract anzeigen

    With the emergence of the fourth industrial revolution (Industrie 4.0) of cyber physical systems, intrusion detection systems are highly necessary to detect industrial network attacks. Recently, the increase in application of specialized machine learning techniques is gaining critical attention in the intrusion detection community. A wide variety of learning techniques proposed for different network intrusion detection system (NIDS) problems can be roughly classified into three broad categories: supervised, semi-supervised and unsupervised. In this paper, a comparative study of selected learning methods from each of these three kinds is carried out. In order to assess these learning methods, they are subjected to investigate network traffic datasets from an Airplane Cabin Demonstrator. In addition to this, the imbalanced classes (normal and anomaly classes) that are present in the captured network traffic data is one of the most crucial issues to be taken into consideration. From this investigation, it has been identified that supervised learning methods (logistic and lasso logistic regression methods) perform better than other methodswhen historical data on former attacks are available. The results of this study have also showed that the performance of semi-supervised learning method (One class support vector machine) is comparatively better than unsupervised learning method (Isolation Forest) when historical data on former attacks are not available.

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Heribert Popp, Ali Fallah Tehrani, Monica I. Ciolacu, R. Beer

    Education 4.0 – Fostering Student's Performance with Machine Learning Methods

    Proceedings of the IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME) [Constanta, Romania; October 26th–29th, 2017]

    2017

    DOI: 10.1109/SIITME.2017.8259941

    MobilTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Christian Kluge, Stefan Schuster, Diana Sellner

    Statistics instead of Stopover

    Range Predictionc for Electric Vehicles

    Operations Research Proceedings 2016

    2017

    DigitalTC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression

    Journal of Retailing and Consumer Services, vol. 32, pp. 131-138

    2016

    Abstract anzeigen

    Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write off and increases the revenue. In fact, beyond of sales forecasting in general a crucial question remains whether a product may sell well or not. Assuming three classes as substantial, middle and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. Thereafter, we combine kernel machines with a probabilistic approach to empower the performance of kernel machines and eventually to make use of it to predicting the number of sales. The proposed approach is more robust to outliers (in the case of highly sold products) and in addition uses prior knowledge, hence it serves more reliable results. In order to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.

    DigitalTC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Supervised Regression Clustering: A Case Study for Fashion Products

    International Journal of Business Analytics (IJBAN), vol. 3, no. 4, pp. 21-40

    2016

    DOI: 10.4018/IJBAN.2016100102

    Abstract anzeigen

    Clustering techniques typically group similar instances underlying individual attributes by supposing that similar instances have similar attributes characteristic. On contrary, clustering similar instances given a specific behavior is framed through supervised learning. For instance, which fashion products have similar behavior in term of sales. Unfortunately, conventional clustering methods cannot tackle this case, since they handle attributes by a same manner. In fact, conventional clustering approaches do not consider any response, and moreover they assume attributes act by the same importance. However, clustering instances with respect to responses leads to a better data analytics. In this research, the authors introduce an approach for the goal supervised clustering and show its advantage in terms of data analytics as well as prediction. To verify the feasibility and the performance of this approach the authors conducted several experiments on a real dataset derived from an apparel industry.

    DigitalTC Grafenau

    Zeitschriftenartikel

    Michael Fernandes, Nari Arunraj, Diane Ahrens

    Application of SARIMAX model to forecast daily sales in retail industry

    International Journal of Operations Research and Information Systems (IJORIS), vol. 7, no. 2, pp. 1-20

    2016

    DOI: 10.4018/IJORIS.2016040101

    Abstract anzeigen

    Abstract During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Article Preview 1. Introduction Discount retail stores have been a noticeable feature of German retail market since the 1980s. In particular, the growth in number of discount retail stores have significantly increased after reunification of Germany. Recently, there is a growing trend of increasing varieties of fruits and vegetables with year-around availability across all the German discount retail outlets rather than just in their traditional growing season. In order to attract customers and remain competitive in the market, the fruits and vegetables are exported from foreign countries and stocked for longer periods. Particularly, increase in number of retail stores, availability of varieties of fruits and vegetables (in stock) with short shelf-lives, frequent price variations, and different storage conditions increase the complexity and results in huge amount of food waste. In Germany, the retail sector produces the food waste of around 0.5 million tons per year (Kranert et al., 2012). Although the retail sector contributes only 5% of the total food waste in food supply chain, mostly they are avoidable food waste (wasting food which is fit for consumption). The quantity of food waste that occurs in the home (61%) is partially due to the management decisions in the retail sector (e.g. frequent promotions) that stimulate the consumer’s eagerness to purchase, and distract them to equate their demand with the purchase (Arunraj et al., 2014; Gooch et al., 2010). Hence, the proper decision making in the retail sector can help the suppliers and consumers to avoid the food waste. The role of sales forecasting in reducing the food waste in retail stores is a significant topic of discussion in the recent food waste related studies (Mena et al., 2011; Mena et al., 2014). According to Mena et al. (2011) and Stenmarck et al. (2011), the improvement of forecast accuracy is one of the essential remedial measures to reduce the food waste in the retail sector of food supply chain.

    DigitalNachhaltigTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, Diane Ahrens

    Improved Forecasting and Purchasing of Fashion Products based on the Use of Big Data Techniques

    Supply Management Research, Wiesbaden

    2016

    ISBN: 9783658088088

    GesundTC Grafenau

    Zeitschriftenartikel

    Christian Kluge, W. Winklmayr, Küchenhoff H., A. Hartl, M. Steiner, M. Winklmayr, M. Ritter

    Radon balneotherapy and physical activity for osteoporosis prevention: a randomized, placebo-controlled intervention study

    Radiation and Environmental Biophysics, vol. 54, no. 1, pp. 123-136

    2015

    DOI: 10.1007/s00411-014-0568-z

    Abstract anzeigen

    Low-dose radon hyperthermia balneo treatment (LDRnHBT) is applied as a traditional measure in the non-pharmacological treatment of rheumatic diseases in Europe. During the last decades, the main approach of LDRnHBT was focused on the treatment of musculoskeletal disorders, but scientific evidence for the biological background of LDRnHBT is weak. Recently, evidence emerged that LDRnHBT influences bone metabolism. We investigated, whether combined LDRnHBT and exercise treatment has an impact on bone metabolism and quality of life in a study population in an age group at risk for developing osteoporosis. This randomized, double-blind, placebo-controlled trial comprised guided hiking tours and hyperthermia treatment in either radon thermal water (LDRnHBT) or radon-free thermal water (PlaceboHBT). Markers of bone metabolism, quality of life and somatic complaints were evaluated. Statistics was performed by linear regression and a linear mixed model analysis. Significant changes over time were observed for most analytes investigated as well as an improvement in self-assessed health in both groups. No significant impact from the LDRnHBT could be observed. After 6 months, the LDRnHBT group showed a slightly stronger reduction of the osteoclast stimulating protein receptor activator of nuclear kB-ligand compared to the PlaceboHBT group, indicating a possible trend. A combined hyperthermia balneo and exercise treatment has significant immediate and long-term effects on regulators of bone metabolism as well as somatic complaints. LDRnHBT and placeboHBT yielded statistically equal outcomes.

    TC Grafenau

    Buch (Monographie)

    Robert Hable

    Einführung in die Stochastik

    Ein Begleitbuch zur Vorlesung

    SpringerSpektrum Lehrbuch, Berlin [u.a.]

    2015

    ISBN: 978-3-662-43497-0

    TC Grafenau

    Zeitschriftenartikel

    M. Wachten, Robert Hable, O. Fishkis

    Assessment of soil water repellency as a function of soil moisture with mixed modelling

    European Journal of Soil Science, vol. 66, no. 5, pp. 910-920

    2015

    DOI: 10.1111/ejss.12283

    Abstract anzeigen

    An understanding of the relation between soil water repellency (SWR) and soil moisture is a prerequisite of water-flow modelling in water-repellent soil. Here, the relation between SWR and soil moisture was investigated with intact cores of soil taken from three types of soil with different particle-size distributions. The SWR was measured by a sessile drop contact angle (CA) during drying at soil pF values that ranged from −∞ to 4.2. From the measured CA, the work of adhesion (Wa) was calculated and its relation with the pF-value was explored. Mixed modelling was applied to evaluate the effects of pF, soil type and soil depth on CA and Wa. For all soil types, a positive relation was observed between CA and the pF-value that could be represented by a linear model for the pF-range of 1–4.2. The variation in slope and intercept of the CA–pF relationship caused by heterogeneity of the samples taken from a single soil horizon was quantified. In addition, the relation between CA and water content (WC) showed hysteresis, with significantly larger CAs during drying than during wetting.