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Zeitschriftenartikel

  • Benedikt Elser
  • Michael Scholz

Price Optimization of Perishable Goods Using a Genetic Algorithm

In: International Journal of Revenue Management vol. 1 pg. 1.

  • (2022)

DOI: 10.1504/IJRM.2022.10044440

Multi-product profit optimisation problems have been studied under nested logit models of consumer behaviour. Although attractive through to the relaxation of strong assumptions of multinomial logit models, nested logit models as well as multinomial logit models require costly discrete choice experiments in order to collect data for estimating model parameters. We propose a novel formulation of multi-product profit optimisation that is especially useful for perishable goods that are of the same type and different only in their quality level. Our model relies on willingness to pay data that can be elicited directly, derived from market data or measured indirectly in auctions or through transactions. We furthermore present a genetic algorithm for solving the formulated multi-product profit optimisation and show that our proposed genetic algorithm finds nearby optimal solutions within a very short time span.
  • TC Grafenau
  • NACHHALTIG
  • DIGITAL
Vortrag

  • Michael Fernandes
  • N. Thomas
  • Benedikt Elser
  • A. Rossi
  • Alexander Pletl
  • G. Cremonese

Extrapolation of CRISM based spectral feature maps using CaSSIS four-band images with machine learning techniques

In: EGU General Assembly 2022

Vienna, Austria

  • 23.-27.05.2022 (2022)

DOI: 10.5194/egusphere-egu22-2765

Spectroscopy provides important information on the surface composition of Mars. Spectral data can support studies such as the evaluation of potential (manned) landing sites as well as supporting determination of past surface processes. The CRISM instrument on NASA’s Mars Reconnaissance Orbiter is a high spectral resolution visible infrared mapping spectrometer currently in orbit around Mars. It records 2D spatially resolved spectra over a wavelength range of 362 nm to 3920 nm. At present data collected covers less than 2% of the planet. Lifetime issues with the cryo-coolers prevents limits further data acquisition in the infrared band. In order to extend areal coverage for spectroscopic analysis in regions of major importance to the history of liquid water on Mars (e.g. Valles Marineris, Noachis Terra), we investigate whether data from other instruments can be fused to extrapolate spectral features in CRISMto these non-spectral imaged areas. The present work will use data from the CaSSIS instrument which is a high spatial resolution colour and stereo imager onboard the European Space Agency’s ExoMars Trace Gas Orbiter (TGO). CaSSIS returns images at 4.5 m/px from the nominal 400 km altitude orbit in four colours. Its filters were selected to provide mineral diagnostics in the visible wavelength range (400 – 1100 nm). It has so far imaged around 2% of the planet with an estimated overlap of ≲0.01% of CRISM data. This study introduces a two-step pixel based reconstruction approach using CaSSIS four band images. In the first step advanced unsupervised techniques are applied on CRISM hyperspectral datacubes to reduce dimensionality and establish clusters of spectral features. Given that these clusters contain reasonable information about the surface composition, in a second step, it is feasible to map CaSSIS four band images to the spectral clusters by training a machine learning classifier (for the cluster labels) using only CaSSIS datasets. In this way the system can extrapolate spectral features to areas unmapped by CRISM. To assess the performance of this proposed methodology we analyzed actual and artificially generated CaSSIS images and benchmarked results against traditional correlation based methods. Qualitative and quantitative analyses indicate that by this novel procedure spectral features of in non-spectral imaged areas can be predicted to an extent that can be evaluated quantitatively, especially in highly feature-rich landscapes.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Florian Wahl
  • Matthias Breslein
  • Benedikt Elser

On-demand forklift hailing system for Intralogistics 4.0

In: Procedia Computer Science vol. 200 pg. 878-886.

  • (2022)

DOI: 10.1016/j.procs.2022.01.285

The shift to I4.0 is happening. While large companies have a range of solutions to implement that change, small and medium-sized enterprises (SME) fall short on solutions tailored for their specific needs. To support SMEs in their transformation toward I4.0, we propose a lightweight system to hail forklifts in a production facility of a medium-sized enterprise. Existing shop floor workflows are implemented within the system and allow machine operators to hail forklift drivers using an embedded or a web-based client. Forklift drivers receive driving instructions on their smartphones. Shift managers can monitor intralogistic activities on a dashboard. Management can extract relevant production and forklift KPIs from the system. In a two-week evaluation phase, we installed our system in a production facility for injection moulded plastic parts. We equipped 12 machines and two forklifts and registered a total of 690 jobs. We found half of the jobs were picked up in 4:05 min and 80% of all jobs were completed in less than 40:02 min.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Michael Fernandes
  • Alexander Pletl
  • N. Thomas
  • A. Rossi
  • Benedikt Elser

Generation and Optimization of Spectral Cluster Maps to Enable Data Fusion of CaSSIS and CRISM Datasets

In: Remote Sensing vol. 14 pg. 2524.

  • (2022)

DOI: 10.3390/rs14112524

Four-band color imaging of the Martian surface using the Color and Stereo Surface Imaging System (CaSSIS) onboard the European Space Agency’s ExoMars Trace Gas Orbiter exhibits a high color diversity in specific regions. Not only is the correlation of color diversity maps with local morphological properties desirable, but mineralogical interpretation of the observations is also of great interest. The relatively high spatial resolution of CaSSIS data mitigates its low spectral resolution. In this paper, we combine the broad-band imaging of the surface of Mars, acquired by CaSSIS with hyperspectral data from the Compact Reconnaissance Imaging Spectrometer (CRISM) onboard NASA’s Mars Reconnaissance Orbiter to achieve a fusion of both datasets. We achieve this using dimensionality reduction and data clustering of the high dimensional datasets from CRISM. In the presented research, CRISM data from the Coprates Chasma region of Mars are tested with different machine learning methods and compared for robustness. With the help of a suitable metric, the best method is selected and, in a further step, an optimal cluster number is determined. To validate the methods, the so-called “summary products” derived from the hyperspectral data are used to correlate each cluster with its mineralogical properties. We restrict the analysis to the visible range in order to match the generated clusters to the CaSSIS band information in the range of 436–1100 nm. In the machine learning community, the so-called UMAP method for dimensionality reduction has recently gained attention because of its speed compared to the already established t-SNE. The results of this analysis also show that this method in combination with the simple K-Means outperforms comparable methods in its efficiency and speed. The cluster size obtained is between three and six clusters. Correlating the spectral cluster maps with the given summary products from CRISM shows that four bands, and especially the NIR bands and VIS albedo, are sufficient to discriminate most of these clusters. This demonstrates that features in the four-band CaSSIS images can provide robust mineralogical information, despite the limited spectral information using semi-automatic processing.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Marco Kretschmann
  • Andreas Fischer
  • Benedikt Elser

Extracting Keywords from Publication Abstracts for an Automated Researcher Recommendation System

In: Digitale Welt (Proceedings of the First International Symposium on Applied Artificial Intelligence in Conjunction with DIGICON) vol. 4 pg. 20-25.

  • (2020)

DOI: 10.1007/s42354-019-0227-2

This paper presents an automated keyword assignment system for scientific abstracts. That system is applied to paper abstracts collected in a local publication database and used to drive a researcher recommendation system. Problems like low data volume and missing keywords are discussed. For remediation, training is performed on an extended data set based on large online publication databases. Additionally a closer look at label imbalance in the dataset is taken. Ten multi-label classification algorithms for assigning keywords from a given catalogue to a scientific abstract are compared. The usage of binary relevance as transformation method with LightGBM as classifier yields the best results. Random oversampling before the training phase additionally increases the F1-Score by around 5-6%.
  • Angewandte Wirtschaftswissenschaften
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Benedikt Elser

Die digitale Wagenreihung bei der Deutschen Bahn

In: WI-Symposium

Deggendorf

  • 28.10.2018 (2018)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL

Projekte

FreshAnalytics, Industriewerkstatt 4.0, WeisDas


Kernkompetenzen

  • Big Data Computing
  • NoSQL Datenbanken
  • Container Orchestrierung
  • Rechnernetze, TCP/IP
  • Overlay Netze, Peer-To-Peer Systeme
  • Graphalgorithmen, Graphanalyse