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Entropy
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Application of Information-Theoretic Concepts in Bio-, Environmental and Engineering Science...
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Application of Information-Theoretic Concepts in Bio-, Environmental and Engineering Science Research

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 6381

Special Issue Editor

Prof. Dr. Jürgen Pilz

E-Mail Website
Guest Editor
Institut für Statistik, Alpen-Adria Universität Klagenfurt, Universitätsstraße 65, 9020 Klagenfurt, Austria
Interests: bayesian statistics; design of experiments; spatial statistics; statistical learning; environmental statistics; industrial statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Engineering decisions are often made with incomplete information and are based on experience and thumb rules. Small sample sizes and limited information can be alleviated by the use of entropy theory. The (maximum) entropy principle has in many aspects of engineering science and civil engineering analysis demonstrated its use, not only for logically inferring missing data, but also in numerical optimization and design processes, where it is exploited for inferring solutions under partial information.

The entropy concept also provides a natural way of determining risk associated with an environmental system and can therefore serve as a basis for risk and reliability analysis. We want to explore the information-theoretical and probabilistic aspects of entropy and look at ways to quantify uncertainty, reflecting situations where we do not know which of the events constituting the complex system under consideration will occur.

The aim of this Special Issue is to encourage interested researchers in engineering science disciplines, industrial engineering, as well as in bio-, geo-, and environmental sciences to present original and recent developments on interfacing information-theoretic concepts with:

  • model selection and data analysis;
  • prediction and design methods;
  • reliability, safety, and risk analysis;

in their research work. We particularly welcome novel applications of these concepts for:

  • designing water, energy, and telecommunication networks;
  • statistical process control in industrial manufacturing;
  • predicting natural hazards and climate change processes;
  • signal processing in biological networks;
  • remote sensing in agriculture and forestry.

Prof. Dr. Jürgen Pilz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

11 pages, 1362 KiB  
Article
Design of Optimal Rainfall Monitoring Network Using Radar and Road Networks
by Taeyong Kwon, Seongsim Yoon and Sanghoo Yoon
Entropy 2021, 23(3), 378; https://0-doi-org.brum.beds.ac.uk/10.3390/e23030378 - 23 Mar 2021
Viewed by 1656
Abstract
Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that [...] Read more.
Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that can effectively reflect the characteristics of the watershed. In this study, conditional entropy was used to calculate the uncertainty of the watershed using rainfall and radar data observed from 2018 to 2019 in the Goesan Dam and Hwacheon Dam watersheds. The results identified radar data suitable for the characteristics of the watershed and proposed a site for an additional rainfall gauge. It is also necessary to select the location of the additional rainfall gauged by limiting the points where smooth movement and installation, for example crossing national borders, are difficult. The proposed site emphasized accessibility and usability by leveraging road information and selecting a radar grid near the road. As a practice result, the uncertainty of precipitation in the Goesan and Hwacheon Dam watersheds could be decreased by 70.0% and 67.9%, respectively, when four and three additional gauge sites were installed without any restriction. When these were installed near to the road, with five and four additional gauge sites, the uncertainty in the Goesan Dam and Hwacheon Dam watersheds were reduced by up to 71.1%. Therefore, due to the high degree of uncertainty, it is necessary to measure precipitation. The operation of the rainfall gauge can provide a smooth site and configure an appropriate monitoring network. Full article
(This article belongs to the Special Issue Application of Information-Theoretic Concepts in Bio-, Environmental and Engineering Science Research)
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27 pages, 24618 KiB  
Article
From a Point Cloud to a Simulation Model—Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling
by Christina Petschnigg, Markus Spitzner, Lucas Weitzendorf and Jürgen Pilz
Entropy 2021, 23(3), 301; https://0-doi-org.brum.beds.ac.uk/10.3390/e23030301 - 03 Mar 2021
Cited by 3 | Viewed by 2373
Abstract
The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, [...] Read more.
The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene. Full article
(This article belongs to the Special Issue Application of Information-Theoretic Concepts in Bio-, Environmental and Engineering Science Research)
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13 pages, 421 KiB  
Article
Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data
by Fang Xie and Johannes Lederer
Entropy 2021, 23(2), 230; https://0-doi-org.brum.beds.ac.uk/10.3390/e23020230 - 16 Feb 2021
Cited by 1 | Viewed by 1845
Abstract
Recent discoveries suggest that our gut microbiome plays an important role in our health and wellbeing. However, the gut microbiome data are intricate; for example, the microbial diversity in the gut makes the data high-dimensional. While there are dedicated high-dimensional methods, such as [...] Read more.
Recent discoveries suggest that our gut microbiome plays an important role in our health and wellbeing. However, the gut microbiome data are intricate; for example, the microbial diversity in the gut makes the data high-dimensional. While there are dedicated high-dimensional methods, such as the lasso estimator, they always come with the risk of false discoveries. Knockoffs are a recent approach to control the number of false discoveries. In this paper, we show that knockoffs can be aggregated to increase power while retaining sharp control over the false discoveries. We support our method both in theory and simulations, and we show that it can lead to new discoveries on microbiome data from the American Gut Project. In particular, our results indicate that several phyla that have been overlooked so far are associated with obesity. Full article
(This article belongs to the Special Issue Application of Information-Theoretic Concepts in Bio-, Environmental and Engineering Science Research)
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