Editorial

3 pages, 178 KiB

Open AccessEditorial

Information Theory: Deep Ideas, Wide Perspectives, and Various Applications

by Irad Ben-Gal and Evgeny Kagan

Entropy 2021, 23(2), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/e23020232 - 17 Feb 2021

Cited by 2 | Viewed by 2374

The history of information theory, as a mathematical principle for analyzing data transmission and information communication, was formalized in 1948 with the publication of Claude Shannon’s famous paper “A Mathematical Theory of Communication” [...] Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

Research

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25 pages, 4316 KiB

Open AccessFeature PaperArticle

Project Management Monitoring Based on Expected Duration Entropy

by Shiva Cohen Kashi, Shai Rozenes and Irad Ben-Gal

Entropy 2020, 22(8), 905; https://0-doi-org.brum.beds.ac.uk/10.3390/e22080905 - 18 Aug 2020

Cited by 2 | Viewed by 2796

Abstract

Projects are rarely executed exactly as planned. Often, the actual duration of a project’s activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity’s completion date. While monitoring a project at various inspection points is pricy, [...] Read more.

Projects are rarely executed exactly as planned. Often, the actual duration of a project’s activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity’s completion date. While monitoring a project at various inspection points is pricy, it can lead to a better estimation of the project completion time, hence saving costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using information theory measures. We search for monitoring (inspection) points that can maximize the information about the project’s estimated duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible monitoring points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is little affected by the number of activities, and the algorithm can address large projects with hundreds or thousands of activities. Numerical experimentation and an analysis of various parameters are presented. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

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19 pages, 1612 KiB

Open AccessArticle

Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic

by Gonen Singer and Matan Marudi

Entropy 2020, 22(8), 871; https://0-doi-org.brum.beds.ac.uk/10.3390/e22080871 - 07 Aug 2020

Cited by 17 | Viewed by 3693

Abstract

In this research, we develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used to select the classifying attributes. We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional [...] Read more.

In this research, we develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used to select the classifying attributes. We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. In such an application, some of the potential classification errors could have critical consequences. The classification tool will enable the spread of the epidemic to be tracked and controlled by yielding insights regarding the relationship between local containment measures and the daily growth factor. In order to benefit maximally from a variety of ordinal and non-ordinal algorithms, we also propose an ensemble majority voting approach to combine different algorithms into one model, thereby leveraging the strengths of each algorithm. We perform experiments in which the task is to classify the daily COVID-19 growth rate factor based on environmental factors and containment measures for 19 regions of Italy. We demonstrate that the ordinal algorithms outperform their non-ordinal counterparts with improvements in the range of 6–25% for a variety of common performance indices. The majority voting approach that combines ordinal and non-ordinal models yields a further improvement of between 3% and 10%. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

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17 pages, 415 KiB

Open AccessArticle

Event-Triggered Adaptive Fault Tolerant Control for a Class of Uncertain Nonlinear Systems

by Chenglong Zhu, Chenxi Li, Xinyi Chen, Kanjian Zhang, Xin Xin and Haikun Wei

Entropy 2020, 22(6), 598; https://0-doi-org.brum.beds.ac.uk/10.3390/e22060598 - 27 May 2020

Cited by 15 | Viewed by 2968

Abstract

This paper considers an adaptive fault-tolerant control problem for a class of uncertain strict feedback nonlinear systems, in which the actuator has an unknown drift fault and the loss of effectiveness fault. Based on the event-triggered theory, the adaptive backstepping technique, and Lyapunov [...] Read more.

This paper considers an adaptive fault-tolerant control problem for a class of uncertain strict feedback nonlinear systems, in which the actuator has an unknown drift fault and the loss of effectiveness fault. Based on the event-triggered theory, the adaptive backstepping technique, and Lyapunov theory, a novel fault-tolerant control strategy is presented. It is shown that an appropriate comprise between the control performance and the sensor data real-time transmission consumption is made, and the fault-tolerant tracking control problem of the strict feedback nonlinear system with uncertain and unknown control direction is solved. The adaptive backstepping method is introduced to compensate the actuator faults. Moreover, a new adjustable event-triggered rule is designed to determine the sampling state instants. The overall control strategy guarantees that the output signal tracks the reference signal, and all the signals of the closed-loop systems are convergent. Finally, the fan speed control system is constructed to demonstrate the validity of the proposed strategy and the application of the general systems. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

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19 pages, 1423 KiB

Open AccessArticle

Cooperative Detection of Multiple Targets by the Group of Mobile Agents

by Barouch Matzliach, Irad Ben-Gal and Evgeny Kagan

Entropy 2020, 22(5), 512; https://0-doi-org.brum.beds.ac.uk/10.3390/e22050512 - 30 Apr 2020

Cited by 5 | Viewed by 2256

Abstract

The paper considers the detection of multiple targets by a group of mobile robots that perform under uncertainty. The agents are equipped with sensors with positive and non-negligible probabilities of detecting the targets at different distances. The goal is to define the trajectories [...] Read more.

The paper considers the detection of multiple targets by a group of mobile robots that perform under uncertainty. The agents are equipped with sensors with positive and non-negligible probabilities of detecting the targets at different distances. The goal is to define the trajectories of the agents that can lead to the detection of the targets in minimal time. The suggested solution follows the classical Koopman’s approach applied to an occupancy grid, while the decision-making and control schemes are conducted based on information-theoretic criteria. Sensor fusion in each agent and over the agents is implemented using a general Bayesian scheme. The presented procedures follow the expected information gain approach utilizing the “center of view” and the “center of gravity” algorithms. These methods are compared with a simulated learning method. The activity of the procedures is analyzed using numerical simulations. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

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18 pages, 267 KiB

Open AccessArticle

Entropy-Based GLDS Method for Social Capital Selection of a PPP Project with q-Rung Orthopair Fuzzy Information

by Li Liu, Jiang Wu, Guiwu Wei, Cun Wei, Jie Wang and Yu Wei

Entropy 2020, 22(4), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/e22040414 - 07 Apr 2020

Cited by 32 | Viewed by 2435

Abstract

The social capital selection of a public–private-partnership (PPP) project could be regarded as a classical multiple attribute group decision-making (MAGDM) issue. In this paper, based on the traditional gained and lost dominance score (GLDS) method, the q-rung orthopair fuzzy entropy-based GLDS method was [...] Read more.

The social capital selection of a public–private-partnership (PPP) project could be regarded as a classical multiple attribute group decision-making (MAGDM) issue. In this paper, based on the traditional gained and lost dominance score (GLDS) method, the q-rung orthopair fuzzy entropy-based GLDS method was used to solve MAGDM problems. First, some basic theories related to the q-rung orthopair fuzzy sets (q-ROFSs) are briefly reviewed. Then, to fuse the q-rung orthopair fuzzy information effectively, the q-rung orthopair fuzzy Hamacher weighting average (q-ROFHWA) operator and q-rung orthopair fuzzy Hamacher weighting geometric (q-ROFHWG) operator based on the Hamacher operation laws are proposed. Moreover, to determine the attribute weights, the q-rung orthopair fuzzy entropy (q-ROFE) is proposed and some significant merits of it are discussed. Next, based on the q-ROFHWA operator, q-ROFE, and the traditional GLDS method, a MAGDM model with q-rung orthopair fuzzy information is built. In the end, a numerical example for social capital selection of PPP projects is provided to testify the proposed method and deliver a comparative analysis. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

27 pages, 1379 KiB

Open AccessArticle

Negation of Pythagorean Fuzzy Number Based on a New Uncertainty Measure Applied in a Service Supplier Selection System

by Haiyi Mao and Rui Cai

Entropy 2020, 22(2), 195; https://0-doi-org.brum.beds.ac.uk/10.3390/e22020195 - 07 Feb 2020

Cited by 11 | Viewed by 2289

Abstract

The Pythagorean fuzzy number (PFN) consists of membership and non-membership as an extension of the intuitionistic fuzzy number. PFN has a larger ambiguity, and it has a stronger ability to express uncertainty. In the multi-criteria decision-making (MCDM) problem, it is also very difficult [...] Read more.

The Pythagorean fuzzy number (PFN) consists of membership and non-membership as an extension of the intuitionistic fuzzy number. PFN has a larger ambiguity, and it has a stronger ability to express uncertainty. In the multi-criteria decision-making (MCDM) problem, it is also very difficult to measure the ambiguity degree of a set of PFN. A new entropy of PFN is proposed based on a technique for order of preference by similarity to ideal solution (Topsis) method of revised relative closeness index in this paper. To verify the new entropy with a good performance in uncertainty measure, a new Pythagorean fuzzy number negation approach is proposed. We develop the PFN negation and find the correlation of the uncertainty measure. Existing methods can only evaluate the ambiguity of a single PFN. The newly proposed method is suitable to systematically evaluate the uncertainty of PFN in Topsis. Nowadays, there are no uniform criteria for measuring service quality. It brings challenges to the future development of airlines. Therefore, grasping the future market trends leads to winning with advanced and high-quality services. Afterward, the applicability in the service supplier selection system with the new entropy is discussed to evaluate the service quality and measure uncertainty. Finally, the new PFN entropy is verified with a good ability in the last MCDM numerical example. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

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14 pages, 2249 KiB

Open AccessArticle

Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy

by Yongzhen Du, Honggeng Yang and Xiaoyang Ma

Entropy 2020, 22(1), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/e22010065 - 03 Jan 2020

Cited by 6 | Viewed by 2308

Abstract

Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source [...] Read more.

Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

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5 pages, 192 KiB

Open AccessLetter

Inferring Authors’ Relative Contributions to Publications from the Order of Their Names When Default Order Is Alphabetical

by Yigal Gerchak

Entropy 2020, 22(9), 927; https://0-doi-org.brum.beds.ac.uk/10.3390/e22090927 - 24 Aug 2020

Viewed by 1551

Abstract

In attributing individual credit for co-authored academic publications, one issue is how to apportion (unequal) credit, based on the order of authorship. Apportioning credit for completed joint undertakings has always been a challenge. Academic promotion committees are faced with such tasks regularly, when [...] Read more.

In attributing individual credit for co-authored academic publications, one issue is how to apportion (unequal) credit, based on the order of authorship. Apportioning credit for completed joint undertakings has always been a challenge. Academic promotion committees are faced with such tasks regularly, when trying to infer a candidate’s contribution to an article they coauthored with others. We propose a method for achieving this goal in disciplines (such as the author’s) where the default order is alphabetical. The credits are those maximizing Shannon entropy subject to order constraints. Full article

(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)

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