Special Issues
Table of Content

Incomplete Data Test, Analysis and Fusion Under Complex Environments

Submission Deadline: 31 May 2025 View: 233 Submit to Special Issue

Guest Editors

A.Prof. Hang Geng, University of Electronic Science and Technology of China, China
Prof. Lianmeng Jiao, Northwestern Polytechnical University, China
Prof. Kai Chen, University of Electronic Science and Technology of China, China
Dr. Weibo Liu, Brunel University London, UK


Summary

Data test, analysis and fusion consists of the acquisition, processing and combination of information from multiple sources, which aims to draw more comprehensive, specific and accurate inferences about the world than that are achievable from any individual source in isolation. This topic is relevant in many areas: monitoring and maintenance of running equipment, target tracking and recognition in battlefield surveillance, sensor fusion in robotics, image processing in computer vision, expert opinion fusion in risk analysis and so forth. As it is known, data acquisition is often conducted in complex environments, sampled data is inherently noisy and incomplete, human experience/knowledge is inevitably imprecise/ambiguous/irrelevant, and data processing is often subject to various uncertainties. As such, the right test, analysis and fusion of such uncertain and incomplete data is always at the core of any fusion system. This gives rise to a series of both theoretical and practical challenges with focuses on three aspects:  1) how the uncertainty is tested and quantified under complex environments? 2) how uncertain pieces of information can be aggregated in a reasonable and precise way? and 3) how the fused data benefits the physical implementation of interested systems?

 

This Special Issue will focus on the latest advances in uncertain information fusion. Possible theories for managing uncertain information include, but are not limited to, the artificial intelligence, machine learning, communication theory, computer modeling, control theory, estimation theory, data analysis, fault diagnosis, intelligent test, information theory, probability theory, Bayesian inference, fuzzy sets, random sets, reliability theory, rough sets, possibility theory, and belief functions. Prospective authors are invited to submit their novel and original manuscripts about the theoretical underpinnings or the practical applications of these theories.


Keywords

Control engineering, Computer modeling, Signal Processing, Information theory, Estimation theory, Uncertain modeling, Data fusion, Target tracking and recognition, Situation assessment, Fault detection, Image fusion, Pattern analysis, Data mining, Artificial intelligence, Health monitoring, Instrumentation and measurement, Intelligent test and data analysis

Published Papers


  • Open Access

    ARTICLE

    Analysis of Progressively Type-II Inverted Generalized Gamma Censored Data and Its Engineering Application

    Refah Alotaibi, Sanku Dey, Ahmed Elshahhat
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 459-489, 2024, DOI:10.32604/cmes.2024.053255
    (This article belongs to the Special Issue: Incomplete Data Test, Analysis and Fusion Under Complex Environments)
    Abstract A novel inverted generalized gamma (IGG) distribution, proposed for data modelling with an upside-down bathtub hazard rate, is considered. In many real-world practical situations, when a researcher wants to conduct a comparative study of the life testing of items based on cost and duration of testing, censoring strategies are frequently used. From this point of view, in the presence of censored data compiled from the most well-known progressively Type-II censoring technique, this study examines different parameters of the IGG distribution. From a classical point of view, the likelihood and product of spacing estimation methods are… More >

  • Open Access

    ARTICLE

    Evaluations of Chris-Jerry Data Using Generalized Progressive Hybrid Strategy and Its Engineering Applications

    Refah Alotaibi, Hoda Rezk, Ahmed Elshahhat
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3073-3103, 2024, DOI:10.32604/cmes.2024.050606
    (This article belongs to the Special Issue: Incomplete Data Test, Analysis and Fusion Under Complex Environments)
    Abstract A new one-parameter Chris-Jerry distribution, created by mixing exponential and gamma distributions, is discussed in this article in the presence of incomplete lifetime data. We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry (CJ) distribution. When the indicated censored data is present, Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices, including the hazard rate and reliability functions. We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity. More >

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