Deyun Zhou

Evaluating water reuse applications under uncertainty: generalized intuitionistic fuzzy-based approach

Water reuse is a viable option to increase urban water supply, especially under new realities of climate change and increasing anthropogenic activities. A sustainable water reuse application should be cost-effective and have acceptable health risk to consumers. Water reuse application evaluation is complex because data acquisitions are usually associated with the problems of uncertainty, hesitancy, and parameterization. In this paper, a generalized intuitionistic fuzzy soft set (GIFSS)-based decision support framework is proposed to provide an effective approach to describe uncertainty and hesitancy in an intuitionistic fuzzy number. In addition, the modified measures of comparison and similarity are proposed to compare water reuse applications. Then, the proposed framework is applied to the City of Penticton (British Columbia, Canada) to evaluate seven water reuse applications. The evaluation results show that the applications of garden flower watering and public parks watering are the most preferred alternatives, which are consistent with the existing practice in the city. Furthermore, the results are highly affected by the generalized parameter and the weights of evaluation criteria. Both the comparison measure-based and similarity measure-based evaluations within the same GIFSS-based framework produce consistent results, indicating an applicable and efficient methodology.

An Extension to Deng’s Entropy in the Open World Assumption with an Application in Sensor Data Fusion

Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed world where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the open world by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the open world assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed world wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few open issues still exist in the current work: the necessary properties for a belief entropy in the open world assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty.

Weapon-Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition

The weapon-target assignment (WTA) problem is a key issue in Command & Control ( ). Asset-based multiobjective static WTA (MOSWTA) problem is known as one of the notable issues of WTA. Since this is an NP-complete problem, multiobjective evolutionary algorithms (MOEAs) can be used to solve it effectively. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a practical and promising multiobjective optimization technique. However, MOEA/D is originally designed for continuous multiobjective optimization which loses its efficiency to discrete contexts. In this study, an improved MOEA/D is proposed to solve the asset-based MOSWTA problem. The defining characteristics of this problem are summarized and analyzed. According to these characteristics, an improved MOEA/D framework is introduced. A novel decomposition mechanism is designed. The mating restriction and selection operation are reformulated. Furthermore, a problem-specific population initialization method is presented to improve the efficiency of the proposed algorithm, and a novel nondominated solution-selection method is put forward to handle the constraints of Pareto front. Appropriate extensions of four MOEA variants are developed in comparison with the proposed algorithm on some generated scenarios. Extensive experiments demonstrate that the proposed method is effective and promising.

An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR) and fault isolation rate (FIR). From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO) algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.

Weighted deng entropy and its application in uncertainty measure

How to quantify the uncertainty information consisted in the body of evidence (BOE) in the framework of Dempster-Shafer evidence theory is still an open issue. A few uncertainty measures have been proposed in Dempster-Shafer evidence theory framework, but these studies mainly focused on the mass function itself and the scale of the frame of discernment (FOD) is totally ignored. Since the existing uncertainty measures do not take full advantage of the information in the BOE, these uncertainty measures are not that efficient in some cases or even cannot present the changing of the information volume in BOE. Based on Deng entropy, this paper presents a new belief entropy named weighted Deng entropy. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the FOD. The numerical example shows that the new belief entropy can quantify the uncertainty in mass BOE more accurately, which is helpful for information processing. In addition, the case study based on the weighted Deng entropy shows a vision of the new method in real application.

A new weighting factor in combining belief function

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences. Many approaches based on discounted evidence and weighted average evidence have been investigated and have made significant improvements. Nevertheless, all of these approaches have inherent flaws. In this paper, a new weighting factor is proposed to address this problem. First, a modified dissimilarity measurement is proposed which is characterized by both distance and conflict between evidences. Second, a measurement of information volume of each evidence based on Deng entropy is introduced. Then two kinds of weight derived from aforementioned measurement are combined to obtain a new weighting factor and a weighted average method based on the new weighting factor is proposed. Numerical examples are used to illustrate the validity and effectiveness of the proposed method. In the end, the new method is applied to a real-life application of river water quality monitoring, which effectively identify the major land use activities contributing to river pollution.

Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration

Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar scale-invariant feature transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance.