scholarly journals Combined Conflict Evidence Based on Two-Tuple IOWA Operators

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1369 ◽  
Author(s):  
Ying Zhou ◽  
Xiyun Qin ◽  
Xiaozhe Zhao
Keyword(s):  
High Performance ◽  
Weighted Average ◽  
Entropy Method ◽  
Maximal Entropy ◽  
Aggregated Data ◽  
Conflicting Evidence ◽  
High Conflict ◽  
Maximal Entropy Method ◽  
Two Parameters ◽  
Iowa Operator

Due to poor natural factors and human interference, the information that was obtained by sensors tends to have high uncertainty and high conflict with others. A combination of highly conflicting evidence with Dempster’s rule often produces results that run counter to intuition. To solve the above problem, a conflict evidence combination methodology is proposed in this article, which contains the distance of evidence, classical conflict coefficient, and two-tuple IOWA operator. Both the classical conflict coefficient and Jousselme distance indicate the degree of evidence conflict, and it is clear that the two parameters are symmetrical. First, the two-tuple IOWA operator is proposed. Second, the orness is determined by aggregated data; then, the weighting vector is calculated by a maximal entropy method. Finally, the weighted average is the evidence in the system by a two-tuple IOWA operator; then, the Dempster combination rule is utilized to fuse information. Compared with other existing methods, the presented methodology has high performance when dealing with conflict evidence and has strong anti-interference ability.

scholarly journals EXPERIENCE OF USING MAXIMAL ENTROPY METHOD (MAXENT) FOR ZONING OF THE TERRITORY BY HERS RISK USING NIZHNY NOVGOROD REGION AS AN EXAMPLE

2017 ◽  
pp. 39-45
Author(s):  
L. A. Solntsev ◽  
V. M. Dubyansky
Keyword(s):  
Potential Risk ◽  
Prediction Accuracy ◽  
Environmental Changes ◽  
Spatial Localization ◽  
Entropy Method ◽  
Maximal Entropy ◽  
Environment Factors ◽  
Vegetation Activity ◽  
Maximal Entropy Method ◽  
Nizhny Novgorod Region

Aim. Zoning of the territory of Nizhny Novgorod region by risk of HFRS infection using Maxent method. Materials and methods. Data from Centre of Hygiene and Epidemiology in Nizhny Novgorod region for each case of the HFRS for 2010 - 2016, data on environment (Bioclim), data on vegetation activity (MODIS) were used. ArcGIS 10.2.2 and Maxent 3.3.3k packages were used. Results. Model for evaluation of potential risk of HFRS in Nizhny Novgorod was developed and validated. Conclusion. The data obtained do not contradict the observed spatial localization of the cases of HFRS infection (prediction accuracy over 75%), detected connection between spatial localization of HFRS cases and combination of environment factors and allow to predict changes in borders of potentially dangerous segments after environmental changes.

A Z-number integrated weighted MULTIMO-ORA method for risk prioritization in FMEA

2021 ◽  
pp. 1-15
Author(s):  
Weizhong Wang ◽  
Yilin Ma ◽  
Shuli Liu
Keyword(s):  
Risk Analysis ◽  
Risk Evaluation ◽  
Oil And Gas ◽  
Weighted Average ◽  
Entropy Method ◽  
The Self ◽  
Analysis Problem ◽  
Risk Prioritization ◽  
Self Confidence ◽  
Evaluation Information

Current risk prioritization approaches for FMEA models are insufficient to cope with risk analysis problem in which the self-confidence of expert’s judgment and the deviation among risk evaluation information are considered, simultaneously. Therefore, to remedy this limitation, this paper reports an extended risk prioritization approach by integrating the MULTIMOORA approach, Z-numbers and power weighted average (PWA) operator. Firstly, the Z-numbers with triangular fuzzy numbers are applied to reflect the self-confidence and uncertainty of expert’s judgment. Next, the PWA operator for Z-numbers (Z-PWA) with similarity measure is proposed to obtain the group risk evaluation matrix by considering the influence of the deviation among risk evaluation information. Then, an extended version of MULTIMOORA method with developed entropy method is presented to calculate risk priority ranking order of each failure. Finally, the equipment failures in a certain oil and gas plant is utilized to test the extended risk prioritization approach for FMEA model. After that, the sensitivity and comparison studies are led to illustrate the availability and reliability of the proposed risk prioritization approach for FMEA based risk analysis problem.

scholarly journals Research on Improvement of the Combination Method for Conflicting Evidence Based on Historical Data

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 762
Author(s):  
Shuai Yuan ◽  
Honglei Wang
Keyword(s):  
Decision Rule ◽  
Historical Data ◽  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Final Decision ◽  
Evidence Based ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
The Difference

In a multi-sensor system, due to the difference of performance of sensors and the environment in which the sensor collects evidence, evidence collected will be highly conflicting, which leads to the failure of D-S evidence theory. The current research on combination methods of conflicting evidence focuses on eliminating the problem of "Zadeh paradox" brought by conflicting evidence, but do not distinguish the evidence from different sources effectively. In this paper, the credibility of each piece of evidence to be combined is weighted based on historical data, and the modified evidence is obtained by weighted average. Then the final result is obtained by combining the modified evidence using D-S evidence theory, and the improved decision rule is used for the final decision. After the decision, the system updates and stores the historical data based on actual results. The improved decision rule can solve the problem that the system cannot make a decision when there are two or more propositions corresponding to the maximum support in the final combination result. This method satisfies commutative law and associative law, so it has the symmetry that can meet the needs of the combination of time-domain evidence. Numerical examples show that the combination method of conflict evidence based on historical data can not only solve the problem of “Zadeh paradox”, but also obtain more reasonable results.

scholarly journals Detection of cirrhosis through ultrasound imaging by intensity difference technique

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Karan Aggarwal ◽  
Manjit Singh Bhamrah ◽  
Hardeep Singh Ryait
Keyword(s):  
High Performance ◽  
Research Work ◽  
Region Of Interest ◽  
Normal Liver ◽  
Cirrhotic Liver ◽  
Intensity Difference ◽  
Non Invasive ◽  
The Us ◽  
The Difference ◽  
Two Parameters

Abstract Cirrhosis is a liver disease that is considered to be among the most common diseases in healthcare. Due to its non-invasive nature, ultrasound (US) imaging is a widely accepted technology for the diagnosis of this disease. This research work proposed a method for discriminating the cirrhotic liver from normal liver through US images. The liver US images were obtained from the radiologist. The radiologist also specified the region of interest (ROI) from these images, and then the proposed method was applied to it. Two parameters were extracted from the US images through differences in intensity of neighboring pixels. Then, these parameters can be used to train a classifier by which cirrhotic region of test patient can be detected. A 2-D array was created by the difference in intensity of the neighboring pixels. From this array, two parameters were calculated. The decision was taken by checking these parameters. The validation of the proposed tool was done on 80 images of cirrhotic and 30 images of normal liver, and classification accuracy of 98.18% was achieved. The result was also verified by the radiologist. The results verified its possibility and applicability for high-performance cirrhotic liver discrimination.

scholarly journals A Self-Adaptive Combination Method in Evidence Theory Based on the Power Pignistic Probability Distance

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 526
Author(s):  
Jian Wang ◽  
Jing-wei Zhu ◽  
Yafei Song
Keyword(s):  
Evidence Theory ◽  
Weighted Average ◽  
Combination Method ◽  
Multiple Sources ◽  
Adaptive Combination ◽  
Conflicting Evidence ◽  
Different Types ◽  
Fusion Methods ◽  
Temporal And Spatial ◽  
Probability Distance

Existing methods employed for combining temporal and spatial evidence derived from multiple sources into a single coherent description of objects and their environments lack versatility in various applications such as multi-sensor target recognition. This is addressed in the present study by proposing an adaptive evidence fusion method based on the power pignistic probability distance. This method classifies evidence sets into non-conflicting and conflicting evidence sets based on the maximum power pignistic probability distance obtained between evidence pairs in the evidence set. Non-conflicting evidence sets are fused using Dempster’s rule, while conflicting evidence sets are fused using a weighted average combination method based on the power pignistic probability distance. The superior evidence fusion performance of the proposed method is demonstrated by comparisons with the performances of seven other fusion methods based on numerical examples with four different evidence conflict scenarios. The results show that the method proposed in this paper not only can properly fuse different types of evidence, but also provides an excellent focus on the components of evidence sets with high confidence, which is conducive to timely and accurate decisions.

MnO2/HF/HNO3/H2O System for High-Performance Texturization on Multi-Crystalline Silicon

2019 ◽  
Vol 960 ◽  
pp. 263-267
Author(s):  
Huan Liu ◽  
Lei Zhao ◽  
Hong Wei Diao ◽  
Wen Jing Wang
Keyword(s):  
High Performance ◽  
Crystalline Silicon ◽  
Weighted Average ◽  
Etching Time ◽  
Component Ratio ◽  
Surface Reflectance ◽  
Simple Processing ◽  
Ratio Range ◽  
Etching System ◽  
Si Wafer

It was found that the addition of MnO2 particles into the HF/HNO3/H2O system could significantly improve the texturization etching performance on multi-crystalline silicon (mc-Si) wafer. For a wide component ratio range of HF/HNO3/H2O from HF-rich to HNO3-rich, by optimizing the MnO2 usage and the etching time, the addition of MnO2 particles always reduced the texture reflectance greatly. Low weighted average surface reflectance (Ra) for the AM1.5G sun spectrum in the wavelength range of 380–1100 nm was achieved on both the slurry wire sliced (SWS) mc-Si and the diamond wire sliced (DWS) mc-Si. Due to its excellent effect and simple processing, the MnO2/HF/HNO3/H2O etching system can be expected as a candidate for high-performance texturization on mc-Si wafer, especially on DWS mc-Si wafer.

Grey target decision-making model of interval grey number based on cone volume

2017 ◽  
Vol 7 (2) ◽  
pp. 247-258 ◽  
Author(s):  
Lizhen Wang ◽  
Wuyong Qian
Keyword(s):  
Decision Making ◽  
Entropy Method ◽  
Maximal Entropy ◽  
Information Distortion ◽  
Content Type ◽  
Model Based ◽  
Proposed Model ◽  
Grey Number ◽  
Decision Making Model ◽  
The Impact

Purpose The purpose of this paper is to propose a grey target decision model based on cobweb area in order to overcome the effect and influence from the extreme value of the index on the decision result. However, it does not take into account the impact of the correlation between indicators on the angle of the index, and produce a certain degree decision information distortion as a result of the equal angle between the indicators. In order to solve the above problems, a novel grey decision-making model based on cone volume is proposed. Design/methodology/approach In this paper, the model uses the whitening weight function to whiten the interval grey number, and the Delphi method and the maximal entropy method are exploited to integrate the weight of the index. On the basis of this, the center of the bull’s eye, the weight and the index value are constructed as the center circle, the radius, and the high cone, respectively. The scheme is selected by the volume of the cone, the decision is made according to the order relation, and the example is utilized to prove and analyze the validity of the proposed model. Findings The results show that the proposed model can well improve the traditional grey target decision-making model from the modeling object and modeling method. Practical implications The method exposed in the paper can be used to deal with the grey target decision-making problems which characteristics are multi-indexes, and the attribute values are interval grey numbers. Originality/value The paper succeeds in overcoming the disadvantages of grey target decision making based on the target center distance and the cobweb area.

Variability Among Aflatoxin Test Results on Runner Peanuts Harvested From Small Field Plots1

2004 ◽  
Vol 31 (1) ◽  
pp. 59-63 ◽  
Author(s):  
T. B. Whitaker ◽  
J. W. Dorner ◽  
F. G. Giesbrecht ◽  
A. B. Slate
Keyword(s):  
Standard Deviation ◽  
High Performance ◽  
Weighted Average ◽  
Aflatoxin Contamination ◽  
Small Field ◽  
Test Results ◽  
Field Plot ◽  
Aflatoxin Concentration ◽  
Production Practices ◽  
Standard Production

Abstract An experiment was conducted to determine the variability associated with aflatoxin contamination of peanuts from plants grown in specified row lengths. Runner peanuts (cv. Georgia Green) were planted in 10, 76.2 m rows (20 seed/m) and grown using standard production practices. Plants were exposed to natural late-season drought conditions making the peanuts susceptible to preharvest aflatoxin contamination. Plants were mechanically dug, inverted, and separated into 500 plots of 1.5 m single rows. Peanuts from each numerically identified plot were harvested with a mechanical picker, dried to 8% kernel moisture (wet basis), shelled, and analyzed for aflatoxin by high performance liquid chromatography (HPLC). The average kernel mass and weighted average aflatoxin concentration for all plots was 131 g and 2278 ng/g, respectively. The kernel mass varied among the 500 plots from a low of 4 g to a maximum of 283 g. The aflatoxin concentration among the 500 plots varied from a low of 0 ng/g to a maximum of 32,142 ng/g. The standard deviation among the 500 plot aflatoxin values was 4061. The standard deviation among sample concentrations for this field study was very similar to previous studies that measured the standard deviation among sample concentrations taken from bulk farmers' stock lots. Increasing plot length decreased the standard deviation among plot aflatoxin values as predicted by statistical theory. For example, increasing plot row length by a factor of four, or from 1.5 to 6 m, decreased the standard deviation by a factor of two, or from 4061 to 2031. A regression equation was developed to predict the effect of plot row length on the variability among aflatoxin plot values. This information is useful for designing field plot experiments to test various strategies for reducing or preventing preharvest aflatoxin contamination.

scholarly journals Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2225
Author(s):  
Piotr Hajder ◽  
Łukasz Rauch
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Computation Time ◽  
Computing Power ◽  
Load Optimization ◽  
Multi Scale ◽  
Scale Modelling ◽  
Similar Computation ◽  
And Performance ◽  
Two Parameters

Numerical computations are usually associated with the High Performance Computing. Nevertheless, both industry and science tend to involve devices with lower power in computations. This is especially true when the data collecting devices are able to partially process them at place, thus increasing the system reliability. This paradigm is known as Edge Computing. In this paper, we propose the use of devices at the edge, with lower computing power, for multi-scale modelling calculations. A system was created, consisting of a high-power device—a two-processor workstation, 8 RaspberryPi 4B microcomputers and 8 NVidia Jetson Nano units, equipped with GPU processor. As a part of this research, benchmarking was performed, on the basis of which the computational capabilities of the devices were classified. Two parameters were considered: the number and performance of computing units (CPUs and GPUs) and the energy consumption of the loaded machines. Then, using the calculated weak scalability and energy consumption, a min–max-based load optimization algorithm was proposed. The system was tested in laboratory conditions, giving similar computation time with same power consumption for 24 physical workstation cores vs. 8x RaspberryPi 4B and 8x Jetson Nano. The work ends with a proposal to use this solution in industrial processes on example of hot rolling of flat products.

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