Evidence theory and differential evolution based uncertainty quantification for buckling load of semi-rigid jointed frames

Tang, Hesheng; Su, Yu; Wang, Jiao

doi:10.1007/s12046-015-0388-0

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…D-S evidence theory is a no-exact reasoning theory introduced by Dempster and developed by Shafer. It is one of the most used methods for multi-sensor information fusion and is very suitable for decision-level fusion [34,35]. The specific flow of the algorithm is as follows:…”

Section: Confidence Score Fusionmentioning

confidence: 99%

Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

et al. 2020

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Vehicle detection is essential for driverless systems. However, the current single sensor detection mode is no longer sufficient in complex and changing traffic environments. Therefore, this paper combines camera and light detection and ranging (LiDAR) to build a vehicle-detection framework that has the characteristics of multi adaptability, high real-time capacity, and robustness. First, a multi-adaptive high-precision depth-completion method was proposed to convert the 2D LiDAR sparse depth map into a dense depth map, so that the two sensors are aligned with each other at the data level. Then, the You Only Look Once Version 3 (YOLOv3) real-time object detection model was used to detect the color image and the dense depth map. Finally, a decision-level fusion method based on bounding box fusion and improved Dempster–Shafer (D–S) evidence theory was proposed to merge the two results of the previous step and obtain the final vehicle position and distance information, which not only improves the detection accuracy but also improves the robustness of the whole framework. We evaluated our method using the KITTI dataset and the Waymo Open Dataset, and the results show the effectiveness of the proposed depth completion method and multi-sensor fusion strategy.

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Section: Confidence Score Fusionmentioning

confidence: 99%

Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

et al. 2020

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“…The Demspter-Shafer mathematical theory of evidence collates information from various sources and helps an individual reach a degree of belief that takes into consideration all of the available evidence (Maseleno et al 2015). Evidence theory has been applied to human reliability assessment (Su et al 2015), information fusion (Li et al 2017), reliability analysis (Zhang et al 2015, Tang et al 2015a), complex engineering systems (Shah et al 2015), and in aeronautics (Tang et al 2015b).…”

Section: Literature Reviewmentioning

confidence: 99%

The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility

Sivaprasad

MacKenzie

2018

Decision Analysis

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Non-probabilistic approaches to decision making have been proposed for situations in which an individual does not have enough information to assess probabilities over an uncertainty. One non-probabilistic method is to use intervals in which an uncertainty has a minimum and maximum but nothing is assumed about the relative likelihood of any value within the interval. The Hurwicz decision rule in which a parameter trades off between pessimism and optimism generalizes the current rules for making decisions with intervals. This article analyzes the relationship between intervals based on the Hurwicz rule and traditional decision analysis using a few probability distributions and an exponential utility functions. This article shows that the Hurwicz decision rule for an interval is logically equivalent to: (i) an expected value decision with a triangle distribution over the interval; (ii) an expected value decision with a beta distribution; and (iii) an expected utility decision with constant absolute risk aversion with a uniform distribution. These probability distributions are not exhaustive. There are likely other distributions and utility functions for which equivalence with the Hurwicz decision rule can also be established. Since a frequent reason for the use intervals is that intervals assume less information than a probability distribution, the results in this article call into question whether decision making based on intervals really assumes less information than subjective expected utility decision making.Abstract Non-probabilistic approaches to decision making have been proposed for situations in which an individual does not have enough information to assess probabilities over an uncertainty. One nonprobabilistic method is to use intervals in which an uncertainty has a minimum and maximum but nothing is assumed about the relative likelihood of any value within the interval. The Hurwicz decision rule in which a parameter trades off between pessimism and optimism generalizes the current rules for making decisions with intervals. This article analyzes the relationship between intervals based on the Hurwicz rule and traditional decision analysis using a few probability distributions and an exponential utility functions.This article shows that the Hurwicz decision rule for an interval is logically equivalent to: (i) an expected value decision with a triangle distribution over the interval; (ii) an expected value decision with a beta distribution; and (iii) an expected utility decision with constant absolute risk aversion with a uniform distribution. These probability distributions are not exhaustive. There are likely other distributions and utility functions for which equivalence with the Hurwicz decision rule can also be established. Since a frequent reason for the use intervals is that intervals assume less information than a probability distribution, the results in this article call into question whether decision making based on intervals really assumes less information than subjective expected utility decisio...

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“…The DS (Dempster Shafer) evidence theory is a useful uncertain reasoning method for multi-sensor information fusion [17,18]. It can be regarded as a generalization of the Bayes theory of subjective probability, which can grasp the uncertainty of the problem and performs better than the traditional probability theory.…”

Section: Introductionmentioning

confidence: 99%

RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory

Zeng

Yang

Wang

et al. 2019

Sensors

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With the development of low-cost RGB-D (Red Green Blue-Depth) sensors, RGB-D object recognition has attracted more and more researchers’ attention in recent years. The deep learning technique has become popular in the field of image analysis and has achieved competitive results. To make full use of the effective identification information in the RGB and depth images, we propose a multi-modal deep neural network and a DS (Dempster Shafer) evidence theory based RGB-D object recognition method. First, the RGB and depth images are preprocessed and two convolutional neural networks are trained, respectively. Next, we perform multi-modal feature learning using the proposed quadruplet samples based objective function to fine-tune the network parameters. Then, two probability classification results are obtained using two sigmoid SVMs (Support Vector Machines) with the learned RGB and depth features. Finally, the DS evidence theory based decision fusion method is used for integrating the two classification results. Compared with other RGB-D object recognition methods, our proposed method adopts two fusion strategies: Multi-modal feature learning and DS decision fusion. Both the discriminative information of each modality and the correlation information between the two modalities are exploited. Extensive experimental results have validated the effectiveness of the proposed method.

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Evidence theory and differential evolution based uncertainty quantification for buckling load of semi-rigid jointed frames

Cited by 11 publications

References 35 publications

Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility

RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory

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