Jun Zhang - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Jun Zhang

Publications

14

Citations

47

Publications by Year

20202022

Co-Authors:

Zhuo ChenXiwen ChenCan DingJiliang FuJiniang Fu

Show All Co-Authors (23)

Jun Zhang

Affiliation

China Electric Power Research Institute, Wuhan, China

Publication Topics

Current Transformer,
Primary Current,
Convolutional Neural Network,
Core Material,
Deep Learning,
Faster R-CNN,
Main Core,
Negative Samples,
Bounding Box,
Detection Of Abnormalities,
False Alarm Rate,
Feature Maps

Biography

Jun Zhang is currently an Engineer with China Electric Power Research Institute Company Ltd. His research interest includes smart sensing.(Based on document published on 11 November 2020).

Publications

14

Citations

47

Publications by Year

20202022

Co-Authors:

Zhuo Chen
Xiwen Chen
Can Ding
Jiliang Fu
Jiniang Fu

Show All Co-Authors (23)

Author's Published Works

Search History

Showing 1-25 of 2,745 resultsfor "Index Terms":Uncertainty Quantification

Conferences (1,755)

Journals (910)

Magazines (58)

Early Access Articles (21)

Standards (1)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Paolo Manfredi(23)
Zheng Zhang(23)
Slawomir Koziel(23)
Sourajeet Roy(22)
Riccardo Trinchero(20)
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA(16)
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA(13)
Sandia National Laboratories, Albuquerque, NM, USA(12)
Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy(12)
Pacific Northwest National Laboratory, Richland, WA, USA(12)
IEEE Access(103)
IEEE Transactions on Geoscience and Remote Sensing(50)
IEEE Transactions on Power Systems(50)
IEEE Transactions on Instrumentation and Measurement(32)
IEEE Transactions on Electromagnetic Compatibility(29)
IEEE(2,690)
OUP(21)
IET(15)
AGU(5)
BIAI(4)
Media(117)
Code(2)
Datasets(1)
Video(1)
Seattle, WA, USA(35)
Orlando, FL, USA(34)
Washington, DC, USA(33)
Chengdu, China(29)
Atlanta, GA, USA(26)
Uncertainty Quantification(2,042)
Neural Network(569)
Monte Carlo Simulation(419)
Epistemic Uncertainty(302)
Random Variables(294)
Inactive(1)
Standard Docs(1)

Select All on Page

Sort By

Results

Visualizing Flow of Uncertainty through Analytical Processes

Yingcai Wu;Guo-Xun Yuan;Kwan-Liu Ma

IEEE Transactions on Visualization and Computer Graphics

Year: 2012 | Volume: 18, Issue: 12 | Journal Article |

Cited by: Papers (41)

Uncertainty can arise in any stage of a visual analytics process, especially in data-intensive applications with a sequence of data transformations. Additionally, throughout the process of multidimensional, multivariate data analysis, uncertainty due to data transformation and integration may split, merge, increase, or decrease. This dynamic characteristic along with other features of uncertainty ...Show More

Visualizing Flow of Uncertainty through Analytical Processes

Yingcai Wu;Guo-Xun Yuan;Kwan-Liu Ma

IEEE Transactions on Visualization and Computer Graphics

Year: 2012 | Volume: 18, Issue: 12 | Journal Article |

Efficient Shape Uncertainty Quantification for the TESLA Cavity

David Ebert;Anna Ziegler;Jürgen Dölz;Sebastian Schöps

2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC)

Year: 2024 | Conference Paper |

HTML

In our contribution, we present an efficient shape uncertainty quantification method based on closed-form shape derivatives for the Maxwell eigenvalue problem. We demonstrate our algorithm for the 9-cell TESLA cavity which is subject to misalignment due to imperfections in the manufacturing process and compare the performance of our approach to the classic Monte Carlo method. In this comparison, w...Show More

Efficient Shape Uncertainty Quantification for the TESLA Cavity

David Ebert;Anna Ziegler;Jürgen Dölz;Sebastian Schöps

2024 IEEE 21st Biennial Conference on Electromagnetic Field Computation (CEFC)

Year: 2024 | Conference Paper |

GraphCast-Qtf: An improved weather prediction model based on uncertainty quantification methods

Lang Zheng;Ruxue Xing;Yaojun Wang

2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

Year: 2024 | Conference Paper |

HTML

In the field of climate prediction, although the traditional numerical weather forecasting (NWP) method has made significant progress in the processing of historical data and improving the prediction accuracy, it still has significant limitations in using a large amount of historical climate data and improving the credibility of the prediction results. With the rapid development of deep learning t...Show More

GraphCast-Qtf: An improved weather prediction model based on uncertainty quantification methods

Lang Zheng;Ruxue Xing;Yaojun Wang

2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

Year: 2024 | Conference Paper |

Uncertainty Quantification for Branch-Current State Estimation in Power Distribution Systems

Florian Thorsten Lutz Strobel;Davood Babazadeh;Simon Stock;Christian Becker

2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

Year: 2024 | Conference Paper |

HTML

The growing number of prosumers in the distribution grid pushes the system to the edge of its operational capacities. This makes it essential to assess the current state of the system. However, the availability of grid measurements is often times not sufficient. To achieve observability, inaccurate pseudo measurements can be included in the state estimation. The obtained results are subject to hig...Show More

Uncertainty Quantification for Branch-Current State Estimation in Power Distribution Systems

Florian Thorsten Lutz Strobel;Davood Babazadeh;Simon Stock;Christian Becker

2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

Year: 2024 | Conference Paper |

Propagation of uncertainty in systems with both probabilistic and possibilistic inputs

2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW)

Year: 2014 | Conference Paper |

HTML

This paper presents a method for generating a global quantification and characterization of the uncertainty in the output of a system with both probabilistic and possibilistic inputs. When we have evidence-based probability distributions of some of the inputs to the system but only possibilistic information about the uncertainties of others, neither standard statistics nor purely possibilistic ana...Show More

Propagation of uncertainty in systems with both probabilistic and possibilistic inputs

2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW)

Year: 2014 | Conference Paper |

Joint Segmentation and Uncertainty Estimation of Ventricular Structures from Cardiac MRI using a Bayesian CondenseUNet

S. M. Kamrul Hasan;Cristian A. Linte

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2022 | Conference Paper |

HTML

While convolutional neural networks (CNNs) have shown potential in segmenting cardiac structures from magnetic resonance (MR) images, their clinical applications still fall short of providing reliable cardiac segmentation. As a result, it is critical to quantify segmentation uncertainty in order to identify which segmentations might be troublesome. Moreover, quantifying uncertainty is critical in ...Show More

Joint Segmentation and Uncertainty Estimation of Ventricular Structures from Cardiac MRI using a Bayesian CondenseUNet

S. M. Kamrul Hasan;Cristian A. Linte

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2022 | Conference Paper |

A quantitative analysis method for model uncertainty based on Verification, Validation, and Uncertainty Quantification

Hui Tang;Lizhi Wang;Lingfei You;Mengfei Yao;Xiaohong Wang

2023 10th International Conference on Dependable Systems and Their Applications (DSA)

Year: 2023 | Conference Paper |

HTML

The uncertainty of the model significantly impacts evaluation accuracy. Current research primarily focuses on model parameters and error propagation mechanisms, overlooking the inherent uncertainty within the model itself. This study presents a quantitative analysis method for model uncertainty based on Verification, Validation, and Uncertainty Quantification (VV&UQ) from the perspective of the mo...Show More

A quantitative analysis method for model uncertainty based on Verification, Validation, and Uncertainty Quantification

Hui Tang;Lizhi Wang;Lingfei You;Mengfei Yao;Xiaohong Wang

2023 10th International Conference on Dependable Systems and Their Applications (DSA)

Year: 2023 | Conference Paper |

Review of Uncertainty Quantification of Measurement and Computational Modeling in EMC Part I: Measurement Uncertainty

Carlo F. M. Carobbi;Sébastien Lalléchère;Luk R. Arnaut

IEEE Transactions on Electromagnetic Compatibility

Year: 2019 | Volume: 61, Issue: 6 | Journal Article |

Cited by: Papers (26)

HTML

In this two-parts paper, methods for the quantification of uncertainty in measurement and computational modeling are reviewed, with an emphasis on applications in electromagnetic compatibility (EMC). The current status of international standards relating to measurement uncertainty in EMC is provided (in part I), as well as a review of selected alternative methods and recent developments in the dom...Show More

Review of Uncertainty Quantification of Measurement and Computational Modeling in EMC Part I: Measurement Uncertainty

Carlo F. M. Carobbi;Sébastien Lalléchère;Luk R. Arnaut

IEEE Transactions on Electromagnetic Compatibility

Year: 2019 | Volume: 61, Issue: 6 | Journal Article |

An Uncertainty Quantification Method Based on Generalized Interval

Youmin Hu;Fengyun Xie;Bo Wu;Yan Wang

2013 12th Mexican International Conference on Artificial Intelligence

Year: 2013 | Conference Paper |

Cited by: Papers (2)

HTML

The need to quantify aleatory and epistemic uncertainties has been widely recognized in the engineering applications. Aleatory uncertainty arises from inherent randomness, whereas epistemic uncertainty is due to the lack of knowledge. Traditionally uncertainty has been quantified by probability measures and the two uncertainty components are not readily differentiated. Intervals naturally capture ...Show More

An Uncertainty Quantification Method Based on Generalized Interval

Youmin Hu;Fengyun Xie;Bo Wu;Yan Wang

2013 12th Mexican International Conference on Artificial Intelligence

Year: 2013 | Conference Paper |

Uncertainty Quantification in Deep Learning Context: Application to Insurance

Mouad Ablad;Bouchra Frikh;Brahim Ouhbi

2020 6th IEEE Congress on Information Science and Technology (CiSt)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

HTML

Nowadays, Deep learning becomes the most powerful black box predictors, which has achieved a high performance in many fields such as insurance especially in fraud detection, claims management, pricing, etc. Despite these achievements, the main interest of these classic deep learning networks is to focus only on improving the accuracy of the model without assessing the quality of the outputs. In ot...Show More

Uncertainty Quantification in Deep Learning Context: Application to Insurance

Mouad Ablad;Bouchra Frikh;Brahim Ouhbi

2020 6th IEEE Congress on Information Science and Technology (CiSt)

Year: 2020 | Conference Paper |

Uncertainty Quantification in Neural Networks Using Stochastic Differential Equations

Geng Yuan;Hiroaki Mukaidani;Tadashi Shima

2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

HTML

Uncertainty quantification plays a crucial role in reduction of uncertainties during optimization and decision making, however, it is not solved yet in deep learning. Bayesian network is often used for uncertainty estimation but is only applicable to cases with a small number of parameters because the prior probability of the parameters is needed. On the other hand, while some non-Bayesian models ...Show More

Uncertainty Quantification in Neural Networks Using Stochastic Differential Equations

Geng Yuan;Hiroaki Mukaidani;Tadashi Shima

2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE)

Year: 2023 | Conference Paper |

An Uncertainty Network Working Mechanism Analysis Method based on Local Interpretable Model-agnostic Explanations

Zhonghao Cheng;Yinxiao Miao;Xiujian Zhang

2022 China Automation Congress (CAC)

Year: 2022 | Conference Paper |

HTML

In recent years, Deep learning has been widely used in different fields which learns the skills from dataset, however the parameter and decision logic is opaque for human. The trustworthy of deep learning model has attracted much attention of researchers, in that uncertainty is an essential part of trusted artificial intelligence. Therefore uncertainty analysis is an important part of the basic th...Show More

An Uncertainty Network Working Mechanism Analysis Method based on Local Interpretable Model-agnostic Explanations

Zhonghao Cheng;Yinxiao Miao;Xiujian Zhang

2022 China Automation Congress (CAC)

Year: 2022 | Conference Paper |

A Two-Phase Monte Carlo Simulation/Non-Intrusive Polynomial Chaos (MSC/NIPC) Method for Quantification of Margins and Mixed Uncertainties (QMMU) in Flutter Analysis

Zhengwen Hu;Baoqiang Zhang;Jing Yang

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (2)

HTML

A two-phase Monte Carlo Simulation/Non-intrusive Polynomial Chaos (MCS/NIPC) method for quantification of margins and mixed uncertainties (aleatory and epistemic uncertainties) is proposed in this paper for the flutter speed boundary analysis. Compared with the traditional MCS/MCS method which needs lots of numerical simulations, the MCS/NIPC method can reduce the computational cost without losing...Show More

A Two-Phase Monte Carlo Simulation/Non-Intrusive Polynomial Chaos (MSC/NIPC) Method for Quantification of Margins and Mixed Uncertainties (QMMU) in Flutter Analysis

Zhengwen Hu;Baoqiang Zhang;Jing Yang

Year: 2020 | Volume: 8 | Journal Article |

Uncertainty Quantification of Cuffless Blood Pressure with Deep Evidential Regression Model

Zhan Shen;Shuaiting Yao;Yifan Chen;Xiaorong Ding

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2024 | Conference Paper |

HTML

Cuffless blood pressure (BP) estimation models have been extensively studied in recent years. However, due to aleatoric and epistemic uncertainty, these methods make it difficult to provide reliable and accurate BP estimations meeting the clinical requirement. In this study, we propose a novel method to quantify the uncertainty of the cuffless BP estimation model and combine epistemic uncertainty ...Show More

Uncertainty Quantification of Cuffless Blood Pressure with Deep Evidential Regression Model

Zhan Shen;Shuaiting Yao;Yifan Chen;Xiaorong Ding

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2024 | Conference Paper |

A Statistical Framework for Visualization of Positional Uncertainty in Deep Brain Stimulation Electrodes

Tushar M. Athawale;Kara A. Johnson;Chris R. Butson;Chris R. Johnson

2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC)

Year: 2019 | Conference Paper |

HTML

Deep brain stimulation (DBS) is an FDA-approved neurosurgical procedure for treating patients with movement disorders such as Parkinson's disease. Patient-specific computational modeling and visualization play a key role for efficient surgical and therapeutic decision-making relevant to DBS. The computational models analyze DBS post-operative brain imaging, e.g., computed tomography (CT), to under...Show More

A Statistical Framework for Visualization of Positional Uncertainty in Deep Brain Stimulation Electrodes

Tushar M. Athawale;Kara A. Johnson;Chris R. Butson;Chris R. Johnson

2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC)

Year: 2019 | Conference Paper |

Uncertainty Quantification in Linear Interpolation for Isosurface Extraction

Tushar Athawale;Alireza Entezari

IEEE Transactions on Visualization and Computer Graphics

Year: 2013 | Volume: 19, Issue: 12 | Journal Article |

Cited by: Papers (43)

We present a study of linear interpolation when applied to uncertain data. Linear interpolation is a key step for isosurface extraction algorithms, and the uncertainties in the data lead to non-linear variations in the geometry of the extracted isosurface. We present an approach for deriving the probability density function of a random variable modeling the positional uncertainty in the isosurface...Show More

Uncertainty Quantification in Linear Interpolation for Isosurface Extraction

Tushar Athawale;Alireza Entezari

IEEE Transactions on Visualization and Computer Graphics

Year: 2013 | Volume: 19, Issue: 12 | Journal Article |

Propagation of parameter uncertainty based on evidence theory

Junming Hu;Fayuan Wei;Renwei Ge;Tianxi Liang

2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

Year: 2011 | Conference Paper |

HTML

Uncertainty quantification is an important component in robust design, because it is essential to obtain the robust solution. The classical robust design method pays more attention to aleatory uncertainty of parameters while disregarding epistemic uncertainty, which is now viewed as an inherent property of the system and has gained a lot of attention in recent years. In this paper, the features of...Show More

Propagation of parameter uncertainty based on evidence theory

Junming Hu;Fayuan Wei;Renwei Ge;Tianxi Liang

2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

Year: 2011 | Conference Paper |

Uncertainty Quantification of LSTM Model for SoC Estimation against Aleatoric and Epistemic Uncertainties

Anbumalar Pandian;Sutha Subbian;Pappa Natarajan;Seevagan Senthilkumar Deepa

2024 Control Instrumentation System Conference (CISCON)

Year: 2024 | Conference Paper |

HTML

Estimation of State of Charge (SoC) with higher accuracy is very essential for range prediction, optimal discharging of Lithium-ion batteries, etc. Physics-based models are commonly employed for SoC estimation to achieve higher accuracy. However, it is challenging due to the need of precise initial SoC. To address this issue, data-driven approach is used to develop SoC prediction model. The effect...Show More

Uncertainty Quantification of LSTM Model for SoC Estimation against Aleatoric and Epistemic Uncertainties

Anbumalar Pandian;Sutha Subbian;Pappa Natarajan;Seevagan Senthilkumar Deepa

2024 Control Instrumentation System Conference (CISCON)

Year: 2024 | Conference Paper |

Design of a simulink model for uncertainty quantification of concentration of contaminant transport through ground water

Debanshee Datta;D. Datta

2011 International Conference on Image Information Processing

Year: 2011 | Conference Paper |

HTML

Uncertainty analysis of any model is one of the main themes of recent trends of computation, modeling, and optimization. Uncertainty analysis is classified as two categories, aleatory and epistemic. Aleatory uncertainty is due to random variability of the model parameters which is irreducible in nature, whereas epistemic uncertainty is due to insufficiency or vagueness of the model parameters and ...Show More

Design of a simulink model for uncertainty quantification of concentration of contaminant transport through ground water

Debanshee Datta;D. Datta

2011 International Conference on Image Information Processing

Year: 2011 | Conference Paper |

Monte Carlo based stochastic finite element model for uncertainty quantification in flip chip BGA electronic packaging

Liu Chu;Jiajia Shi;Robin Braun

2021 22nd International Conference on Electronic Packaging Technology (ICEPT)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

HTML

The Ball grid array (BGA) electronic packaging method is an efficient and appropriate way for high density integrated circuits. The uncertainties in the material and geometrical parameters to product reliability and safety under the real operating situation are the crucial issues deserved more attention. In this paper, the Monte Carlo based stochastic finite element model (MC-SFEM) is proposed for...Show More

Monte Carlo based stochastic finite element model for uncertainty quantification in flip chip BGA electronic packaging

Liu Chu;Jiajia Shi;Robin Braun

2021 22nd International Conference on Electronic Packaging Technology (ICEPT)

Year: 2021 | Conference Paper |

Multilevel Monte Carlo FDTD Method for Uncertainty Quantification

Xiaojie Zhu;Luca Di Rienzo;Xikui Ma;Lorenzo Codecasa

IEEE Antennas and Wireless Propagation Letters

Year: 2022 | Volume: 21, Issue: 10 | Journal Article |

Cited by: Papers (5)

HTML

The recent multilevel Monte Carlo method is here proposed for uncertainty quantification in electromagnetic problems solved by the finite-difference time-domain (FDTD) method, when material parameters are modeled as random variables. It improves the estimations of the mean and variance of the quantities of interest computed on a FDTD spatial grid by sampling at coarser levels of discretization. Th...Show More

Multilevel Monte Carlo FDTD Method for Uncertainty Quantification

Xiaojie Zhu;Luca Di Rienzo;Xikui Ma;Lorenzo Codecasa

IEEE Antennas and Wireless Propagation Letters

Year: 2022 | Volume: 21, Issue: 10 | Journal Article |

Geomagnetic Disturbance Uncertainty Quantification Modeling: An Electromagnetic Transient and Steady-State Simulation based Approach

Arturo Bretas;Kiana Brown;Varoozhan Hartoonian;Jonathan Snodgrass

2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

HTML

Geomagnetic disturbances have been shown to disrupt the operation of the bulk electrical system through lowfrequency effects in the earth’s magnetic field that in turn induce changing electrical fields on the earth’s surface. As a result, geomagnetically induced currents flow in transmission lines, introducing the risk for widespread damage to high-voltage transformers and voltage collapse due to ...Show More

Geomagnetic Disturbance Uncertainty Quantification Modeling: An Electromagnetic Transient and Steady-State Simulation based Approach

Arturo Bretas;Kiana Brown;Varoozhan Hartoonian;Jonathan Snodgrass

2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

Year: 2023 | Conference Paper |

Quantifying Uncertainty in Real Time with Split BiRNN for Radar Human Activity Recognition

Lorin Werthen-Brabants;Geethika Bhavanasi;Ivo Couckuyt;Tom Dhaene;Dirk Deschrijver

2022 19th European Radar Conference (EuRAD)

Year: 2022 | Conference Paper |

Cited by: Papers (5)

HTML

Radar systems can be used to perform human activity recognition in a privacy preserving manner. Deep Neural Networks are able to effectively process the complex radar data and make predictions. Often these networks are large and do not scale well when processing a large amount of radar streams at once, for example when monitoring multiple rooms in a hospital. This work proposes Bayesian Split Bidi...Show More

Quantifying Uncertainty in Real Time with Split BiRNN for Radar Human Activity Recognition

Lorin Werthen-Brabants;Geethika Bhavanasi;Ivo Couckuyt;Tom Dhaene;Dirk Deschrijver

2022 19th European Radar Conference (EuRAD)

Year: 2022 | Conference Paper |

Application of QMU to structural analysis considering uncertainty

Chao-Yang Xie;Hong-Zhong Huang;Fa-Yuan Wei

2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

Year: 2012 | Conference Paper |

Cited by: Papers (2)

HTML

Quantification of Margins and Uncertainties (QMU) is a methodology for assessing the confidence in the performance of complex systems, and has been applied to general engineering areas. In this paper, the concept and process of QMU is introduced at first, and then an implementation framework of QMU is proposed for structural analysis considering uncertainty. The framework is a synthesis of several...Show More

Application of QMU to structural analysis considering uncertainty

Chao-Yang Xie;Hong-Zhong Huang;Fa-Yuan Wei

2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

Year: 2012 | Conference Paper |

A Literature Survey on Estimating Uncertainty in Deep Learning Models: Ensuring safety in Intelligent Systems

Soja Salim;Jayasudha JS

2023 2nd International Conference on Computational Systems and Communication (ICCSC)

Year: 2023 | Conference Paper |

HTML

Popular Deep learning models suffer many drawbacks such as making wrong predictions with great confidence, lack of uncertainty estimation capability, and failure in real-time scenarios. The main reason for the uncertainty is due to the large gap between how neural networks are trained in practice and how they are evaluated in deployment. When it comes to safety-critical applications, it is very im...Show More

A Literature Survey on Estimating Uncertainty in Deep Learning Models: Ensuring safety in Intelligent Systems

Soja Salim;Jayasudha JS

2023 2nd International Conference on Computational Systems and Communication (ICCSC)

Year: 2023 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.