Showing 1-25 of 10,051 resultsfor "Index Terms":Uncertainty Term
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Uncertainty in Field-Level Measurements of LTE Signals Associated With User Load
Recent modulation schemes in mobile communications, as Long Term Evolution (LTE), give rise to the question of how to properly assess the exposure to the electromagnetic fields generated by base station signals. This letter examines the changes in the field strength associated with the user load variations in the LTE signals. First, we have generated a set of signals, with the same configuration p...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 |
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
Signal and Coherence Bandwidth Effects on Total Radiated Power Measurements of LTE Devices in Reverberation Chambers
An uncertainty model of total radiated power (TRP) measurements in a reverberation chamber (RC) has been proposed in a previous work, where it was hypothesized that the signal bandwidth had no effect on measurement uncertainty. However, our recent experiments indicate that the signal bandwidth of a long-term evolution (LTE) device indeed affects TRP’s measurement uncertainty. In this article, we i...Show More
An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction
We consider the task of performing prediction with neural networks (NNs) on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the prediction model. A multilayer perceptron NN is trained to map interval-valued input data onto interval outputs, representing the prediction intervals (PIs) ...Show More
Estimation of SAMG Entry Time in NPPs Using LSTM with Uncertainty Evaluation
2024 9th International Conference on Frontiers of Signal Processing (ICFSP)
Year: 2024 | Conference Paper |
The entry condition of the Severe Accident Management Guideline (SAMG) in Nuclear Power Plants (NPPs) is determined by the Core Exit Temperature (CET). If the CET exceeds 922 $K$ (1200 ${}^{\circ}F$), severe accident management begins. Because a severe accident can induce a large scale of damage, it is necessary to prepare for such accident and take preemptive actions. However, the operators may b...Show More
Exploring the trade-off between long-term storage deployment and transmission expansion in the power sector
2022 18th International Conference on the European Energy Market (EEM)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Long-term storage systems are being studied as one option to ensure cost-efficient and reliable operation of future, highly decarbonized energy systems with a high share of variable renewable energy sources. In this paper, we analyze whether and under which circumstances a trade-off between long-term energy storage systems and transmission capacity expansion can exist. We investigate this trade-of...Show More
Bayesian Dual-Input-Channel LSTM-Based Prognostics: Toward Uncertainty Quantification Under Varying Future Operations
IEEE Transactions on Reliability
Cited by: Papers (3)
Deep learning methods have received tremendous attention in remaining useful life (RUL) prediction in recent years. Despite the promising results achieved by those deep learning methods, they failed to take account of the impact of varying future operations on RUL prediction and prognostics uncertainty. In most industrial scenarios, the RUL of products is closely related to future missions and loa...Show More
Prediction Interval Estimation of Aeroengine Remaining Useful Life Based on Bidirectional Long Short-Term Memory Network
Reliable and accurate aeroengine remaining useful life (RUL) prediction plays a key role in the aeroengine prognostics and health management (PHM) system. However, due to the epistemic uncertainties associated with aeroengine systems, prediction errors are unavoidable and sometimes significant in traditional deterministic point prediction methods. To improve the accuracy and credibility of RUL pre...Show More
Comparative Analysis of LSTM and Bayesian Neural Networks for Lithium-Ion Battery Capacity Degradation Prediction
2025 15th International Renewable Energy Congress (IREC)
Year: 2025 | Conference Paper |
Accurately predicting lithium-ion battery capacity degradation is vital for optimizing performance and lifespan. This study compares Long Short-Term Memory (LSTM) networks and Bayesian Neural Networks (BNN) for forecasting battery degradation patterns using publicly available datasets. LSTM demonstrated superior prediction accuracy. However, BNN provided valuable insights into prediction un-certai...Show More
IEEE Std 1528-200X specifies protocols and test procedures for the measurement ofthe peak spatial-average SAR induced inside a simplified model of the head of users of certainhandheld radio transceivers. These transceivers are intended to be used for personal wirelesscommunications services, operate in the 300 MHz to 6 GHz frequency range, and are intended tobe operated while held against the ear....Show More
Identification of the Impact of Wind Speed and Load Uncertainties on Short-term Voltage Stability
2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Short-term voltage stability is significantly affected due to the intrinsic uncertainties in the power networks induced by the variations of renewable energy sources (RESs) and system load variations. These inherent uncertainties are usually neglected in the deterministic stability approach of the short-term voltage stability analysis. Hence, the probabilistic analysis is essential in investigatin...Show More
This part of the standard provides recommendations for summarizing the performance and theexpected uncertainty of the reflection coefficient of rectangular-waveguide apertures and interfaces for 110 GHz and above. The information provided also facilitates the development of a complete uncertainty analysis for the performance of rectangular waveguide interfaces. For example, it includes the dimensi...Show More
Dynamic Modeling and Measurement Uncertainty Evaluation of the Nonlinear Piezoelectric Impulse Drive System With Small Samples
Yinye Ding;Wenhao Chen;Rencheng Song;Hongli Li;Chengliang Pan;Haojie Xia
Evaluating measurement uncertainty is crucial for ensuring the reliability of piezoelectric drive systems. However, existing international standards are insufficient for dynamic measurement uncertainty evaluation, primarily due to the complexity of dynamic systems and the challenges of establishing uncertainty propagation models with limited samples. To address this issue, we propose a temporal ev...Show More
Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction
Thi-Lich Nghiem;Viet-Duc Le;Thi-Lan Le;Pierre Maréchal;Daniel Delahaye;Andrija Vidosavljevic
2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)
Year: 2022 | Conference Paper |
Cited by: Papers (8)
Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) provide state-of-the-art performance in various tasks. However, these models are faced with overfitting on small data and cannot measure uncertainty, which have a negative effect on their generalization abilities. In addition, the prediction task can face many challenges because of the complex long-term fluctuations, especially ...Show More
Regional Short Term Load Forecasting Model Considering Dynamic Participation of Multiple Users
2021 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)
Year: 2021 | Conference Paper |
Accurate short-term load forecasting results can effectively guarantee the safe dispatch and stable operation of power system. However, with the popularization of distributed generation and the increase of user-side flexible resource, the load characteristics of users have changed, which has a certain impact on the accuracy of short-term load forecasting. Therefore, a regional short-term load fore...Show More
Evaluation of Uncertainty and Error of LSTM-Based Day-Ahead Load Forecasting Models
2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Load forecasting is becoming increasingly important for planning and operational studies of electricity networks, which feature much higher levels of interactions between supply and demand sides, resulting in much larger variations of power flows. This paper evaluates uncertainty and error in a stacked bidirectional variant of a long short-term memory (SB-LSTM) model, which is applied for a day-ah...Show More
Characterizing and Quantifying Diagnostic (Un)Certainty in Medical Reports through Natural Language Processing
Kathleen Isenegger;Yilan Dong;Mengyuan Shang;Jacob Furst;Daniela Stan-Raicu
2019 International Conference on Computational Science and Computational Intelligence (CSCI)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
Miscommunication of diagnostic uncertainty can deeply affect the quality of treatment a patient receives. A standardized quantification based on the language used in medical reports is a solution for gaining clarity about the amount of uncertainty an author intended to convey. We use natural language processing techniques to create a dictionary of terms and phrases used in a corpus of radiology re...Show More
Optimized uncertainty estimation in the calibration of two-port networks transmission coefficient
2007 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement
Year: 2007 | Conference Paper |
The uncertainty evaluation in the calibration of two-port networks transmission coefficient is here dealt with Attention is focused on the covariance contribution of the uncertainty expression and on the effect of mismatch terms. To this aim, the approach suggested by the Guide for the expression of uncertainty in measurement (GUM) is discussed, and solutions are proposed aimed at optimally combin...Show More
Uncertainty Analysis in the Application of Fault Diagnosis and Prognosis
2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)
Year: 2021 | Conference Paper |
Fault Diagnosis and Prognosis (FDP) approaches have gained significant attention in the past decades. The performance of FDP relies greatly on the model that describes the fault behavior and dynamics under study. For commonly used state space model, uncertainties exist in both the state transition equation and the measurement equation. In this paper, the influence of uncertainties on FDP results a...Show More
Generation maintenance scheduling (GMS), as a medium-term operational planning problem in power system, encounters both midterm and short-term uncertainty sources. This article presents a multiscale multiresolution uncertainty model to characterize midterm and short-term uncertainties distinctively in GMS problem. In the proposed multi-scale multi-resolution GMS (MMGMS) model, the midterm uncertai...Show More
Short-term Aircraft Trajectory Prediction Considering Weather Effect
2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Accurate short-term four-dimensional trajectory prediction (TP) can enhance conflict detection capability and facilitate informed decision making for conflict resolution. The challenge of trajectory prediction lies in considerable uncertainties, especially the uncertainty introduced by weather effects. To address this challenge, we employ the Long Short-Term Memory (LSTM) neural network, renowned ...Show More
Incorporating Uncertainty into Unsupervised Machine Learning for Cyber-Physical Systems
2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)
Year: 2020 | Conference Paper |
In the field of Cyber-Physical Systems (CPS), the early detection of anomalies is crucial to avoid future faulty behaviors, e.g. preventing downtimes or decreasing product qualities. As a solution, unsupervised machine learning can be used to learn models of the historic system behavior and consequentially detect deviations from these models. Since CPS data are high dimensional time series, suitab...Show More
Quantifying Uncertainty in Source Term Estimation with Tensorflow Probability
2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
Fast and accurate location and quantification of a dangerous chemical, biological or radiological release plays a significant role in evaluating emergency situations and their consequences. Thanks to the advent of Deep Learning frameworks (e.g. Tensorflow) and new specialized hardware (e.g. Tensor Cores), the excellent fitting ability of Artificial Neural Networks (ANN) has been used by several re...Show More
Long-Term Variation of Global LAI and the Uncertainty: Analysis of the GEOV2 and Modis LAI Products
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Year: 2020 | Conference Paper |
Cited by: Papers (1)
This study investigates the long-term variation of global leaf area index (LAI) and their uncertainties based on an analysis of the most recent global GEOV2 and MODIS (V6.0) products from 2003 to 2018. The global LAI values, the product qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) were analyzed to study their temporal trend. Results shows that the global GEOV2 and MO...Show More
Uncertainty Estimation in Power Consumption of a Smart Home Using Bayesian LSTM Networks
2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP)
Year: 2022 | Conference Paper |
Smart cities and smart houses have recently seen a rise in power and energy consumption. Household energy demand is likewise ameliorating with population growth and enhanced living standards. For this reason, it is vital to predict power consumption demand for a smart home. Predicting the power consumption is a multivariate time series issue with multiple factors impacting power usage. The variabl...Show More
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