Πλοήγηση ανά Θέμα "Forecasting"
Αποτελέσματα 1-20 από 125
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Accounting for amplitude of excitation in model updating through a hierarchical Bayesian approach: Application to a two-story reinforced concrete building
(2019)Calibrated linear equivalent models of civil structures are often used for response prediction and performance assessment. However, these models are only valid for a narrow range of excitation level for which these models ... -
Adaptive Bayesian Inference Framework for Joint Model and Noise Identification
(2022)Model updating, the process of inferring a model from data, is prone to the adverse effects of modeling error, which is caused by simplification and idealization assumptions in the mathematical models. In this study, an ... -
An AI-based Prediction-as-a-Service Model for Estimating Machine Bearing Health Status in Industry 4.0 5G Applications
(2021)Intelligent Machine Condition Monitoring (MCM) and Prediction for machine bearings is very important for efficient Industrial 5G applications. Common fault diagnosis and other classification methods usually extract time ... -
Analysis of heat affected zone in Al1239 and Al2198 laser welds using inverse modeling
(2013)Case study analyses of Al2139 and Al2198 laser welds are presented. These analyses demonstrate the concept of constructing parameter spaces for prediction of properties within the Heat Affected Zone (HAZ) of welds using ... -
Application of deep learning and chaos theory for load forecasting in Greece
(2021)In this paper, a novel combination of deep learning recurrent neural network and Lyapunov time is proposed to forecast the consumption of electricity load, in Greece, in normal/abrupt change value areas. Our method verifies ... -
Application of Fuzzy Cognitive Maps to water demand prediction
(2015)This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model and predict time series. The multi-step supervised-learning based-on-gradient methods as well as population-based learning, with ... -
Applying Long Short-Term Memory Networks for natural gas demand prediction
(2019)Long Short-Term Memory (LSTM) algorithm encloses the characteristics of the advanced recurrent neural network methods and is used in this research study to forecast the natural gas demand in Greece in the short-term. LSTM ... -
Approximate kNN Classification for Biomedical Data
(2020)We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution ... -
Artificial intelligence in small bowel capsule endoscopy - current status, challenges and future promise
(2021)Neural network-based solutions are under development to alleviate physicians from the tedious task of small-bowel capsule endoscopy reviewing. Computer-assisted detection is a critical step, aiming to reduce reading times ... -
A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing
(2022)Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers and its strong potential to contribute to the implementation of the UN Sustainable Development Goals. ... -
Bayesian optimal experimental design for parameter estimation and response predictions in complex dynamical systems
(2017)A Bayesian optimal experimental design (OED) framework is revisited and applied to a number of structural dynamics problems. The objective is to optimize the design of the experiment such that the most informative data are ... -
Bayesian optimal experimental design using asymptotic approximations
(2017)Bayesian optimal experimental design (OED) tools for model parameter estimation and response predictions in structural dynamics include sampling (Huan and Marzouk, J. Comput. Phys., 232:288–317, 2013) and asymptotic ... -
Benefits of vendor managed inventory programs in two-stage supply chains
(2018)This paper investigates potential benefits of an Information Sharing (IS) scenario in a two-stage supply chain where demand for an item is generated by the AR(1) process and inventory replacements are made according to an ... -
Can genetic algorithms improve trading decisions in financial markets?
(2005)Over the last years, trading systems are widely used for market assessment however parameter optimization of these systems has adopted little concern. This paper, paper provides an answer to the question Can Genetic ... -
Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting
(2022)The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting ... -
Coding Time Prediction in H.264/HEVC Transcoding Using Macroblock Sizes
(2019)The continuous customers' demand for higher resolution video led to the development of video coding standards that surpass the limitations of H.264/AVC. Prominent examples in this category include the High Efficiency Video ... -
Combined forecasting system for short-term bus load forecasting based on clustering and neural networks
(2020)Micro-grids as 'micro-graphs' of the power systems involve the management of small loads, either isolated or connected to the main grid. Load forecasting is a tool of fundamental importance in power systems design and ... -
A comparative analysis of performances of econometric, fuzzy and time-series models for the forecast of transport demand
(2007)The paper attempts to evaluate how the fuzzy method can improve the range of forecast achieved by classic econometric and time-series models. Based on a survey of factors affecting rail passenger demand and using data of ... -
A comparative analysis of Statistical and Computational Intelligence methodologies for the prediction of traffic-induced fine particulate matter and NO2
(2021)With the urbanization increase, urban mobility and transportation induce higher traffic volumes causing environmental, economic and social impacts. This is due to continuous usage of fossil fuel energy resources generating ... -
Comparing the effect of three transition models on the CFD predictions of a NACA0012 airfoil aerodynamics
(2016)The ability to accurately predict transition is important for flows around airfoils, especially at low Reynolds numbers. Transition from laminar to turbulent flow strongly influences the flow separation and the skin friction, ...