Results 31 to 40 of about 8,169 (234)
Prediction of Mechanical Properties of LDPE-TPS Nanocomposites Using Adaptive Neuro-Fuzzy Inference System [PDF]
علوم و تکنولوژی پلیمر, 2012 The changes in the behaviour of mechanical properties of low densitypolyethylene-thermoplastic corn starch (LDPE-TPCS) nanocompositeswere studied by an adaptive neuro-fuzzy interference system.
Maryam Sabetzadeh +2 more
doaj +1 more source
The Canadian Journal of Chemical Engineering, EarlyView.Abstract Global energy demand and environmental concerns have intensified the search for renewable and sustainable energy sources. This study thus, focuses on optimizing the transesterification process of waste cooking oil (WCO) using thermally activated basic oxygen furnace slag catalyst calcined at 850°C (BOF 850). The optimization and modelling were
Johra S. Ali, Hillary L. Rutto
wiley +1 more source
The application of ANFIS prediction models for thermal error compensation on CNC machine tools
, 2015 Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change.
Longstaff, Andrew P. +3 more
core +1 more source
Improved Adaptive Neuro-Fuzzy Inference Model for Photovoltaic Power Forecast
, 2022 Photovoltaic (PV) systems are recently the most used sustainable energy source to fit with the energy demand growth. Generally, batteries, as storage systems, are installed along with PV modules. When it comes to an optimal power management of PV/battery
Habib, Mustapha, +5 more
core +1 more source
Journal of Agricultural Machinery, 2014 Suitable soil structure is important for crop growth. One of the main characteristics of soil structure is the size of soil aggregates. There are several ways of showing the stability of soil aggregates, among which the determination of the median weight
R Sedghi, Y Abbaspour Gilandeh
doaj +1 more source
Using an adaptive fuzzy-logic system to optimize the performances and the reduction of chattering phenomenon in the control of induction motor [PDF]
Serbian Journal of Electrical Engineering, 2009 Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance.
Barazane Linda +3 more
doaj +1 more source
Energy Consumption and CO2 Emissions Forecasting of Transport Sector Using Machine Learning
Energy Science &Engineering, EarlyView.The transport sector accounts for approximately one‐quarter of Iran's final energy consumption. The energy demand in this sector has the least variation, with petroleum products accounting for more than 85% of the demand. Furthermore, the accelerated growth of energy consumption and the sector's reliance on fossil fuels, which are the main cause of ...
Amir Hossein Akbari +2 more
wiley +1 more source
Bayesian inference using an adaptive neuro-fuzzy inference system [PDF]
, 2023 For most Bayesian inference problems that are of interest, solving for the model parameter posterior probability distribution remains to be the main challenge.
Knaiber, Mohammed, Alawieh, Leen
core +1 more source
PMU‐Based Wide Area Monitoring With Machine Learning to Prevent Blackouts in Bangladesh Power System
Energy Science &Engineering, EarlyView.A Unified Real‐time Dynamic State Measurements (URTDSM) system with PMU and Phasor Data Concentrator (PDC) deployment plan has been proposed to avoid blackout in the Bangladeshi power system. Machine learning has been used to process data from PMU to identify abnormal events. ABSTRACT The electrical power system must be trustworthy and secure enough to
Imi Bintey Fariha Rahman +5 more
wiley +1 more source
Research progress on the depth of anesthesia monitoring based on the electroencephalogram
Ibrain, Volume 11, Issue 1, Page 32-43, Spring 2025.Electroencephalogram (EEG) can noninvasive, continuous, and real‐time monitor the state of brain electrical activity, and the monitoring of EEG can reflect changes in the depth of anesthesia (DOA). The development of artificial intelligence can enable anesthesiologists to extract, analyze, and quantify DOA from complex EEG data.
Xiaolan He, Tingting Li, Xiao Wang
wiley +1 more source