Results 71 to 80 of about 11,326 (149)

Dual-environment feature fusion-based method for estimating building-scale population distributions

Geo-spatial Information Science
Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues.
Guangyu Liu, Rui Li, Jing Xia, Zhaohui Liu, Jing Cai, Huayi Wu, Mingjun Peng   +6 more
doaj   +1 more source

Predicting steady degradation in ship power system: A deep learning approach based on comprehensive monitoring parameters

IET Intelligent Transport Systems
Steady degradation (SD) prediction is crucial for the intelligent operation and maintenance of ship power system (SPS). Addressing the challenge of predicting the SD process, this study introduces the YC2Model, a system‐level predictive method that ...
Xingshan Chang, Xiaojian Xu, Bohua Qiu, Muheng Wei, Xinping Yan, Jie Liu   +5 more
doaj   +1 more source

Performance Evaluation of ARIMA, Autoformer, and Symmetric LSTNFCL Models for Traffic Accident Emergency Prediction

The prediction of pre-hospital medical emergencies based on historical timing data holds significant potential for enhancing individual safety. In this study, we constructed ARIMA (Autoregressive Integrated Moving Average Model), Autoformer, and the ...
Chonghong Dan, Haoran Li, Zhou He, Honglei Wei, Qingbiao Song, Maowu Pu   +5 more
core   +1 more source

Stock Market Index Prediction Using CEEMDAN-LSTM-BPNN-Decomposition Ensemble Model

Journal of Applied Mathematics
This study investigates the forecasting of the Deutscher Aktienindex (DAX) market index by addressing the nonlinear and nonstationary nature of financial time series data using the CEEMDAN decomposition method.
John Kamwele Mutinda, Abebe Geletu
doaj   +1 more source

Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH)

, 2014
This study was aimed to build a model for the estimation of national harvested area of rice by incorporating element of variant heterogeneity and the influence of asymmetry factors on time series data using five types of GARCH models, namely: symmetric ...
Kusman Sadik, I Made Sumertajaya, Teuku Achmad Iqbal   +2 more
core   +1 more source

Comparison of ARIMA boost, Prophet boost, and TSLM models in forecasting Davao City weather data

The geography of the Philippines experiences climate variability thus, providing accurate and timely weather forecasts to the population is crucial. Climate forecasts, which are issued and disseminated by government agencies, serve as essential risk ...
R. Mercado, Tamara Cher, Galleto Jr., Fredelino A., B. Rogers, Jamal Kay   +2 more
core   +1 more source

Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sites

Journal of Big Data
The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this
Bubryur Kim, K. R. Sri Preethaa, Sujeen Song, R. R. Lukacs, Jinwoo An, Zengshun Chen, Euijung An, Sungho Kim   +7 more
doaj   +1 more source

Statistical Analysis of Shunt-FACTS Devices Impact on Power Flow Control [PDF]

, 2012
: In this study, impact of Static VAr Compensator (SVC) on power flow control is studied by statistical indices. For this purpose, several conventional branches of SVC are introduced and their impacts are discuses on active and reactive powers flow ...
Mohammad Karimi, Payam Farhadi, Young Researchers Club, Tina Sojoudi, Mohammad Bagher Ahmadi, Parsabad Moghan Branch, Davood Mostafa   +6 more
core  

Enhancing Short-Term Wind Speed Prediction Based on Deep Learning With Ensemble Learning Model for Small Wind Turbine Applications

IEEE Access
Wind is unstable and unpredictable, and power generation is not constant. Wind speed prediction reduces these disadvantages, and it is essential to measure accurate wind speed predictions to install and stabilize wind power generation systems.
J. Sathyaraj, V. Sankardoss
doaj   +1 more source

Learning-Driven Intelligent Passivity Control Using Nonlinear State Observers for Induction Motors

Automation
This paper proposes a learning-driven passivity-based control (PBC) strategy for sensorless induction motors, combining a nonlinear adaptive observer with recurrent neural networks (RNNs) to improve robustness and estimation accuracy under dynamic ...
Belkacem Bekhiti, Kamel Hariche, Mohamed Roudane, Aleksey Kabanov, Vadim Kramar   +4 more
doaj   +1 more source