The need for accurate estimates of reference crop evapotranspiration (ETo) is important in irrigation planning and design, irrigation scheduling, reservoir management among other applications. ETo can be accurately determined using the internationall...
Predicting reservoir water levels helps manage droughts and floods. Predicting reservoir water level is complex because it depends on factors such as climate parameters and human intervention. Therefore, predicting water level needs robust models. Ou...
We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the lo...
Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance i...
The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for d...
This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of mach...
Extreme learning machine (ELM) is known as a kind of single-hidden layer feedforward network (SLFN), and has obtained considerable attention within the machine learning community and achieved various real-world applications. It has advantages such as...
Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth&...
This study proposes an experimental data-driven hybrid prediction framework for hydraulic turbine performance using a Particle Swarm Optimization-enhanced Extreme Learning Machine (PSO-ELM). The performance of three hydraulic turbines, namely Pelton,...
In high-elevation or high-latitude permafrost areas, persistent subzero temperatures significantly impact the freeze–thaw durability of concrete structures. Traditional methods for studying the frost resistance of concrete in permafrost regions...
In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit...
The frequency of typhoons in China has gradually increased, resulting in serious damage to low-voltage power grid lines. Therefore, it is of great significance to study the influencing factors and predict the amount of damage, which contributes to en...
The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) p...
As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because E...
The prognosis of lithium-ion batteries for their remaining useful life is an essential technology in prognostics and health management (PHM). In this paper, we propose a novel hybrid prediction method based on particle filter (PF) and extreme learnin...
Currently, there are some emerging online learning applications handling data streams in real-time. The On-line Sequential Extreme Learning Machine (OS-ELM) has been successfully used in real-time condition prediction applications because of its good...
The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO2) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization....
It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this...
State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems of the traditional state space modeling methods that rely on component-level models (CLMs) and canno...
To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a...
The proton exchange membrane fuel cell (PEMFC) is an extremely clean and efficient power generation device. However, its limited lifespan has restricted the large-scale commercial development of PEMFCs. Life prediction is a promising solution for the...
In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and fe...
The relationships between the fatigue crack growth rate
A coherent optical (CO) orthogonal frequency division multiplexing (OFDM) scheme gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Neve...
Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-preci...
Blasting is essential for breaking hard rock in opencast mines and tunneling projects. It creates an adverse impact on flyrock. Thus, it is essential to forecast flyrock to minimize the environmental effects. The objective of this study is to forecas...
In this study, an improved version of the Extreme Learning Machine, namely the Improved Weighted Regularization ELM (IWRELM), is proposed for hourly precipitation forecasting that is multi-steps ahead. After finding the optimal values of the proposed...
The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the...
Nowadays, direct-drive systems are widely used in the actuators of computer numerical control machine tools. Linear motors are widely used in high-end computer numerical control machine tools due to their high positioning accuracy, good dynamic respo...
Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) meth...
With the rapid development of indoor positioning technology, radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-line-of-sight, non-contact and rapid identification. However, the accuracy of...
The global population growth has led to a considerable rise in demand for wheat. Today, the amount of energy consumption in agriculture has also increased due to the need for sufficient food for the growing population. Thus, agricultural policymakers...
Accurate forecast of PM2.5 pollution is highly needed for the timely prevention of haze pollution in many cities suffered from frequent haze pollution. In this work, an online recurrent extreme learning machine (OR-ELM) technique with online data upd...
This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge...
The reliable and safe operation of traction elevators depends on traction capacity, which is degraded by traction sheave groove wear. The resulting slippage reduces transmission efficiency and may cause a catastrophic failure due to the sudden loss o...
Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment...
Accurate wind speed forecasting plays a significant role for grid operators and the use of wind energy, which helps meet increasing energy needs and improve the energy structure. However, choosing an accurate forecasting system is a challenging task....
Monthly runoff prediction plays a crucial role in water resource management, flood prevention, and disaster reduction. This study proposed a novel hybrid model for predicting monthly runoff by combining variational modal decomposition (VMD) with an e...
Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparame...
In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (T...
Accurate photovoltaic (PV) power forecasting degrades when models are deployed across sites or seasons, primarily due to distribution shift (amplitude bias and scale mismatch) and anomalous contamination—with pronounced amplitude–phase er...
Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the d...
Finding the best configuration of a neural network’s hyper-parameters may take too long to be feasible using an exhaustive search, especially when the cardinality of the search space has a big combinatorial number of possible solutions with var...
Natural wetland ecosystems provide not only important habitats for many wildlife species, but also food for migratory and resident animals. In Shanghai, the Chongming Dongtan International Wetland, located at the mouth of the Yangtze River, plays an...
In recent years, it has become inevitable to dig underneath existing highways when excavating tunnels. The soil settlement induced by ground excavation may adversely affect existing highways. In this study, a settlement monitoring system is used to o...
Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusi...
Hysteresis is a non−unique phenomenon known as a multi−valued mapping in different fields of science and engineering. Accurate identification of the hysteretic systems is a crucial step in hysteresis compensation and control. This study proposes a no...
Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computi...
Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crud...
Background: Accurate liver segmentation from abdominal computed tomography (CT) images is an important task for surgical planning, volumetric analysis, and tumor assessment. Although recent deep learning-based three-dimensional segmentation approache...
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