Nanopositioning technology is widely used in high-resolution applications. It often uses piezoelectric actuators due to their superior characteristics. However, exhibit a hysteresis phenomenon that limits positioning accuracy. To compensate for the effect, developing an accurate model of very important. This task challenging, requiring some considerations multivalued mapping loops and generaliz...

Abstract Every country must have an accurate and efficient forecasting model to avoid manage the epidemic. This paper suggests upgrade one of evolutionary algorithms inspired by nature, Barnacle Mating Optimizer (BMO). First, exploration phase original BMO is enhanced enforcing replacing sperm cast equation through Levy flight. Then, Least Square Support Vector Machine (LSSVM) partnered with im...

A new method based on least square support vector machine (LSSVM) combined with FastICA is proposed to extract the fetal electrocardiogram (FECG) from the abdominal signals of a pregnant woman. Firstly, the LSSVM is applied to estimate the maternal electrocardiogram (MECG) component in the multiplex abdominal signals. Then the optimal estimation of multiplex noise-added FECG is obtained by remo...

Machine learning algorithms are extensively used to reduce the complexity of applied problems in various fields, including energy. Accurate prediction performance water alternating gas (WAG) injection as an enhanced oil recovery (EOR) process is great importance optimal management hydrocarbon resources. In current work, a hybrid mathematical model proposed for near-immiscible WAG process. We us...

The problem of low digging efficiency and mining imbalance due to outdated technology degree equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key roadheading equipment. Accurate lithology significantly affects automatic control roadheader cutting conditions. Completing process rock wall by a involved following steps: building tunnelin...

In order to change the insufficiency of traditional network flow prediction and improve its accuracy, the paper proposed a kind of network flow prediction method based on the self-adaptive genetic least square support vector machine optimization. Through analyzing the individual parameter of the LS-SVM principle and self-adaptive remains algorithm, the network flow prediction model structure of...

In order to realize real-time and precise monitoring of the tool wear in milling process, this paper presents a predictive model based on stacked multilayer denoising autoencoders (SMDAE) technique, particle swarm optimization with an adaptive learning strategy (PSO-ALS), least squares support vector machine (LSSVM). Cutting force vibration information are adopted as signals. Three steps make u...

In power generation industries, boilers are required to be operated under a range of different conditions accommodate demands for fuel randomness and energy fluctuation. Reliable prediction the combustion operation condition is crucial an in-depth understanding boiler performance maintaining high efficiency. However, it difficult establish accurate model based on traditional data-driven methods...