Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines....
The volatility of the cumulative displacement of landslides is related to the influence of external factors. To improve the prediction of nonlinear changes in landslide displacement caused by external influences, a new combined forecasting model of l...
Effective prediction of wastewater treatment is beneficial for precise control of wastewater treatment processes. The nonlinearity of pollutant indicators such as chemical oxygen demand (COD) and total phosphorus (TP) makes the model difficult to fit...
The frequent and sharp fluctuations in garlic prices seriously affect the sustainable development of the garlic industry. Accurate prediction of garlic prices can facilitate correct evaluation and scientific decision making by garlic practitioners, t...
In order to accurately predict the gas concentration, find out the gas abnormal emission in advance, and take effective measures to reduce the gas concentration in time, this paper analyzes multivariate monitoring data and proposes a new dynamic comb...
Combined high and low cycle fatigue (CCF) generally induces the failure of aircraft gas turbine attachments. Based on the aero-engine load spectrum, accurate assessment of fatigue damage due to the interaction of high cycle fatigue (HCF) resulting fr...
A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power....
With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict win...
Accurate landslide displacement prediction is an essential prerequisite for early warning systems aimed at mitigating geological hazards. However, the inherent nonlinearity and dynamic complexity of landslide evolution often hinder forecasting perfor...
A novel method is proposed for a combined high and low cycle fatigue (CCF) life prediction model based on Miner’s rule, incorporating load interactions and coupled damage effects to evaluate the fatigue life of wind turbine blades under CCF loa...
With the acceleration of industrialization, the impact of the toxic metalloid arsenic (As) and metal lead (Pb) on aquatic ecosystems has garnered widespread concern. However, the specific toxic effects of how these two metals jointly impact aquatic o...
The deviation of conveyor belts is a key factor that restricts material conveying efficiency of horizontal curves. An evaluation system of the deviation state of curve conveyor belt based on the ARIMA–LSTM combined prediction model is proposed...
Heat rate of a combined cycle power plant (CCPP) is a parameter that is typically used to assess how efficient a power plant is. In this paper, the CCPP heat rate was predicted using an artificial neural network (ANN) method to support maintenance pe...
The widely distributed “Step-type” landslides in the Three Gorges Reservoir (TGR) area have caused serious casualties and heavy economic losses. The prediction research of landslide displacement will be beneficial to the establishment of...
The frequent fluctuation of pork prices has seriously affected the sustainable development of the pork industry. The accurate prediction of pork prices can not only help pork practitioners make scientific decisions but also help them to avoid market...
A rockburst is a geological disaster that occurs in resource development or engineering construction. In order to reduce the harm caused by rockburst, this paper proposes a prediction study of rockburst propensity based on the intuitionistic fuzzy se...
To attain precise forecasts of surface displacements and deformations in goaf areas (a void or cavity that remains underground after the extraction of mineral resources) following coal extraction, this study based on the limitations of individual tim...
With rapid urban development, the “waste-free city” concept has emerged. Therefore, the accurate prediction of the amount of C&D waste is of great importance. However, many countries and regions, including China, have not yet establis...
This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks,...
Accurate prediction of dam inflows is essential for effective water resource management and dam operation. In this study, we developed a multi-inflow prediction ensemble (MPE) model for dam inflow prediction using auto-sklearn (AS). The MPE model is...
In order to effectively deal with the adverse effects of the randomness of photovoltaic output on the operation of combined heat and power (CHP) microgrids, this paper proposes an adaptive robust optimal scheduling strategy for CHP microgrids based o...
To address the problem of strong uncertainty in the high proportion of new energy output, an improved convolutional long- and short-term memory (CLSTM) hybrid neural network is proposed for PV power combination prediction. Firstly, considering the la...
The corrosion of oil and gas pipelines represents a significant factor influencing the safety of these pipelines. The extant research on intelligent algorithms for assessing corrosion rates in pipelines has primarily focused on static evaluation meth...
Feeding rates serve as a vital indicator for adjusting the working parameters of the combine harvester. A non-invasive diagnostic approach to predicting the feed rates of combine harvesters by collecting vibration signals of the inclined conveyor was...
Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower acc...
Combination prediction models have gained great development in the area of information science, and are widely applied in engineering fields. The underwater glider (UG) is a new type of unmanned vehicle used in ocean observation for the advantages of...
Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need t...
In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration...
This paper proposes a new linear combination model to predict the closing prices on multivariate financial data sets. The new approach integrates two delays of deep learning methods called the two-delay combination model. The forecasts are derived fr...
3 July 2020
Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-d...
The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IEALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for...
Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control s...
Groundwater level prediction is essential for sustainable water resource management. Although machine learning models are widely applied, input variable selection critically affects predictive performance, and existing studies rarely evaluate model p...
The accurate prediction of residential heat load is crucial for effective heating system design, energy management, and cost optimization. In order to further improve the prediction accuracy of the model, this study introduced principal component ana...
Universal Time (UT1) is a core component of the Earth orientation parameters (EOP). High-precision UT1 predictions are essential for satellite navigation, deep-space exploration, and the maintenance of national standard time. Although effective angul...
Using construction and demolition waste (CDW) as road subgrade filling materials is an excellent way to solve the disparity between increased demand and road construction aggregate shortages. However, a key quality control problem is predicting the s...
Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and im...
The outbreak of coronavirus disease 2019 (COVID-19) has caused a global disaster, seriously endangering human health and the stability of social order. The purpose of this study is to construct a nonlinear combinational dynamic transmission rate mode...
Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of pos...
Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a...
Background: Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA) is a rare form of primary liver malignancy. Microvascular invasion (MVI) indicates poor postsurgical prognosis in cHCC-CCA. The objective of this study was to investigate...
To reduce or evaluate the damage of tunnels in seismically active areas when earthquakes happen, it is very important to quickly predict the tunnel damage. This study proposes an anti-entropy–fuzzy analytic hierarchy process (FAHP) combination...
Habitat analysis enables spatial characterization of intratumoral heterogeneity; however, its application in nasopharyngeal carcinoma (NPC), particularly regarding metastatic lymph node (MLN), remains limited. This study aims to systematically compar...
Deep excavation engineering is a comprehensive discipline that involves multiple fields such as engineering geology, hydrogeology, and foundation engineering. With the improvement of the utilization rate of underground space, the demand for the const...
Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genet...
Oil monitoring for wind turbine gearboxes can reflect wear and lubrication conditions, and better identify pits on the tooth surface, fatigue wear, and other early faults. However, oil monitoring with one or several single predicting models brings in...
At present, the data related to construction safety accidents in China have the characteristics of small sample size, large fluctuation, large base, nonlinearity, etc. It is of great significance in accident prevention to break through the limitation...
In complex field environments, wheat grows densely with overlapping organs and different plant weights. It is difficult to accurately predict feed quantity for wheat combine harvester using the existing YOLOv5s and uniform weight of a single wheat pl...
The accurate forecasting of short-term subway passenger flow is beneficial for promoting operational efficiency and passenger satisfaction. However, the nonlinearity and nonstationarity of passenger flow time series bring challenges to short-term pas...
It is critically meaningful to accurately predict the ionospheric F2 layer critical frequency (foF2), which greatly limits the efficiency of communications, radar, and navigation systems. This paper introduced the entropy weight method to develop the...
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