scholarly journals An Enhanced Ad Event-Prediction Method Based on Feature Engineering

2019 ◽  
Author(s):  
Saeid Soheily-Khah ◽  
Yiming Wu
Keyword(s):  
Prediction Method ◽  
Feature Engineering ◽  
Event Prediction

scholarly journals Dynamic Pollution Emission Prediction Method of a Combined Heat and Power System Based on the Hybrid CNN-LSTM Model and Attention Mechanism

2021 ◽  
Author(s):  
Anping Wan ◽  
Jie Yang ◽  
Ting Chen ◽  
Yang Jinxing ◽  
Ke Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power System ◽  
Prediction Method ◽  
Learning Model ◽  
Combined Heat And Power ◽  
Attention Mechanism ◽  
Feature Engineering ◽  
Pollution Emission ◽  
Highly Nonlinear ◽  
Deep Learning Model

Abstract The prediction of pollution emission from a combined heat and power (CHP) system is very important for the production regulation and emergency response of a power system. The composition and structure of the CHP equipment are complex, and the production process is cumbersome. The fuel chemical reaction of the pulverized coal in the boiler represents a highly nonlinear and strongly interrelated process that is strongly affected by external environmental factors, which causes a certain level of volatility and uncertainty. In this study, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network-long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.

Froth image feature engineering-based prediction method for concentrate ash content of coal flotation

2021 ◽  
Vol 170 ◽  
pp. 107023
Author(s):  
Zhiping Wen ◽  
Changchun Zhou ◽  
Jinhe Pan ◽  
Tiancheng Nie ◽  
Ruibo Jia ◽  
...  
Keyword(s):  
Prediction Method ◽  
Ash Content ◽  
Image Feature ◽  
Feature Engineering ◽  
Coal Flotation ◽  
Froth Image

Predicting event mentions based on a semantic analysis of microblogs for inter-region relationships

2018 ◽  
Vol 44 (6) ◽  
pp. 818-829 ◽  
Author(s):  
Gwan Jang ◽  
Sung-Hyon Myaeng
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Online Social Network ◽  
Prediction Method ◽  
The Novel ◽  
User Interests ◽  
Event Prediction ◽  
Markov Cluster Algorithm ◽  
Novel Method

An ability to predict people’s interests in different regions would be valuable to many applications including marketing and policymaking. We posit that social media plays an important role in capturing collective user interests in different regions and their dynamics over time and across regions. Event mentions in microblogs of social media like Twitter not only reflect the people’s interests in different regions but also affect the posting of future messages as the content of microblogs propagates to others through an online social network. Differentiating from the various network analysis techniques that have been developed to capture people’s interests and their propagation patterns, we propose an event mention prediction method that utilises an analysis of inter-region relationships. We first obtain regional user interests for each topic by applying Latent Dirichlet Allocation (LDA) to region-specific collections of tweets and then compute pairwise similarities among regions. The resulting similarity-based region network becomes the basis for constructing region groups through Markov Cluster Algorithm, which helps removing noise relationships among regions. We then propose a relatively simple regression technique to predict future event mentions in different regions. We demonstrate that the proposed method outperforms the state-of-the-art event prediction method, confirming that the novel method of constructing groups from region-based sub-topic interests indeed contributes to the increase in the prediction accuracy.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

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