Novel intelligent adjustment height method of Shearer drum based on adaptive fuzzy reasoning Petri net

2021 ◽  
pp. 1-15
Author(s):  
Weibing Wang ◽  
Shenquan Wang ◽  
Shuanfeng Zhao ◽  
Zhengxiong Lu ◽  
Haitao He
Keyword(s):  
Petri Net ◽  
Fuzzy Inference ◽  
Regression Tree ◽  
Fuzzy Reasoning ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Adaptive Fuzzy ◽  
Fuzzy Petri Net ◽  
Non Linear ◽  
Height Model

The complexity of the coalface environment determines the non-linear and fuzzy characteristics of the drum adjustment height. To overcome this challenge, this study proposes an adaptive fuzzy reasoning Petri net (AFRPN) model based on fuzzy reasoning and fuzzy Petri net (FPN) and then applies it to the intelligent adjustment height of the shearer drum. This study constructs adaptive and reasoning algorithms. The former was used to optimize the AFRPN parameters, and the latter made the AFRPN model run. AFRPN could represent rules that had non-linear and attribute mapping relationships and could adjust the parameters adaptively to improve the accuracy of the output. Subsequently, the drum adjustment height model was established and compared to three models neural network (NN), classification and regression tree(CART) and gradient boosting decision tree (GBDT). The experimental results showed that this method is superior to other drum adjustment height methods and that AFRPN can achieve intelligent adjustment of the shearer drum height by constructing fuzzy inference rules.

Effect of Odia and Tamil Music on the ANS and the Conduction Pathway of Heart of Odia Volunteers

2017 ◽  
pp. 240-263 ◽  
Author(s):  
Suraj Kumar Nayak ◽  
Utkarsh Srivastava ◽  
D. N. Tibarewala ◽  
Goutam Thakur ◽  
Biswajit Mohapatra ◽  
...  
Keyword(s):  
Mother Tongue ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Cardiac Conduction ◽  
Linear Classifiers ◽  
Ecg Signals ◽  
Conduction Pathway ◽  
Non Linear ◽  
Boosted Tree ◽  
Classification And Regression

The current study delineates the effect of Odia and Tamil music on the Autonomic Nervous System (ANS) and cardiac conduction pathway of Odia volunteers. The analysis of the ECG signals using Analysis of Variance (ANOVA) showed that the features obtained from the HRV domain, time-domain and wavelet transform domain were statistically insignificant. But non-linear classifiers like Classification and Regression Tree (CART), Boosted Tree (BT) and Random Forest (RF) indicated the presence of important features. A classification efficiency of more than 85% was achieved when the important features, obtained from the non-linear classifiers, were used. The results suggested that there is an increase in the parasympathetic activity when music is heard in the mother tongue. If a person is made to listen to music in the language with which he is not conversant, an increase in the sympathetic activity is observed. It is also expected that there might be a difference in the cardiac conduction pathway.

A Data-Analytics Tutorial: Building Predictive Models for Oil Production in an Unconventional Shale Reservoir

SPE Journal ◽  
2018 ◽  
Vol 23 (04) ◽  
pp. 1075-1089 ◽  
Author(s):  
Jared Schuetter ◽  
Srikanta Mishra ◽  
Ming Zhong ◽  
Randy LaFollette (ret.)
Keyword(s):  
Predictive Models ◽  
Decision Rules ◽  
Regression Tree ◽  
Production Performance ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Well Completion

Summary Considerable amounts of data are being generated during the development and operation of unconventional reservoirs. Statistical methods that can provide data-driven insights into production performance are gaining in popularity. Unfortunately, the application of advanced statistical algorithms remains somewhat of a mystery to petroleum engineers and geoscientists. The objective of this paper is to provide some clarity to this issue, focusing on how to build robust predictive models and how to develop decision rules that help identify factors separating good wells from poor performers. The data for this study come from wells completed in the Wolfcamp Shale Formation in the Permian Basin. Data categories used in the study included well location and assorted metrics capturing various aspects of well architecture, well completion, stimulation, and production. Predictive models for the production metric of interest are built using simple regression and other advanced methods such as random forests (RFs), support-vector regression (SVR), gradient-boosting machine (GBM), and multidimensional Kriging. The data-fitting process involves splitting the data into a training set and a test set, building a regression model on the training set and validating it with the test set. Repeated application of a “cross-validation” procedure yields valuable information regarding the robustness of each regression-modeling approach. Furthermore, decision rules that can identify extreme behavior in production wells (i.e., top x% of the wells vs. bottom x%, as ranked by the production metric) are generated using the classification and regression-tree algorithm. The resulting decision tree (DT) provides useful insights regarding what variables (or combinations of variables) can drive production performance into such extreme categories. The main contributions of this paper are to provide guidelines on how to build robust predictive models, and to demonstrate the utility of DTs for identifying factors responsible for good vs. poor wells.

scholarly journals Estimating the Optimal Dexketoprofen Pharmaceutical Formulation with Machine Learning Methods and Statistical Approaches

2021 ◽  
Vol 27 (4) ◽  
pp. 279-286
Author(s):  
Atakan Başkor ◽  
Yağmur Pirinçci Tok ◽  
Burcu Mesut ◽  
Yıldız Özsoy ◽  
Tamer Uçar
Keyword(s):  
Machine Learning ◽  
Regression Tree ◽  
Cost Effective ◽  
Pharmaceutical Formulation ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Disintegration Time ◽  
Pharmaceutical Dosage Form ◽  
Extreme Gradient Boosting

Objectives: Orally disintegrating tablets (ODTs) can be utilized without any drinking water; this feature makes ODTs easy to use and suitable for specific groups of patients. Oral administration of drugs is the most commonly used route, and tablets constitute the most preferable pharmaceutical dosage form. However, the preparation of ODTs is costly and requires long trials, which creates obstacles for dosage trials. The aim of this study was to identify the most appropriate formulation using machine learning (ML) models of ODT dexketoprofen formulations, with the goal of providing a cost-effective and timereducing solution.Methods: This research utilized nonlinear regression models, including the k-nearest neighborhood (k-NN), support vector regression (SVR), classification and regression tree (CART), bootstrap aggregating (bagging), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods, as well as the t-test, to predict the quantity of various components in the dexketoprofen formulation within fixed criteria.Results: All the models were developed with Python libraries. The performance of the ML models was evaluated with R2 values and the root mean square error. Hardness values of 0.99 and 2.88, friability values of 0.92 and 0.02, and disintegration time values of 0.97 and 10.09 using the GBM algorithm gave the best results.Conclusions: In this study, we developed a computational approach to estimate the optimal pharmaceutical formulation of dexketoprofen. The results were evaluated by an expert, and it was found that they complied with Food and Drug Administration criteria.

scholarly journals Reliability Evaluation for Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 167276-167287
Author(s):  
Lixiang Wang ◽  
Wei Dai ◽  
Jun Ai ◽  
Weiwei Duan ◽  
Yu Zhao
Keyword(s):  
Petri Net ◽  
Manufacturing System ◽  
Fuzzy Reasoning ◽  
Reliability Evaluation ◽  
Adaptive Fuzzy

scholarly journals Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2849 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Yumin Guo ◽  
Xuesong Han ◽  
Lijia Wen
Keyword(s):  
Random Forest ◽  
Species Distribution ◽  
Performance Metrics ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Supporting Evidence ◽  
Boosted Regression Tree ◽  
Stochastic Gradient Boosting

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha,n = 33), White-naped Crane (Grus vipio,n = 40), and Black-necked Crane (Grus nigricollis,n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation.

scholarly journals Using Hybrid Artificial Intelligence Approaches to Predict the Fracture Energy of Concrete Beams

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qinghua Xiao ◽  
Congming Li ◽  
Shengxiang Lei ◽  
Xiangyu Han ◽  
Qiaofeng Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fracture Energy ◽  
Concrete Structures ◽  
Regression Tree ◽  
Pso Algorithm ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Significant Variable ◽  
Test Dataset ◽  
Hybrid Artificial Intelligence

Fracture energy is always used to represent the fracture performance of concrete structures/beams, which is crucial for the application of concrete. However, due to the nature of concrete material and the complexity of the fracture process, it is difficult to accurately determine the fracture energy of concrete and predict the fracture behavior of different concrete structures. In this study, artificial intelligence approaches were tried to seek a feasible way to solve these prediction issues. Firstly, the ridge regression (RR), the classification and regression tree (CART), and the gradient boosting regression tree (GBRT) were selected to construct the predictive models. Then, the hyperparameters were tuned with the particle swarm optimization (PSO) algorithm; the performances of these three optimum models were compared with the test dataset. The mean squared errors (MSEs) of the optimum RR, CART, and GBRT models were 0.0447, 0.0164, and 0.0111, respectively, which indicated that their performances were excellent. Compared with the RR and CART models, the hybrid model constructed with GBRT and PSO appeared to be the most accurate and generalizable, both of which are significant for prediction work. The relative importance of the variables that influence the fracture energy of concrete was obtained, and compressive strength was found to be the most significant variable.

scholarly journals Why to choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

2016 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Yumin Guo ◽  
Xuesong Han ◽  
Lijia Wen
Keyword(s):  
Random Forest ◽  
Species Distribution ◽  
Performance Metrics ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Supporting Evidence ◽  
Boosted Regression Tree ◽  
Stochastic Gradient Boosting

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution, and more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n=33), White-naped Crane (Grus vipio, n=40), and Black-necked Crane (Grus nigricollis, n=75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models) Besides, we developed an ensemble forecast by averaging predicted probability of above four models results. Commonly-used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. Latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years, and by now, has been known to perform extremely well in ecological predictions. However, while increasingly on the rise its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and it allows robust and rapid assessments and decisions for efficient conservation.

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