Implementation of Adaptive Neuro-Fuzzy Inference System and Image Processing for Design Applications Paper Age Prediction DOI Creative Commons

Valeria Cynthia Dewi,

Victor Amrizal,

Fenty Eka Muzayyana Agustin

et al.

Jurnal Riset Ilmu Teknik, Journal Year: 2023, Volume and Issue: 1(1), P. 45 - 57

Published: May 31, 2023

The development of technology today is widely misused by some people who intend to forge paper on documents and books. One way find out the authenticity a knowing its age. age can be known in several ways: carbon dating, uranium potassium-argon dating. But these methods still have weaknesses, requiring sophisticated equipment at high cost, long processes get results limited access. To solve this problem, researchers made an application that identify range sheet with faster process, low cost does not used laboratory employees alone. Paper Age Prediction Application desktop-based, using MATLAB programming language Anfis Sugeno (TSK) Gaussian membership function method. Image processing taking average values C, M, Y, K from 70 images as database will trained ANFIS. research method uses interviews, observations, literature studies—the prototype test showed success rate identifying 60 data had been 100% against 40 42.5%.

Language: Английский

A Kernel Extreme Learning Machine-Grey Wolf Optimizer (KELM-GWO) Model to Predict Uniaxial Compressive Strength of Rock DOI Creative Commons
Chuanqi Li, Jian Zhou, Daniel Dias

et al.

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(17), P. 8468 - 8468

Published: Aug. 24, 2022

Uniaxial compressive strength (UCS) is one of the most important parameters to characterize rock mass in geotechnical engineering design and construction. In this study, a novel kernel extreme learning machine-grey wolf optimizer (KELM-GWO) model was proposed predict UCS 271 samples. Four namely porosity (Pn, %), Schmidt hardness rebound number (SHR), P-wave velocity (Vp, km/s), point load (PLS, MPa) were considered as input variables, output variable. To verify effectiveness accuracy KELM-GWO model, machine (ELM), KELM, deep (DELM) back-propagation neural network (BPNN), empirical established compared with UCS. The root mean square error (RMSE), determination coefficient (R2), absolute (MAE), prediction (U1), quality (U2), variance accounted for (VAF) adopted evaluate all models study. results demonstrate that best predicting performance indices. Additionally, identified parameter by using impact value (MIV) technique.

Language: Английский

Citations

34
Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete DOI
Xiancheng Mei, Chuanqi Li, Qian Sheng

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2022, Volume and Issue: 30(11), P. 2185 - 2202

Published: March 23, 2022

This study, proposes the use of a novel rubber-sand concrete (RSC) material, which comprises rubber particles, sand, and cement, as an aseismic material in practical engineering construction. The uniaxial compressive strength (UCS) damping materials is important factor that directly affects seismic activity underground structures. To predict UCS RSC, artificial intelligence model back propagation neural network (BPNN), optimized through four swarm optimization (SIO) algorithms: particle algorithm (PSO), fruit fly (FOA), lion (LSO), sparrow search (SSA), used. dataset for prediction models was obtained from compression tests RSC laboratory. performances hybrid were evaluated using six performance indicators: root mean square error (RMSE), correlation coefficient (R), determination (R2), absolute (MAE), (MSE), sum (SSE).The capability these graded based on indicators ranking system. results show ability LSO-BPNN better than three other models, with RMSE (1.0635, 1.2352), R (0.9887, 0.9713), R2 (0.9776, 0.9165), MAE (0.7257, 0.8243), MSE (1.1352, 1.5256), SSE (64.7074, 36.6151), score (24, 24) training testing phases, respectively. Therefore, efficient accurate method predicting RSCs. Sensitivity analysis showed sand most elements affected prediction, followed by lowest relative importance being RPZ. study provides guidance extension application to engineering.

Language: Английский

Citations

33
Feature selection using metaheuristics made easy: Open source MAFESE library in Python DOI

Nguyen Van Thieu,

Ngoc Hung Nguyen, Ali Asghar Heidari

et al.

Future Generation Computer Systems, Journal Year: 2024, Volume and Issue: 160, P. 340 - 358

Published: June 7, 2024

Language: Английский

Citations

8
Ventilation on demand in underground mines using neuro-fuzzy models: Modeling and laboratory-scale experimental validation DOI
Ahmad Ihsan, Nuhindro Priagung Widodo, Jianwei Cheng

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108048 - 108048

Published: Feb. 19, 2024

Language: Английский

Citations

7
An enhanced stability evaluation system for entry-type excavations: Utilizing a hybrid bagging-SVM model, GP and kriging techniques DOI Creative Commons
Shuai Huang, Jian Zhou

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 1, 2024

In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis critical span graph must be updated meet more stringent engineering requirements. Given this, this study introduces support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) optimization (Harris hawks (HHO), cuckoo search (CS)) techniques, overcome limitations traditional methods. The indicates that hybrid model combining SVM, bagging, CS strategies has good prediction performance, its test accuracy reaches 0.86. Furthermore, partition scheme is adjusted based on CS-BSVM 399 cases. Compared previous empirical or semi-empirical methods, new overcomes interference subjective factors possesses higher interpretability. Since relying solely one technology cannot ensure credibility, further genetic programming (GP) kriging interpolation techniques. explicit expressions derived through GP offer probability value, technique provide interpolated definitions two subclasses. Finally, platform developed above three approaches, which rapidly feedback.

Language: Английский

Citations

7
Numerical Simulation of Gas Extraction in Coal Seam Strengthened by Static Blasting DOI Open Access
Xiaoqiang Zhang,

Fengyuan Zhou,

Jiaxing Zou

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(19), P. 12484 - 12484

Published: Sept. 30, 2022

For mines with low permeability and high gas emissions, static blasting technology is used to pre-split the coal seam increase strengthen extraction, which will significantly reduce occurrence of accidents in mines. Taking Wangjialing Coal Mine as research object, mathematical model fluid-solid established. The numerical simulation software COMSOL simulate established model. Simultaneously, factors affecting efficiency extraction are analyzed by adjusting parameters. results reveal a more significant drop pressure increasing time. At 10 d, 30 90 d 180 increases 11.80%, 18.67%, 22.22% 24.13% comparison conventional extraction. In studying influence expansion other on during blasting, it found that change negative has little effect Static can achieve safe mining, providing basis field application efficient

Language: Английский

Citations

27
Research and Application of an Intelligent Prediction of Rock Bursts Based on a Bayes-Optimized Convolutional Neural Network DOI

Mingliang Li,

Kegang Li, Qingci Qin

et al.

International Journal of Geomechanics, Journal Year: 2023, Volume and Issue: 23(5)

Published: March 2, 2023

Intelligent prediction of rock bursts has great significance in mechanics research and a high value engineering applications. An intelligent rockburst method based on Bayes-optimized convolutional neural network (BOCNN) was proposed. First, an exploratory analysis data conducted using joint distribution diagrams the heat map correlation matrix to establish high-quality set cases parameter system for prediction. Second, six models were built by combining machine learning algorithms, such as random forest, k-nearest neighbor (KNN), Bayes, deep (CNN1d CNN2d), BOCNN. In addition, we used accuracy, precision, recall, F1 score, receiver operating characteristic curve, Taylor diagram, probability indicator results indicators evaluate accuracy models. A comparative explore with good robustness, generalization performance, accuracy. Moreover, 11 established analysis. Then, MATLAB tool build applied findings Jiangbian Hydropower Station Sichuan Province, China. The study show that can provide technical support predicting hazards mining, transportation, water conservancy hydropower projects scientific basis later construction design structures.

Language: Английский

Citations

15
Deformation extent prediction of roadway roof during non-support period using support vector regression combined with swarm intelligent bionic optimization algorithms DOI
Bingbing Yu, Qing Li,

Tongde Zhao

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 145, P. 105585 - 105585

Published: Jan. 11, 2024

Language: Английский

Citations

6
Hybrid method for analyzing air thermal conditions in underground mines DOI
Ahmad Ihsan, Jianwei Cheng, Nuhindro Priagung Widodo

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 245, P. 123026 - 123026

Published: Jan. 1, 2024

Language: Английский

Citations

5
Regulation and Optimization of Air Quantity in a Mine Ventilation Network with Multiple Fans DOI Open Access
Jinmiao Wang, Mingtao Jia, Bin Lin

et al.

Archives of Mining Sciences, Journal Year: 2023, Volume and Issue: unknown

Published: July 20, 2023

Regulation and optimization of aiR Quantity in a mine Ventilation netwoRk with multiple fans the ventilation system underground is an important guarantee for workers' safety environmental conditions.As mining activities continue, constantly changing.therefore, to ensure on demand, network regulation are very important.in this paper, path method based graph theory studied.however, existing algorithms do not meet needs actual optimization.therefore, algorithm optimized improved from four aspects.First, depth-first search algorithm, independent proposed solve problem false paths searched when there unidirectional circuit network.Secondly, calculation formula amended that number downcast upcast shaft, multi-downcast multi-upcast shaft circuits calculated accurately.thirdly, avoid both increase control points multi-fan disturbances airflow distribution by determining reference through all paths, shared fan must be identified.Fourthly, position regulators determined optimized, final air quantity realized.the case study shows can effectively accurately realize network.

Language: Английский

Citations

12