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Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第2期11卷 1-12页

作者： Pan, Lijun Zhengzhou Normal Univ Coll Elementary Educ Zhengzhou 450044 Henan Peoples R China

INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial ***: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation ***: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements;then, the intelligent learning model is constructed through a k-means clustering algorithm and deep compression sparse self-encoder;finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment ***: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient ***: The results show that the proposed method improves the model's accuracy.

关键词： Intelligent learning model Model element extraction k-means clustering algorithm Deep compression sparse self-encoder

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Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

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WATER 2024年第19期16卷 2786页

作者： Liu, Weitao Han, Mengke Zhao, Jiyuan Shandong Univ Sci & Technol Coll Safety & Environm Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number k = 3 was determined. The collected data were clustered into three categories using the k-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7 similar to 414.9 m and 750 similar to 900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified

关键词： floor failure depth k-means clustering algorithm grey correlation analysis CatBoost algorithm groundwater

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A New Discharge Pattern for the Characterization and Identification of Insulation Defects in GIS

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ENERGIES 2018年第4期11卷 971页

作者： Yao, Rui Hui, Meng Li, Jun Bai, Lin Wu, Qisheng Changan Univ Sch Elect & Control Engn Xian 710064 Shaanxi Peoples R China State Grid Shaanxi Elect Power Res Inst Xian 710049 Shaanxi Peoples R China

Identification of insulation defects in gas insulated metal-enclosed switchgear (GIS) is important for partial discharge (PD) evaluation. This article proposes a polar coordinate pattern approach to characterize the different kinds of defect types. These defect types include floating electrodes, a fixed protrusion on the enclosure, surface contamination on the spacer, metallic prominence on the high voltage electrode, a void in the insulator, and free metal particles on the enclosure. First, the physical models for the insulation defects in the established GIS model are designed. Second, the phase resolved pulse sequence (PRPS) data sets are obtained using ultra-high frequency (UHF) measurement. Then, the polar coordinate patterns are proposed to characterize the defects. Nine discharge parameters combined with the parameters based on quadrant statistical theory constitute the input feature vector to identify the PD types. The experimental results show that these new parameters could produce a clear, quantitative description of the characteristics of the defect types and could be used to distinguish between the different kinds of defect types.

关键词： gas insulated metal-enclosed switchgear (GIS) partial discharge polar coordinate pattern k-means clustering algorithm feature vector classification

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A new method for building roof segmentation from airborne LiDAR point cloud data

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MEASUREMENT SCIENCE AND TECHNOLOGY 2013年第9期24卷 095402. doi:10.1088/0957-0233/24/9/095402-095402. doi:10.1088/0957-0233/24/9/095402页

作者： kong, Deming Xu, Lijun Li, Xiaolu Beihang Univ Sch Instrument Sci & Optoelect Engn State Key Lab Inertial Sci & Technol Beijing 100191 Peoples R China Beihang Univ Sch Instrument Sci & Optoelect Engn Minist Educ Key Lab Precis Optomechatron Technol Beijing 100191 Peoples R China

A new method based on the combination of two kinds of clustering algorithms for building roof segmentation from airborne LiDAR (light detection and ranging) point cloud data is proposed. The k-plane algorithm is introduced to classify the laser footprints that cannot be correctly classified by the traditional k-means algorithm. High-precision classification can be obtained by combining the two aforementioned clustering algorithms. Furthermore, to improve the performance of the new segmentation method, a new initialization method is proposed to acquire the number and coordinates of the initial cluster centers for the k-means algorithm. In the proposed initialization method, the geometrical planes of a building roof are estimated from the elevation image of the building roof by using the mathematical morphology and Hough transform techniques. By calculating the number and normal vectors of the estimated geometrical planes, the number and coordinates of the initial cluster centers for the k-means algorithm are obtained. With the aid of the proposed initialization and segmentation methods, the point cloud of the building roof can be rapidly and appropriately classified. The proposed methods are validated by using both simulated and real LiDAR data.

关键词： LiDAR segmentation of point cloud k-means clustering algorithm k-plane clustering algorithm estimation of cluster centers mathematical morphology

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Online Detection of Turning Tool Wear Based on Machine Vision

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2022年第5期22卷 050903-050903页

作者： Dong, Xinfeng Li, Yongsheng Weifang Univ Sch Mech & Automat Weifang 261061 Peoples R China Shandong Tianrui Heavy Ind Co Ltd Weifang 261061 Peoples R China Shandong Magnet Suspens Technol Ind Res Inst Weifang 261061 Peoples R China

To identify the tool wear in real time, this article builds the online detection system and proposes an integrated image processing method to measure the wear width for flank face of turning tool. First, the images of flank face wear are collected by the image acquisition system. Second, the collected images are cropped near the flank face wear, and the wear area and background for the cropped images are separated by k-means clustering algorithm. Then, the wear edge is identified by the grayscale transformation and edge detection algorithm, and the wear width is calculated by Hough transformation. Finally, the cutting experiment is carried out on MAG HTC200 CNC lathe to verify the validity of the proposed method, and the results show that the identified wear width by the proposed method and actual measured wear width for flank face of turning tool are in good agreement and show the proposed method is effective and have micron scale calculation accuracy.

关键词： wear width flank face of turning tool k-means clustering algorithm Hough transform online detection machine vision

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Detection and diagnosis of process fault using unsupervised learning methods and unlabeled data

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INTERNATIONAL JOURNAL OF ADVANCES IN ENGINEERING SCIENCES AND APPLIED MATHEMATICS 2023年第1期15卷 24-36页

作者： Rahoma, Abdalhamid Imtiaz, Syed Ahmed, Salim khan, Faisal Mem Univ Newfoundland Fac Engn & Appl Sci Ctr Risk Integr & Safety Engn St John NF Canada Texas A&M Univ Mary Kay OConnor Proc Safety Ctr Dept Chem Engn Artie McFerrin College Stn TX USA Mem Univ Newfoundland Fac Engn & Appl Sci Ctr Risk Integr & Safety Engn C RISE 240 Prince Philip Dr St John NF A1B 3X5 Canada

Supervised learning methods, commonly used for process monitoring, require labeled historical datasets for normal condition as well for each faulty condition, which demands significant effort in data mining. This article proposes a methodology combining principal component analysis (PCA) with the k-means clustering algorithm to automate detection and diagnosis of fault from unlabeled data. The k-means algorithm is used for fault detection and diagnosis by exploiting PCA for data mining. PCA is able to precisely detect and diagnose fault from large set of unlabeled historical data. The proposed method improves the online diagnosis by using clustering algorithm in the monitoring stage. Based on the Euclidean distance between each dataset and cluster centroid of the training data, the k-means clustering algorithm is able to decide if the process is at a normal state or belongs to a particular faulty state. To illustrate the effectiveness of the methodology, the proposed method is applied to two industrial processes: (i) a separator unit from an offshore gas processing platform and (ii) a distillation column of a crude refining unit. The results show that the proposed method is able to avoid the data labeling exercise and is effective in detecting and diagnosing fault in large-scale industrial processes.

关键词： Principal component analysis (PCA) Fault detection and diagnosis (FDD) k-means clustering algorithm

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Multi-objective configuration of an intelligent parking lot and combined hydrogen, heat and power (IPL-CHHP) based microgrid

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SUSTAINABLE CITIES AND SOCIETY 2022年 76卷 103433-103433页

作者： Hosseinnia, Hamed Mohammadi-Ivatloo, Behanm Mohammadpourfard, Mousa Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Aalborg Univ Dept Energy Aalborg Denmark Univ Tabriz Fac Chem & Petr Engn Tabriz Iran

Recently, financial issues have been considered as the main aspects of microgrid (MG) evaluation in the literature. In this study, the optimal configuration of the MG has been calculated by presenting a reliability-constrained optimization model. In this optimization approach, the MG units are considered in full available state and random outage state through the planning horizon. To model a proposed MG in details, its uncertainties are formulated in the main function. A combination of Latin hypercube sampling (LHS) algorithm and k-means clustering algorithm is applied to generate all uncertainties. The proposed model simultaneously optimizes two objectives, namely, economic costs and emission performance. Time of use (TOU) based demand response (DR) program has been employed for optimal management of the demand side. At first, the bi-objective function is converted to a sequence of single-objective constrained problems by employing E-constraints. All Pareto front solutions are obtained by utilizing GAMS for solving the developed mixed-integer linear programming (MILP) model. To make a trade-off among solutions, the max-min fuzzy decision-making method has been used. Due to the positive effect of the DR program on the configuring problem, the total emission and economic costs of MG have been reduced up to 4.01% and 1.72%, respectively.

关键词： Combined hydrogen Heat and power (CHHP) Reliability constraint Latin hypercube sampling (LHS) k-means clustering algorithm Eco-emission function

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Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2020年 131卷 109967-109967页

作者： Yang, Ying Campana, Pietro Elia Yan, Jinyue Malardalen Univ Sch Business Soc & Engn S-72123 Vasteras Sweden KTH Royal Inst Technol Dept Chem Engn S-10044 Stockholm Sweden

This paper analyzes if solar photovoltaic technology is economically feasible enough to compete with coal-fired power in Chinese cities in the subsidy-free context. Considering this, this paper further investigates how profitable investing in solar PV projects is. This paper firstly analyzes to what degree local coal-fired power plants can be replaced by distributed solar power in 344 prefectural-level cities in China. Levelized Cost of Electricity of solar PV power and the local desulfurized coal benchmark price are used for simplified cost crossover math to identify the replacement risk of local coal-fired power plants. Four risk-levels and their corresponding cities are identified, i.e. deemed no cost-risk, potentially at cost-risk, at cost-risk, and substantially at cost-risk. As a whole, 85.17% of current coal-fired power plants from the investigated cities are under cost-risk. Levelized Profit of Electricity, Net Present Value, Internal Rate of Return, and Discounted Payback Period are calculated for each city, and grouped using k-means algorithm. The cities are clustered into four groups, i.e. high return, medium return, moderate return, and low return. The results show that 65.99% of all the cities could achieve a moderate or higher financial return. The cost-risk and investment profit results are mapped for a better understanding of the regional variation in China.

关键词： Distributed solar photovoltaic Zero subsidies Cost-risk Investment-profit Crossover math k-means clustering algorithm

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Surface reinforcement of recycled aggregates (RAs) by geopolymer and quantifying its morphological characteristics by machine learning

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JOURNAL OF BUILDING ENGINEERING 2024年 91卷

作者： Chen, Zhengfa Zhang, Jiahao Cao, Shuang Cindy Song, Yan Chen, Zhaoyan Changzhou Univ Sch Urban Construct Civil Engn Dept Changzhou 213164 Jiangsu Peoples R China Changzhou Univ Sch Mat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Zhaoshe Decorat Co Ltd Wuhan 430070 Hubei Peoples R China

Recycled concrete aggregate (RCA) and recycled brick aggregate (RBA), derived from construction and demolition waste (C&DW), are increasingly used in concrete materials to conserve natural resources. In certain places, RBA, typically derived from C&DW, originally made with minerals that absorb water easily, dating back to the last century, exhibit higher water absorption and poorer mechanical properties compared to RCA. This study investigates the use of fly ashbased geopolymer paste (FGP) as a surface reinforcement coating for pre-treated RCA (PRCA) and pre-treated RBA (PRBA). Image processing and machine learning are employed to quantify the morphological characteristics of RAs. The results demonstrate improved morphology and altered distribution of morphological characteristic indexes for PRCA and PRBA. Physical and mechanical tests reveal the highest improvement in PRCA and PRBA occurs at a SiO2/Na2O molar ratio (Ms) of 1.4. Apparent density increases by 2.3 % and 2.6 %, and crushing value decreases by 21.6 % and 22.2 %, respectively. However, affected by the paste type, coating thickness, and geopolymerization reaction, the water absorption increases by 2.1 % and 5.8 %. XRD, TG-DTG, SEM, and LF-NMR tests indicate the formation of N(C)-A-S-H on RAs' surface by FGP, enhancing their microstructure.

关键词： k-means clustering algorithm C4.5 decision tree algorithm Surface reinforcement Morphological characteristics of RAs LF-NMR

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Fault detection based on RBF neural network in a hydraulic position servo system

Fault detection based on RBF neural network in a hydraulic p...

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6th World Congress on Intelligent Control and Automation

作者： Liu, Hongmei Ouyang, Pingechao Wang, Shaoping Beijing Univ Aeronaut & Astronaut FAc 303 Beijing 100083 Peoples R China

ISBN: (纸本)1424403316

Due to the inherent nonlinearity existed in the hydraulic system, the failure mechanism become complex and the failure characteristics are difficult to extract. Model-based fault diagnosis method depends heavily on the accuracy of mathematical model. An accuracy mathematical model of the process, however, is difficult to avail because of the nonlinearity and ripple coupling in actual hydraulic servo system. Therefore, Robustness of fault diagnosis method based on approximate linear model is worse. A failure observer based on RBF neural network is developed to realize failure detection. The Gaussian function is used for hidden node function, whose centers are adjusted by improved k-means clustering algorithm presented. The weights of output layer are obtained based on improved LS (least square) presented. The trained RBF observer, working concurrently with the,, actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, then rebuilds the system states. The output of the system is accurately estimated. By comparing the estimated output with the actual measurements, residual signal is generated and then analyzed to report the occurrence of faults. The experimental results demonstrate that the failure observer based on RBF neural network is effective in detecting the failure of the hydraulic position servo system.

关键词： status estimate failure detection RBF neural network hydraulic position servo system k-means clustering algorithm

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