Grey Relational Analysis Algorithm Research Articles

Research on anomaly detection in oil chromatography based on convolutional neural network and grey relational analysis algorithm

Abstract The power transformer is a crucial voltage conversion node and a core device in substations. Currently, dissolved gas analysis (DGA) in oil is a practical and validated method for assessing the operational status of transformers. In this study, addressing issues such as insufficient data mining from single dissolved gas measurements, we employed a combination of Convolutional Neural Network and Grey Relational Analysis algorithm to enhance the reliability of identifying abnormal dissolved gas data in transformer oil. This approach improves the sensitivity and accuracy of transformer fault detection. The study includes data processing and result analysis, indicating that the proposed algorithm effectively identifies abnormal dissolved gas data in transformer oil.

Intelligent prediction and evaluation method of optimal frequency based on PSO-BPNN-AdaBoost model

To achieve rapid and accurate determination of the optimal compaction frequency for high-speed railway subgrade materials, a method based on the PSO-BPNN-AdaBoost model for intelligent frequency estimation is proposed. Firstly, the Particle Swarm Optimization (PSO) algorithm is introduced to obtain the optimal hyperparameters of the Backpropagation Neural Network (BPNN), and then the PSO-BPNN-AdaBoost model is established by integrating the AdaBoost ensemble algorithm. Secondly, taking graded gravel fill material as an example, the Grey Relational Analysis algorithm (GRA) is employed to identify the main controlling features affecting f op as input features for the model, and the predictive performance of the model is evaluated. Finally, the model’s reliability is verified through ablation analysis. The results indicate that the PSO-BPNN-AdaBoost model demonstrates higher predictive accuracy. The main controlling features influencing f op are revealed to be the maximum particle size (d max), gradation parameters (b, m), coarse aggregate elongation index (EI), Los Angeles Abrasion (LAA), water absorption rates (W ac, W af).

Research and Application of the Interactive English Online Teaching System Based on the Internet of Things

Nowadays, due to the pandemic and other problems, the establishment of physical classes is a big headache for both students and teachers, due to which the education system all over the world is shifted to the online system from the physical system. Advance technologies such as the Internet of Things (IoT) are playing a significant part in various sectors of life such as health, business, and education. In order to effectively improve the effect of online English teaching, this study designed an interactive online English teaching system based on the IoT technology. This study proposes three topological structures for the establishment of the proposed IoT-based online English teaching system. Based on the analysis of the three topological structures of the IoT, this study chooses to design each submodule of the front and back of the system in the network IoT environment to realize the daily operation and various functions of the system and to realize the interactive design of both of the teacher and student side. Based on this approach, an online English teaching system is designed, and the teaching quality based on this system is evaluated with the help of an algorithm known as grey relational analysis algorithm. The experimental results show that, after the application of this system, students have access to the teaching materials and content in a short period of time; and the English test scores were improved and were significantly higher as compared to the traditional teaching system. In addition, at the same time, the internal consistency reliability of the proposed system is very high which fully demonstrates the effectiveness of the proposed system.

A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

In order to ensure that the large-scale application of photovoltaic power generation does not affect the stability of the grid, accurate photovoltaic (PV) power generation forecast is essential. A short-term PV power generation forecast method using the combination of K-means++, grey relational analysis (GRA) and support vector regression (SVR) based on feature selection (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data were clustered through the multi-index K-means++ algorithm and divided into ideal and non-ideal weather. The GRA algorithm was used to match the similar day and the nearest neighbor similar day of the prediction day. And selected appropriate input features for different weather types to train the SVR model. Under ideal weather, the average values of MAE, RMSE and R2 were 0.8101, 0.9608 kW and 99.66%, respectively. And this method reduced the average training time by 77.27% compared with the standard SVR model. Under non-ideal weather conditions, the average values of MAE, RMSE and R2 were 1.8337, 2.1379 kW and 98.47%, respectively. And this method reduced the average training time of the standard SVR model by 98.07%. The experimental results show that the prediction accuracy of the proposed model is significantly improved compared to the other five models, which verify the effectiveness of the method.

Phase Identification of Low-Voltage Users with Novel Grey Relational Analysis Method

With the development of smart grid, the extensive use of advanced metering infrastructure (AMI) equipment further improves the lean management level of grid, which makes it possible to accurately analyse the power consumption information of low-voltage users. This paper proposes a novel algorithm for accurately identifying the phase of low voltage users. The algorithm is based on grey relational analysis (GRA). Data collected from independent users and buses are used for comparative analysis by proposed algorithm. In this paper, the algorithm and its experimental results in Hebei Province are introduced in detail. The results show encouraging results that the proposed algorithm can accurately identify the low-voltage users’ electricity phase, and the performance of the improved GRA algorithm is better than the traditional GRA. The proposed method also has better effect compared with other methods of phase identification.

Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets

Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.

A Short-Term PV Power Forecasting Method Using a Hybrid Kmeans-GRA-SVR Model under Ideal Weather Condition

With the continuous increase of solar penetration rate, it has brought challenges to the smooth operation of the power grid. Therefore, to make photovoltaic power generation not affect the smooth operation of the grid, accurate photovoltaic power prediction is required. And short-term forecasting is essential for the deployment of daily power generation plans. In this paper, A short-term photovoltaic power generation forecast method based on K-means++, grey relational analysis (GRA) and support vector regression (SVR) (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data was clustered through the multi-index K-means++ algorithm. And the similar days and the nearest neighbor similar day of the prediction day were selected by the GRA algorithm. Then, similar days and nearest neighbor similar days were used to train SVR to obtain an accurate photovoltaic power prediction model. Under ideal weather, the average values of MAE, RMSE, and R2 were 0.8101 kW, 0.9608 kW, and 99.66%, respectively. The average computation time was 1.7487 s, which was significantly better than the SVR model. Thus, the demonstrated numerical results verify the effectiveness of the proposed model for short-term PV power prediction.

Research on Vegetable Pest Warning System Based on Multidimensional Big Data.

Pest early warning technology is part of the prerequisite for the timely and effective control of pest outbreaks. Traditional pest warning system with artificial mathematical statistics, radar, and remote sensing has some deficiency in many aspects, such as higher cost, weakness of accuracy, low efficiency, and so on. In this study, Pest image data was collected and information about four major vegetable pests (Bemisia tabaci (Gennadius), Phyllotreta striolata (Fabricius), Plutella xylostella (Linnaeus), and Frankliniella occidentalis (Pergande) (Thysanoptera, Thripidae)) in southern China was extracted. A multi-sensor network system was constructed to collect small-scale environmental data on vegetable production sites. The key factors affecting the distribution of pests were discovered by multi-dimensional information, such as soil, environment, eco-climate, and meteorology of vegetable fields, and finally, the vegetable pest warning system that is based on multidimensional big data (VPWS-MBD) was implemented. Pest and environmental data from Guangzhou Dongsheng Bio-Park were collected from June 2017 to February 2018. The number of pests is classified as level I (0–56), level II (57–131), level III (132–299), and level IV (above 300) by K-Means algorithm. The Pearson correlation coefficient and the grey relational analysis algorithm were used to calculate the five key influence factors of rainfall, soil temperature, air temperature, leaf surface humidity, and soil moisture. Finally, Back Propagation (BP) Neural Network was used for classification prediction. The result shows: I-level warning accuracy was 96.14%, recall rate was 97.56%; II-level pest warning accuracy was 95.34%, the recall rate was 96.45%; III-level pest warning accuracy of 100%, the recall rate was 96.28%; IV-level pest warning accuracy of 100%, recall rate was 100%. It proves that the early warning system can effectively predict vegetable pests and achieve the early warning of vegetable pest’s requirements, with high availability.

An evaluation of EU member states according to human development and global competitiveness dimensions using the multi-period grey relational analysis (MP-GRA) technique

PurposeThe purpose of this paper is to investigate the multi-period, multi-attribute decision-making problems that arise when the information required to make decisions is provided at different periods.Design/methodology/approachFor the multi-period grey relational analysis (MP-GRA) procedure, the time dimension is added to the grey relational analysis algorithm, which is a multi-attribute decision analysis that has been developed. As a case study to test the functionality and applicability of the model, 28 European Union member states were ranked by the MP-GRA method developed according to their human development and global competitiveness variables for the years 2006-2015.FindingsThe general ranking of EU member states has been provided in intervals of certain time periods called decision units.Originality/valueAnother dimension based on time periods has been added to a ranking technique. This case also shows that the opinions of decision-makers may be added separately to enhance evaluations.

Application Research on Quantitative Prediction of Mineral Resources Based on the Grey Relational Analysis Algorithm with Dual Hesitant Fuzzy Information

With the rapid economic development, China is facing a comparatively short supply of mineral resources after the relatively long-term developmental stage that is based on the consumption of mineral resources. Contradictions among the population, resources, the environment and developments have arisen. Therefore, under the constraint conditions of limited and non-renewable mineral resources, the mineral resources industry in China must adhere to sustainable development. The aim of this paper is to investigate the multiple attribute decision making problems for quantitative prediction of mineral resources with dual hesitant fuzzy information and incomplete weight information. Then, based on the traditional GRA method, calculation steps for solving dual hesitant fuzzy multiple attribute decision-making problems with incomplete weight information are proposed. Finally, an algorithm of quantitative prediction of mineral resources based on the grey relational analysis with dual hesitant fuzzy information is proposed.

Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade

A novel thruster fault identification method for autonomous underwater vehicle is presented in this article. It uses the proposed peak region energy method to extract fault feature and uses the proposed least square grey relational grade method to estimate fault degree. The peak region energy method is developed from fusion feature modulus maximum method. It applies the fusion feature modulus maximum method to get fusion feature and then regards the maximum of peak region energy in the convolution operation results of fusion feature as fault feature. The least square grey relational grade method is developed from grey relational analysis algorithm. It determines the fault degree interval by the grey relational analysis algorithm and then estimates fault degree in the interval by least square algorithm. Pool experiments of the experimental prototype are conducted to verify the effectiveness of the proposed methods. The experimental results show that the fault feature extracted by the peak region energy method is monotonic to fault degree while the one extracted by the fusion feature modulus maximum method is not. The least square grey relational grade method can further get an estimation result between adjacent standard fault degrees while the estimation result of the grey relational analysis algorithm is just one of the standard fault degrees.

An Improved Optimal Measurement Data Correlation Algorithm Based on Multi-Source Information

An improved optimal 3-Dimensional (3-D) assignment algorithm based on Multi-source Information (IMFOA) is proposed. The algorithm firstly gets the correlation degree between multi-source measurements of single-sensor and target track through fusing multi-source information by using grey relational analysis algorithm and then a 3-D assignment model based on multi-source information fusion can be got. Further, an improved point- track optimal assignment algorithm is used to complete point-track correlations. In comparison with the Optimal 3- D Assignment (OA) algorithm based on dynamic information and the Improved Optimal Assignment (IOA) algorithm which implements point-track correlation through using marginal correlation probability on the basis of OA algorithm, IMFOA algorithm improves the tracking accuracy of multi-target in varying degrees under different detection scenarios and is an optimal measurement data correlation algorithm with stronger anti-interference.

Innovative parametric design for environmentally conscious adhesive dispensing process

Due to increasing environmental consciousness, the European Union has prohibited the use of lead substances in electronics soldering material. 58Bi/42Sn solder with a melting temperature of only $$138^{\,\circ }$$ C helps achieve a lower process temperature to resolve the board warpage issue. Curing the adhesive simultaneously with solder reflow helps simplify the assembly process and reduces the manufacturing cost. When a low soldering temperature profile is used, the impact on the adhesion performance of the cured adhesive becomes a major concern. This research investigates the influence of adhesive dispensing process parameters on the shear strength of 0805 chip capacitors. Experimental data is analyzed using the principal component analysis (PCA) technique and PCA integrated with grey relational analysis algorithm. This study also proposes an innovative parametric design for artificial neural network (ANN) modeling for the multi-quality function problem to determine the optimal process scenarios. Results of the confirmation test indicate that the samples prepared using the process parameters identified by ANN are superior to the others. Thus, the optimal process parameters are adhesive dispense location beneath the component body, placement time of 0 s, a curing temperature of $$160^{\,\circ }$$ C and a conveyor speed of 1 m/min. The implementation of the optimal process has improved chip capacitor fall-off from 2.5 to 0.88 %.

Dynamic analysis for covert channel based on grey relational analysis algorithm

Search and recognition is the most important part of the covert channel analysis. At present, most of the covert channel search methods are static analysis on system. It has many shortcomings, such as heavy workload, omission, inaccurate, and so on. It is necessary to find the dynamic search method for covert channel during a system is running. This paper presented a covert channel dynamic search method based on grey relational analysis algorithm (GRAA) in gray theory. The method is efficient and can be used in conjunction with existing covert channel search methods. Finally, a closed-loop feedback will be formed so that the system's safety mechanism can be self-improvement gradually.