Genetic Algorithm Extreme Learning Machine Research Articles

Analysis of influencing factors and prediction of carbon emissions of typical urban agglomerations in China: a case study of Beijing-Tianjin-Hebei region.

Achieving the "double carbon" goal is a major task and challenge facing China. The emission reduction actions in typical urban agglomerations are of great significance. Based on the consideration of the impact of regional coordinated development, this study analyzed influencing factors and conducted prediction of carbon emissions from terminal energy consumption in the Beijing-Tianjin-Hebei (BTH) region. Firstly, the factors affecting carbon emissions were screened through the STIRPAT model. Then, the paper designs different scenarios and finally uses the genetic algorithm extreme learning machine (GA-ELM) algorithm to predict the carbon emissions of the BTH region, with and without considering the impact of the coordinated development strategy. The research shows that the increase in energy intensity and the improvement of energy consumption structure have the largest promotion effect on carbon emission reduction. At the same time, the significant role of the coordinated development strategy in promoting regional carbon emission reduction was verified. Therefore, the BTH region should adhere to the path of coordinated development, innovate low-carbon technology, and deepen the concept of green consumption to promote the realization of regional carbon emission reduction goals.

Dimensional Error Prediction of Grinding Process Based on Bagging–GA–ELM with Robust Analysis

Grinding, which determines the final dimension of parts, is an important process in manufacturing companies. In praxis, in order to avoid quality problems on the customer’s side, an online dimension check is normally used after the grinding process to ensure the product dimensions; however, it is always hysteretic and needs extra space and machine investment. To deal with the issue, dimensional error prediction of the grinding process is highly needed, and does not require extra space or machinery, as well as having better real-time performance. In this paper, a dimensional error prediction algorithm using principal component analysis (PCA), extreme learning machine (ELM), genetic algorithm (GA), and ensemble strategy (bagging algorithm) is designed. Specifically, PCA is used as a pre-treatment method to extract the main relevant components, then a bagging–GA–ELM model is constructed to predict the final product dimensional error after the grinding process, in which extreme learning machine (ELM) is utilized as a basic framework because of its fast calculation speed. GA, with its excellent global optimization capability, is implemented to search optimal input weights and thresholds of ELM, enabling ELM to obtain a better prediction performance. In addition, considering the complex environment of the industrial field, the bagging algorithm is employed to enhance the anti-noise ability of the proposed algorithm. Finally, the proposed algorithm is verified by a case from a bearing company.

Numerical analysis onto thermal balance behaviors of motorized spindle unit

Generally, the accuracy stability of a precision motorized spindle unit is influenced by its structural thermal balance behaviors (power matching degree of spindle structural heat generation–dissipations). For the accurate analysis of this spindle heat transfer behaviors based on temperature detections, this paper describes a method constructed with the numerical simulation technology and a proposed genetic algorithm-extreme learning machine (GA-ELM) algorithm. Firstly, the heat–fluid–solid coupling FE transient model is established to simulate spindle thermal behaviors, determinations of its thermal loads/boundary conditions are initially based on empirical calculations. Secondly, according to spindle temperature detections, these initial values are applied to thermal simulations and corrected by GA to make simulated spindle temperatures gradually approach detections. Specially, ELM is adopted to estimate the functional relationships from the parent population to the child population generated by genetic operators with increasing fitness values, and the trained ELM model is utilized to ensure the GA faster convergence. Eventually, based on the corrected thermal load/boundary condition values and simulation results, the time-varying power matching conditions of spindle structural heat generation–dissipation are analyzed. This study provides a theoretical basis for the optimization and promotion of spindle structural design and coolant control strategy.

A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5°C temperature control target.

The clean and low-carbon transition of China's power sector is of great importance to the achievement of dual carbon targets and the control of global warming. This paper first estimates the remaining carbon budget of the power sector under a 1.5°C temperature control target and on this basis constructs 1.5°C and 2°C power transition scenarios, examining key boundary conditions such as economic development and changes in the cost of power generation technologies. Second, the Genetic Algorithm-Extreme Learning Machine (GA-ELM) model is used to forecast the electricity demand for the next forty years. Finally, with the objective of minimising the total planning cost, a pathway optimisation model of the power system is constructed to explore the optimal transition path for the power system using the dual carbon target, carbon budget and electricity demand as the main constraints. The results of the study show that the carbon budget of the Chinese power sector is approximately 7.1 × 1010 t CO2 for a 1.5°C temperature control target. The electricity demand tends to saturate after 2050 and reaches 1.58 × 1013 kWh in 2060. The time of the carbon peak and carbon neutralisation in the power sector is 5years ahead of the double carbon target. By 2060, the power system will be dominated by new energy sources, with the proportion of installed non-fossil energy capacity at over 90% and the proportion of non-fossil energy generation at over 85%. Compared to that under the 2°C temperature control target, the power sector under the 1.5°C temperature control target needs to accelerate the pace of the low-carbon transition of electricity and deal with key issues such as the orderly withdrawal of coal power, the construction of a diversified clean energy system and the application of carbon capture devices. This study recommends that the process of building a zero-emissions power sector requires a good pace of the construction of new power systems at a suitable pace, increased efforts to tackle key technologies and improved relevant market mechanisms. China's carbon-neutral pathway in the power sector also has implications for other countries' clean, low-carbon transitions of their power systems.

A mid-infrared exhaled carbon dioxide isotope detection system based on 4.35 μm quantum cascade laser

The carbon dioxide isotope in human exhaled gas can be used as an indicator to determine Helicobacter pylori (HP), the detection of carbon dioxide isotope in human exhaled gas is of great significance for the treatment of human diseases. Based on Tunable Diode Laser Absorption Spectroscopy (TDLAS), a carbon dioxide isotope detection system for exhaled gas was developed to detect the carbon dioxide isotope in human exhaled gas, a 4.35 μm quantum cascade laser (QCL) and a 3 m optical path multi-pass gas cell (MPGC) were adopted in the detection system. The exhaled gas pretreatment system was designed to pass through human exhaled gas into the MPGC. The performance of the system was researched using a gas distribution station fitted with standard 12CO2 and 13CO2 concentrations. The results show that the limits of detection（LOD）of 12CO2 and 13CO2 reach up ∼ 0.0134 ppm and ∼ 0.0149 ppm, respectively. The detection precision of the system is ∼ 0.00214 ‰ with a 48 s averaging time. QCL-based mid-infrared detection system has higher sensitivity and lower detection limit compared with near-infrared detection system. The volume of MPGC is 250 ml, which is much less than the volume of normal human exhaled gas, therefore the majority of human exhaled gas can be filled with MPGC. Two-stage proportional-integral-derivative (PID) temperature control is adopted to eliminate the effect of drift of the spectrum due to the change of temperature, so that the stability of the system is improved. A high precision and high-speed Genetic Algorithm Extreme Learning Machine (GA-ELM) algorithm is applied for improving the concentration inversion precision of the system. Finally, the performance of detection system was validated at Sichuan Cancer Hospital for detecting carbon dioxide isotope in exhaled gas, and the verification results illustrate that the detection system has a very important application prospect in the field of exhaled gas detection.

Selection of optimal models for predicting growth stress in Artemisia desertorum by comparison of linear regression and multiple neural networks: Take the construction of a green mine in the Bayan Obo mine as an example

In recent years, more and more countries are focusing on the control of mining sites and the surrounding ecological environment, and the new environmental concept of green mines has been proposed. By investigating the ecological background of a mine site, pollution and ecological imbalances in the mine can be predicted, managed or transformed. This study investigated the effects of rare earth elements on plant growth in the Baotou Bayan Obo Rare Earth Mine and evaluated soil contamination and subsequent remediation through the measured plant height. Using linear regression, BP(Back Propagation) neural networks, GA-BP(Genetic Algorithm- Back Propagation) neural networks, ELM(Extreme Learning Machine) and GA-ELM(Genetic Algorithm- Extreme Learning Machine) model prediction instruments, the different rare earth solution concentrations were set as input values and the heights of Artemisia desertorum, which as the model plant, were set as output values in the prediction. The results showed that the linear regression predicted the standard error of single La(III), Ce(III) solution and compound La(III) + Ce(III) solution for Artemisia desertorum growth stress was on the high side, 7.02%− 8.92%; the efficiency range of each group of models under BP neural network, GA-BP neural network and ELM neural network were 1.15%− 2.53%, 0.85%− 1.28%, 1.76%− 3.53%; while the efficiency range under GA-ELM neural network was 0.59%− 0.68%, with average error values and predicted values close to the true values. Among them, the MAPE of GA-ELM neural network are significantly lower than other models, and the error decreases with increasing concentration of the compound solution. So GA-ELM neural network can be used as an efficient, fast and reasonable optimal model for predicting the growth stress of Artemisia desertorum in Bayan Obo mining area. The experimental results can provide a theoretical basis for assessing the risk of soil rare earth contamination in the area, evaluating the expectation of later remediation, and provide a degree of new ideas for the construction of green mines.

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Technique Combined with PCA–GA–ELM Algorithm

Thermal barrier coatings (TBCs) are usually used in high temperature and harsh environment, resulting in thinning or even spalling off. Hence, it is vital to detect the thickness of the TBCs. In this study, a hybrid machine learning model combined with terahertz time-domain spectroscopy technology was designed to predict the thickness of TBCs. The terahertz signals were obtained from the samples prepared in laboratory and actual turbine blade. The principal component analysis (PCA) method was used to decrease the data dimensions. Finally, an extreme learning machine (ELM) was proposed to establish the thickness of TBCs prediction model. Genetic algorithm (GA) was selected to optimize the model to make it more accurate. The results showed that the root correlation coefficient (R2) exceeded 0.97 and the errors (root mean square error and mean absolute percentage error) were less than 2.57. This study proposes that terahertz time-domain technology combined with PCA–GA–ELM model is accurate and feasible for evaluating the thickness of the TBCs.

Moisture content assessment of dried Hami jujube using image colour analysis

To investigate the feasibility of image colour information in predicting the moisture content of dried Hami jujube, the images were obtained under different colour space models, and the colour model component mean and chromaticity frequency sequences of R, G, B, H, S, V, L*, a* and b* were extracted through image analysis. After optimising the colour model component mean and chromaticity frequency sequence, the model was established and compared. The results showed that the GA-ELM (genetic algorithm - extreme learning machine) model established by CARS (competitive adaptive reweighted sampling) method to optimise 12 chromaticity features of S chromaticity frequency sequence had the best prediction effect, with R<sub>c</sub> of 0.917, R<sub>p</sub> of 0.934 and residual predictive deviation (RPD) of 2.507. Therefore, the colour image information can accurately predict the moisture content of dried Hami jujube.

Peer-to-peer multi-energy sharing for home microgrids: An integration of data-driven and model-driven approaches

To form the economic and sustainable communities, peer-to-peer energy sharing is becoming one of the most promising ways for coordinating home microgrids. This paper presents a double auction-based peer-to-peer multi-energy sharing mechanism, to achieve the coordination of the intelligent, self-interested, and privacy-conscious home microgrids, and conduct the electricity sharing and heat sharing simultaneously. Based on a combination of model-driven optimization and data-driven prediction, an integrated model is developed, which can analyze historical transaction data, excavate the hybrid market trading rules, accurately and stably predict energy sharing strategies, and efficiently optimize the operation strategies of uncontrollable and controllable equipment. A hybrid genetic algorithm–extreme learning machine approach is proposed to realize data-driven prediction. According to the cost minimization objective, a joint optimization of the home microgrid electricity and heat energy sharing strategies and distributed energy operation strategy is conducted, to facilitate the energy interaction among microgrids and enhance the coordination between electricity and heat systems. In addition, we have evaluated the accuracy and stability of the genetic algorithm–extreme learning machine model and proved the validity of the model. Numerical results demonstrate that the proposed model can efficiently solve the multi-energy sharing problem, obtain high cost savings, improve the independence of microgrids, realize complementary advantages of multi-energy, and address the complicated relationships among the peers, which are regarded as stakeholders with separate privacy and interests.

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection.

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential.

To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models.

Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

Spoken language identification based on optimised genetic algorithm–extreme learning machine approach

The determination and classification of a recognized spoken language based on certain contents and datasets is known as the process of language identification (LID). The common process in carrying out LID entails the mandatory processing of data which enables the extraction of the necessary features for the process. The extraction involves a mature process whereby the development of the standard LID features have been conducted much earlier by means of a mel-frequency cepstral coefficients, shifted delta cepstral, Gaussian mixture model and i-vector-based framework. Despite that, improvement or rather optimisation still needs to be done on the learning process based on the extracted features so as to obtain all the knowledge embedded within them. The classification and regression analysis can benefit tremendously from the use of the extreme learning machine (ELM) which is a particularly effective and useful learning model for training a single-hidden layer neural network. However, owing to the randomly selected weights embedded in the input’s hidden layers, the model’s learning process is rendered to be ineffective or not optimised in its entirety. In this study, the ELM is employed as the learning model for LID due to the standard feature extraction. In addition, this study proposes a new optimised genetic algorithm (OGA) with three different selection criteria (i.e., roulette wheel, K-tournament and random) to select the appropriate initial weights and biases of the input hidden layer of the ELM, thereby minimising the classification error and improving the general performance of the ELM for LID. Results show the excellent performance of the proposed OGA–ELM with three different selection criteria, namely, roulette wheel, K-tournament and random, with the highest accuracies of 99.50%, 100% and 99.38%, respectively.

A novel Grouping Genetic Algorithm–Extreme Learning Machine approach for global solar radiation prediction from numerical weather models inputs

This paper presents a novel scheme for global solar radiation prediction, based on a hybrid neural-genetic algorithm. Specifically a grouping genetic algorithm (GGA) and an Extreme Learning Machine algorithm (ELM) have been merged in a single algorithm, in such a way that the GGA solves the optimal selection of features, and the ELM carries out the prediction. The proposed scheme is also novel because it uses as input of the system the output of a numerical weather meso-scale model (WRF), i.e., atmospherical variables predicted by the WRF at different nodes. We consider then different problems associated with this general algorithmic framework: first, we evaluate the capacity of the GGA–ELM for carrying out a statistical downscaling of the WRF to a given point of interest (where a measure of solar radiation is available), i.e., we only take into account predictive variables from the WRF and the objective variable at the same time tag. In a second evaluation approach, we try to predict the solar radiation at the point of interest at different time tags t+x, using predictive variables from the WRF. Finally, we tackle the complete prediction problem by including previous values of measured solar radiation in the prediction. The proposed algorithm and its efficiency for selecting the best set of features from the WRF are analyzed in this paper, and we also describe different operators and dynamics for the GGA. Finally, we evaluate the performance of the system with these different characteristics in a real problem of solar radiation prediction at Toledo’s radiometric observatory (Spain), where the proposed system has shown an excellent performance in all the subproblems considered, in terms of different error metrics.

A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction

Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

Real-Time Transient Stability Assessment Based on Genetic Algorithm-Extreme Learning Machine

In this paper an genetic algorithm-extreme learning machine (ELM) based real-time transient stability assessment method is proposed. This method uses genetic algorithm (GA) to search optimal input weights and hidden biases in the principle of cross validation to establish GA-ELM classifier. In order to do real-time transient stability assessment, generator trajectories of rotor angle, rotor speed, voltage magnitude, electromagnetic power and imbalance power in-and post-disturbance are chosen as original features for the quick access based synchronously sampled values. Simulation results of New-England 39-bus system show that this method has good performance in power system transient stability assessment.