ELM Algorithm Research Articles

Construction and Monitoring of a Quantitative Inversion Model for Conductivity Using Unmanned Aerial Vehicle Remote Sensing Based on ELM Algorithm

Construction and Monitoring of a Quantitative Inversion Model for Conductivity Using Unmanned Aerial Vehicle Remote Sensing Based on ELM Algorithm

Shrinkage and Porosity of Cotton and Hemp Fabrics Based on ELM Algorithm

Shrinkage and Porosity of Cotton and Hemp Fabrics Based on ELM Algorithm

MACHINE LEARNING ALGORITHM FOR RAPID OBJECT DETECTION BASED ON COLOR FEATURES

The identification of objects is of utmost importance in a wide range of computer vision applications, such as surveillance systems, autonomous cars, and environmental monitoring. Accurate and efficient object recognition methods are crucial in pastoral environments, characterized by the prominent presence of cattle and other objects, to provide effective analysis and decision-making processes. The present study paper introduces an innovative methodology for efficient identification of objects in pastoral landscapes through the utilization of a Colour Feature Extreme Learning Machine (CF-ELM). The CF-ELM method integrates color characteristics with the ELM algorithm to attain enhanced object detection accuracy while preserving computational economy. The experimental findings provide empirical evidence supporting the efficacy and efficiency of the suggested approach in the detection of items within pastoral landscapes. In addition to the CF-ELM, an algorithm for desktop-based categorization of items within a pastoral environment is provided, with individual speeds ranging from 0.05 s to 0.17 s for a single image, evaluated in each color space. The algorithm is intended for usage in scenarios with challenging and variable terrain, making it appropriate for application in agricultural or pastoral settings.

Air pollutant concentration and population distribution economic planning simulation based on target tracking

China has emerged as one of the nations with the worst air pollution in recent years. The severe air pollution has caused a large number of population migration and also caused serious economic problems. Since the concentration of air pollutants can change quickly in a short amount of time, the study first tracked PM2.5, PM10, NO2, CO, SO2, and O3 as targets before using the particle swarm optimization algorithm to improve the PIO algorithm, which is based on the traditional pigeon swarm algorithm. To estimate the concentration of air pollutants, combine the wavelet packet decomposition technique, MDS visualization method, and k-means algorithm. Then, apply the enhanced PIO algorithm to optimize the ELM algorithm. Finally, a new type of decomposition-optimization-clustering-integration hybrid learning model, namely DOCIAPC model, is constructed. The experimental findings indicate that, when predicting the concentration of various air pollutants, the DOCIAPC model’s average direction prediction accuracy is 90.37% . In conclusion, the model suggested in the study has excellent performance and applicability, and it can accurately predict the concentration of air pollutants, help the government take action to reduce air pollution, balance the environment and economy, as well as the allocation of labor and its resources in the city.

Betel Leaf Diseases Classification using Machine Learning Algorithm: A Feasible Approach

Even though the development in science and technology in various domains, agriculture is essential for humans to survive. It is mandatory to implement the development of science in the agriculture sector. There is a huge demand for agricultural workers because of lots of risk in production behind productivity. One of the major risks is diseases caused in plants. Our approach uses region of interest algorithms for image segmentation and feature extractions are done by GLCM and finally the diseases are classified with the help of ELM algorithm. These improvements in the classification of plant leaf diseases not only increase the precision and effectiveness of disease diagnosis, but also make it easier to accurately monitor vast fields of crops quickly. Due to its ability to precisely diagnose and categorize plant leaf diseases, the extreme learning machine technique is a novel strategy that has attracted a lot of attention. Additionally, this method has benefits over conventional image processing methods, which could find it difficult to categorize various plant leaf diseases. Research on betel leaf has received a lot of attention because of its possible medical benefits. Studies on its impact on dental health have been done because betel leaf extracts have antibacterial and anti-inflammatory properties. Approximately 1047 betel leaf images are captured to test our approach by taking five classes of diseases like Anthracnose, Phytophthora Foot Rot, Fusarium wilt, Bacterial Leaf Rot, Powdery Mildew. The model reports accuracy of 97%.

An ELM Algorithm Study of Chinese Wushu Spreading in International Cultural Exchange

Abstract Strong visual impact and easy-to-understand communication methods have made the YouTube video platform the forefront of international cultural exchange and dissemination of Chinese martial arts. In this paper, we first design a principal component-ridge regression algorithm to correct the multicollinearity problem and eliminate its negative impact on regression analysis. Then, based on the “center path-edge path” perspective in ELM theory, we assumed that the video theme, duration, title, and other factors would affect the dissemination effect of Chinese wushu in international cultural exchanges and constructed an ELM model for the international dissemination of Chinese wushu. Then we designed a questionnaire with international students in China as the research object, conducted sample reliability and validity tests, and combined the principal component-ridge regression algorithm to explore the influencing factors of Chinese Wushu in international cultural exchange communication. The results are presented as follows: except for the p-value of 0.021 and 0.004 for video scientificity and the presence or absence of characters on the video cover, the p-value of all the other items is less than 0.001. The video theme, video scientificity, video duration, video title, cover characters, cover text, and animated video all have a significant positive impact on the effect of dissemination of Chinese Wushu in international cultural exchanges. The theoretical basis of this paper is used to accelerate the construction of Chinese Wushu cultural influence and optimize the communication strategy of Chinese Wushu international cultural exchange.

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.

Fault Diagnosis Method for High Voltage Power Metering System Based on the PCA-ELM Algorithm

The current fault diagnosis of high-voltage power metering systems lacks the processing of information dimensions, which leads to the low correctness of fault diagnosis results. To this end, a fault diagnosis method based on the PCA-ELM algorithm for high-voltage power metering systems is proposed. Eliminate data information noise. Extract fault information features. Based on PCA to reduce the information dimension, the ELM algorithm is used to analyze the fault information characteristics. The fault information characteristics correspond to the fault types to complete the fault diagnosis of the high-voltage power metering system. Comparative experiments are designed for verification. The measurement results show that the accuracy rate of fault diagnosis for this method is 99.89%, and the average accuracy rate of fault type diagnosis is 98.87%, which substantially improves the correct rate of diagnosis results.

Application of ELM algorithm-based generative adversarial network model in network security

Abstract To improve the efficiency and accuracy of network intrusion discrimination, this paper introduces intrusion detection techniques in a generative adversarial network model. Firstly, a basic framework of a generative adversarial network is constructed. Secondly, the generative adversarial network is trained, and the training process is analyzed to find the data discrimination point in the network. Finally, ELM (Extreme Learning) algorithm is introduced at this discriminating point. The output weight matrix is derived using the minimization square loss function and least squares regression to improve the intrusion discrimination accuracy and intrusion cracking rate in the generative adversarial network, improving network security. To verify the security of the ELM algorithm, this paper simulates the intrusion of the constructed network model, and the results show that the intrusion detection accuracy of the generative adversarial network model based on the ELM algorithm can reach 100%, which is higher than that of DCGAN network 19% and LSGAN network 23%, respectively. The intrusion cracking rate of its layer 5 neural network can reach 92% at the second 2.5 seconds of the simulated intrusion. From the above results, it is clear that the generative adversarial network model based on the ELM algorithm can accurately detect and efficiently crack the intrusion to improve the network security performance.

Large-signal behavior modeling of GaN HEMTs using SSA augmented ELM algorithm

Large-signal behavior modeling of GaN HEMTs using SSA augmented ELM algorithm

Analysis of distribution network planning with distributed power supply based on ELM algorithm

Abstract With the development of the social economy, the importance of energy and environmental issues is becoming more and more prominent. Distributed power sources (DG) have the advantages of low carbon and environmental protection, flexible control, and low investment cost, but their nodes in the grid are random, which will increase the complexity and uncertainty of grid operation after grid connection. In this paper, we study the planning path of DGs in distribution networks and the protection technologies involved. Firstly, we focus on the impact of DG access on the protection of distribution networks and the impact of different access capacities and access locations of DG on the protection of distribution networks. Second, after studying the current status of the application of traditional genetic algorithms in distribution network fault location, a two-layer planning model based on the modified extreme learning machine algorithm (MPSO-ELM) is proposed, with the load demand guaranteed by the first layer of power allocation and the fault location incorporated into the distribution network protection scheme in the second layer. Finally, simulations are performed to compare and demonstrate the advantages of this MPSO-ELM algorithm. The simulation results show that the MPSOELM algorithm converges faster than the traditional genetic algorithm by 7s, the average operating speed of power nodes is 3.61ms faster than the traditional genetic algorithm by 5.59ms, and the accuracy in fault location is improved by 23.14%. The simulation results verify the comprehensiveness, accuracy, and high efficiency of the ELM algorithm for double-layer planning of distribution networks containing distributed power supplies, which is important for improving the level of power grid construction and avoiding the risk of power grid construction.

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform.

MEMS gyroscopes are one of the core components of inertial navigation systems. The maintenance of high reliability is critical for ensuring the stable operation of the gyroscope. Considering the production cost of gyroscopes and the inconvenience of obtaining a fault dataset, in this study, a self-feedback development framework is proposed, in which a dualmass MEMS gyroscope fault diagnosis platform is designed based on MATLAB/Simulink simulation, data feature extraction, and classification prediction algorithm and real data feedback verification. The platform integrates the dualmass MEMS gyroscope Simulink structure model and the measurement and control system, and reserves various algorithm interfaces for users to independently program, which can effectively identify and classify seven kinds of signals of the gyroscope: normal, bias, blocking, drift, multiplicity, cycle and internal fault. After feature extraction, six algorithms, ELM, SVM, KNN, NB, NN, and DTA, were respectively used for classification prediction. The ELM and SVM algorithms had the best effect, and the accuracy of the test set was up to 92.86%. Finally, the ELM algorithm is used to verify the actual drift fault dataset, and all of them are successfully identified.

A hybrid-extreme learning machine based ensemble method for online dynamic security assessment of power systems

• A hybrid model is developed, blending ELM and Levenberg-Marquardt. • Particle swarm optimization-based weighted averaging ensemble is developed. • Energy margin and time margin are calculated for better application of techniques. • Hybrid ELM is found most suited technique for online DSA application. This manuscript develops a new hybrid-extreme learning machine (ELM) based ensemble model for real-time dynamic security assessment (DSA) of power systems. In order to boost the forecasting accuracy of ELM algorithm, a Levenberg-Marquardt (LM) backpropagation algorithm is used. The Ensemble strategy takes advantage of complementary information to improve the generalization capability of model. To realize an accurate ensemble model, particle swarm optimization (PSO) based weighted averaging is used to combine the individual forecasts. Transient energy function (TEF) terms, generator real and reactive power outputs and fault location are taken as input features for classification as well as prediction of transient energy margin (TEM) and time margin (TM) for a given operating condition and fault location. The potential of the proposed technique is validated on New England 39-bus and 68-bus test power systems. The results demonstrate that the developed model outperforms other state-of-the-art methods.

Application of ELM Algorithm Incorporating AE Principles in Wireless Communication Signal Detection

Application of ELM Algorithm Incorporating AE Principles in Wireless Communication Signal Detection

Application of convolutional neural network in fusion and classification of multi-source remote sensing data.

Through remote sensing images, we can understand and observe the terrain, and its application scope is relatively large, such as agriculture, military, etc. In order to achieve more accurate and efficient multi-source remote sensing data fusion and classification, this study proposes DB-CNN algorithm, introduces SVM algorithm and ELM algorithm, and compares and verifies their performance through relevant experiments. From the results, we can find that for the dual branch CNN network structure, hyperspectral data and laser mines joint classification of data can achieve higher classification accuracy. On different data sets, the global classification accuracy of the joint classification method is 98.46%. DB-CNN model has the highest training accuracy and fastest speed in training and testing. In addition, the DB-CNN model has the lowest test error, about 0.026, 0.037 lower than the ELM model and 0.056 lower than the SVM model. The AUC value corresponding to the ROC curve of its model is about 0.922, higher than that of the other two models. It can be seen that the method used in this paper can significantly improve the effect of multi-source remote sensing data fusion and classification, and has certain practical value.

Solving partial differential equation based on extreme learning machine

In this paper, we present a novel learning method based on extreme learning machine algorithm called ELMNET for solving partial differential equations (PDEs). A loss function that relies on partial differential equation (PDE), initial and boundary condition (I/BC) residual was proposed. The proposed loss function is discretization-free and highly parallelizable. The network parameters are determined by solving a system of linear equations using the ELM algorithm. We demonstrated the performance of ELMNET in solving the advection–diffusion PDE (AD-PDE) as case-studies. The experimental results from the proposed method were compared to the efficient deep neural network and they showed that the ELMNET attains significant improvements in term of both accuracy and training time.

Optimization of High-speed Dry Milling Process Parameters Based on Improved ELM and Genetic Algorithm

High-speed dry grinding has the characteristics of high processing efficiency and clean environment. The high-speed dry grinding method meets the requirements of the green and efficient development of the national manufacturing industry. However, inappropriate cutting parameters seriously affect the surface quality of the workpiece and cause the workpiece to be scrapped. Therefore, this paper proposed an optimization method based on the combination of the improved extreme learning machine neural network (ELM) for high-speed dry milling surface roughness prediction model and genetic algorithm (GA). The Taguchi orthogonal experiment results show that the surface roughness of high-speed dry milling can be accurately predicted by the improved ELM and thereafter the optimal cutting parameter combination can be determined by GA.

A digital twin dosing system for iron reverse flotation

Flotation is an essential process in beneficiation production. The amount of flotation reagent has a significant influence on the quality of the product. An inappropriate dosing system will lead to metal loss and reagent waste, especially when the nature of the raw ore changes frequently. If the dosing system is not updated in time, it will cause economic losses. Based on digital twin technology and machine learning algorithms, this research designed a digital twin system for iron reverse flotation reagents. Based on the flotation froth image and transformer algorithm, a soft sensor model of tailings grade is established to monitor the product quality in real-time. The flotation dosing model established based on the ELM algorithm automatically updates the reagent system and intelligently assigns the controller. On the basis of stabilizing product quality, this research avoids the waste of reagents and improves the economic benefits of production efficiency. The system was applied in an iron flotation plant, and industrial operation effect verified the method.

A Novel Method for Recognizing Traffic Signs using Color and Texture Properties using the ELM Algorithm

Road accidents cause a lot of financial and human losses every year. One of the causes of these accidents is human error, and the driver ignores traffic signs. Therefore, accurate detection of these signs will help to increase the safety of drivers and pedestrians and reduce accidents. In recent years, much research has been done to increase the accuracy of panel recognition, most of which are problems that affect the diagnosis, such as adverse weather conditions, light reflection, and complex backgrounds. In the present study, considering the diversity of traffic signs' geometric shapes, the sign detection part has been done using a torsional neural network. Then, in the feature extraction section, we used LBP and HOG techniques, and at the end, the section was identified and classified using the ELM algorithm. The results obtained on 12569 images, 75% of which were used for training and 25% for experimentation, show that the accuracy of this research has improved by 95% compared to the essential work by 93%.

An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms

Purification of polluted water and return back to the agriculture field is the wastewater treatment for plants. Contaminated water causes illness and health emergencies of public. Also, health risk due release of toxic contaminants brings problem to all living beings. At present, sensors are used in waste water treatment and transfer data via internet of things (IoT). Prediction of wastewater quality content which is presence of total nitrogen (T-N) and total phosphorous (T-P) elements, chemical oxygen demand (COD), biochemical demand (BOD), and total suspended solids (TSS) is associated with eutrophication that should be prevented. This may leads to algal bloom and spoils aquatic life which is consumed by human. The presence of nitrogen and phosphorous elements is in the content of wastewater, and these elements are associated with eutrophication which should be prevented. Adsorption of T-N and T-P activated carbon was predictable as one of the most promising methods for wastewater treatment. Many research works have been done. The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. The accuracy of the BKNN-ELM algorithm in classification of water quality status produced the highest accuracy of the highest accuracy which is [Formula: see text] and [Formula: see text] with rate of accuracy which is 93.56%, and the lowest accuracy is [Formula: see text]. Experiment evaluation shows that a total suspended solid predicted by proposed model is 91 with accuracy of 93%. The relative error rate of prediction is 12.03 which is lesser than existing models.